Implementation of Lean Manufacturing in a
Low-Volume Production Environment
By
Garret J. Caterino
B.S. Mechanical Engineering, Worcester Polytechnic Institute, 1993
Submitted to the Sloan School of Management and the Department of Mechanical Engineering
in Partial Fulfillment of the Requirements for the Degrees of
Master of Science in Management
And
Master of Science in Mechanical Engineering
In conjunction with the Leaders for Manufacturing Program at the
Massachusetts Institute of Technology
June 2001
BARKER
s
F
Jut
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@ 2001 Massachusetts Institute of Technology. All rights reserved.
Signature of Author
I/1
Sloan School of Management
Department of Mechanical Engineering
May 11, 2001
Certified by
James M. Utterback, Thesis Supervisor
Professor of Management and Engineering
Certified by
David E. Hardt, Thesis Supervisor
Professor of Mechanical Engineering
Accepted by
Margaret AndreWs, Exdcutive Director of Masters Program
Sloan School of Management
Accepted by
Ain Sonin, Chairman, Department Committee on Graduate Studies
Department of Mechanical Engineering
*
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Implementation of Lean Manufacturing in a
Low-Volume Production Environment
By
Garret J. Caterino
Submitted to the Sloan School of Management and the Department of Mechanical Engineering
on May 11, 2001 in partial fulfillment of the requirements for the Masters of Science in
Management and the Masters of Science in Mechanical Engineering.
Abstract
Lean Manufacturing is a powerful method to improve a manufacturing environment. Moving
beyond the more traditional Lean settings where high manufacturing volumes and "part"
production are often common elements, the use of Lean techniques for a low-volume finalassembly application was explored in this work.
Instron Corporation was utilized as a research setting to develop and demonstrate the
implementation of these Lean techniques to their final assembly operations. Challenges for this
project included 1) reducing the production throughput time of Instron's Electro-Mechanical and
Hardness material testing products and 2) providing greater assembly flexibility to handle
variations in customer orders. A framework of Lean Manufacturing techniques was specifically
outlined for a low-volume environment. Both the physical assembly environment and work
processes were analyzed as a system. Revised assembly area layouts, standardized work
procedures, point of use (POU) inventory, worker cross-training, organized kanban card-driven
inventory re-supply policies and kanban-driven assembly procedures were proposed and
implemented.
Improvements were realized through reductions in assembly throughput time and variation
reductions in these times. In addition, greater visibility and control of the assembly processes for
both assemblers and management on a day-to-day basis was achieved. Beyond improving the
assembly process, the research demonstrated the importance of integrating inventory
management with the defined assembly process. Results from a revised inventory policy
revealed potential reductions in inventory and improved vendor coordination. Overall, results
from this research effort proved that Lean Manufacturing techniques can successfully be adapted
to low-volume assembly environments. Further, the methods outlined in this project can be used
as a process roadmap to achieve similar improvements in other low-volume assembly areas.
Thesis Supervisor:
David E. Hardt
Title: Professor of Mechanical Engineering
Thesis Supervisor:
James M. Utterback
Title: Professor of Management and Engineering
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4
Acknowledgements
I would first like to acknowledge the support and resources provided by the Leaders for
Manufacturing (LFM) Program. The past two years have been an incredible experience, and I
would like to thank everyone involved in creating this unique program and the education it
provides.
I would also like to thank Dave Hardt and Jim Utterback, my LFM project advisors, for their
support and guidance through the internship process. During numerous visits to Instron, they
greatly helped in analyzing the needs of the company and making suggestions to implement
lasting changes within Instron's environment. They also provided clear direction and insight into
making this thesis a worthwhile reference for implementing Lean methods in similar low-volume
environments.
At Instron, I would like to thank Bill Milliken, Vice President of Manufacturing, for sponsoring
the project and providing the funding to make the project a success. I would also like to thank
Kerry Rosado for his time in supervising the project and setting its direction and objectives.
The Instron process improvement management team members also deserve thanks for their
efforts and willingness to explore new production and inventory management methods. Team
members include Marc Montlack, Paul Meroski, Paul Carmichael, Len Travers, Scott MacEwen,
and Peter Paska. Additional thanks must also be given to all of the technicians on the factory
floor who provided insight into the proposed work process changes and who took an active part
in implementing the new processes.
Outstanding administrative and purchasing support was always available during the project, for
which thanks must be given to Jan Masterson, Ron Mills and Phil Hood. Last, I greatly
appreciated the time for numerous conversations with and recommendations from Brad Monroe,
Vice President of Purchasing, and Jud Broome, Director of Parts and Service. Such candid
conversations provided much insight into the work conducted during the project term and
beyond.
Finally, I would like to dedicate this work to my wife Debby, for her unending support and
commitment through the past two years. Her love and companionship make all of these efforts
worthwhile.
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Table of Contents
Title Page
I
Abstract
3
Acknowledgements
5
Table of Contents
7
1. Introduction
1.1 Thesis Objective
1.2 Lean Transformation: Prepare the Environment then Implement Process Changes
1.3 Instron Corporation as the Research Environment
1.3.1 Electromechanical and Hardness Testers - Examples of Low Volume Products
1.3.2 Original Project Perspective
1.3.3 Resulting Project Goals for Instron
1.3.4 Pilot Process to Exemplify New System
1.4 Summary of Thesis Chapters
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2. Lean Manufacturing and its Application in a Low-Volume Environment
2.1 Lean Manufacturing Introduction
2.2 Key Concepts of Lean Manufacturing
2.2.1 Adding Value and Removing Waste
2.2.2 Implementing Flow in a Production Process
2.2.3 Implementing Pull in a Production Process
2.3 System Implementation and Management Influence
2.4 Review of Prior LFM Lean Manufacturing Thesis Research
2.5 First Look at Instron - Identifying Opportunities for Improvement in a Cyclical
Low-Volume Environment
2.6 Cost of Non-Optimized Process
2.7 Lean Manufacturing for a Low-Volume Manufacturer
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3. Process Selection and Layout Design of a Manufacturing Environment
3.1 Identification of Manufacturing Process
3.2 Decision Parameters to Design the Factory Layout
3.3 Instron Electromechanical/Hardness Assembly Process
3.3.1 Classification of Instron's Manufacturing Process
3.3.2 Process Proposal for Instron
3.3.3 Instron's Physical Factory Arrangement
3.3.4 Final Layout Proposal
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4. Component Inventory Stocking and Material Handling
4.1 Point of Use Inventory Placement
4.2 Failure Modes to Consider for Point of Use Inventory
4.2.1 Multiple Use Inventory - Optimized Stocking Locations
4.2.2 Material Handling Ownership and Control
4.3 Integrating Point of Use Inventory with the External Supply Chain
4.4 How Point of Use Inventory is Managed at Instron
4.5 Materials Resource Planning vs. Pull Inventory Policies
4.6 Kanban Inventory Management at Instron
4.7 Combining Kanban and MRP Processes - Mixed Model Solution for Instron
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5. Implementation of a Single Piece Flow Assembly Process
5.1 Process Flow Definitions
5.2 Process Implementation at Instron
5.2. 1Capacity Analysis
5.2.2 Level Loading the Assembly Schedule
5.2.3 Pull Production, Assembly Kanbans and Strategically Placed WIP
5.2.3. 1Kanban Quantity
5.2.3.2 Kanban Locations for Strategic WIP Placement
5.2.4 Decision Rules Govern Work Process
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6. Alignment of Inventory and Manufacturing Processes
6.1 Setting Proper Inventory Control Measures-The Hidden Costs of Independent Metrics 59
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6.2 Inventory Management Calculations
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6.2.1 Frequency of Inventory Review
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6.2.2 Determining the Minimum Reorder Points (ROP)
63
6.2.3 Lot Size Order Quantities: Should EOQ Theory Be Used?
66
6.3 Proper Inventory Level for Instron Electromechanical
66
6.3.1 Inventory Classified According to Distribution By Value Calculations
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6.3.2 Example Minimum Level Calculations for Class "A" Part
69
6.4 Linearized Assembly Output Enables Inventory Reductions
7. Results and Recommendations
7.1 Results at Instron - Flow Time Decreased by 40% in Electromechanical Production
7.2 Additional Improvements at Instron
7.3 Sustaining the Process Improvements
7.4 New Models Arrive in Manufacturing
7.5 Comparison of the Low-Volume vs. the Original Lean Manufacturing Process Goal
7.6 Future Recommendations for Continuous Improvement
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Appendix A: Data Timesheets
81
Appendix B: Labor Capacity Model
83
Appendix C: Inventory Analysis Model and Spreadsheets
85
Annotated Bibliography
91
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/
INTRODUCTION
Lean Manufacturing is a powerful method used to make lasting improvements in a production
environment. Moving beyond the more traditional Lean settings where high manufacturing
volumes and "part" production are often common elements, the use of Lean techniques in a lowvolume final-assembly application was explored in this work. Instron Corporation was utilized
as a research setting to develop and demonstrate the implementation of numerous Lean
techniques. Results from this research effort proved that Lean Manufacturing techniques can be
successfully adapted to such low-volume environments to provide improvements in throughput
time, product output flexibility, and coordination of inventory requirements.
1.1 Thesis Objective:
The objective of this thesis was to develop a practical methodology to improve the
responsiveness and flexibility of a low-volume assembly process that experiences an inherently
cyclical demand pattern. Using the elements of Lean Manufacturing as a basis for improvement,
a framework of selected Lean techniques was proposed for such a low-volume process that
would specifically provide:
1. Reductions in assembly throughput times to allow manufacturing to become a strategic
method in improving customer order responsiveness
2. Increased production flexibility to allow multiple product variants to be produced using
one standardized production process
3. Increased consistency of output quantity per unit of time
4. Increased coordination of inventory levels to both statistically satisfy manufacturing
demands and maximize inventory metrics
The reader is encouraged to use the framework in similar environments to achieve comparable
process improvements. Numerous functional examples and descriptions from Instron's
implementation are outlined in detail to provide direction in applying this process. Critical
analyses of Lean methods and the problems encountered during the pilot development and
implementation process are also explained to minimize similar encounters in future Lean
Manufacturing implementations.
1.2 Lean Transformation: Prepare the Environment then Implement Process Changes
In embracing Lean methods in an assembly process, both the physical manufacturing
environment and work processes must be considered as a complementary system. Often the
existing physical manufacturing environment must be modified first to more fully accommodate
a new planned process. Both the process and environment of a low volume manufacturer were
analyzed to reengineer them as a system in this project (Hammer, 1992). To better explain the
process in detail, the general methodology was organized into four phases as outlined below.
9
Step One: Identify an optimal work process
The existing work methods were analyzed to determine how well they satisfied the desired
manufacturing process performance metrics. For example, in this research example, metrics
included assembly throughput time and the consistent "heartbeat" of production output. Once
the gap between desired and actual performance was measured, process improvements using the
building blocks of Lean Manufacturing were outlined to prepare an assembly process
methodology that would provide improvements to the chosen metrics.
Step Two: Reorganize the physical plant floor layout and supporting structures
The layout of the factory floor was physically rearranged to reflect the chosen assembly process.
Decision parameters were derived to guide the arrangement, again placing emphasis on the
targeted process output metrics as well as with consideration to the physical design of the
products being produced. For manual assembly operations, actions included aligning assembly
stations to facilitate production flow, moving inventory locations adjacent to the factory floor,
standardizing material handling, and removing physical barriers that reduce teamwork and
communication within product lines.
Step Three: Implement the desired work process
Having organized the physical environment, the revised work process using the chosen lean
manufacturing structure was implemented. Techniques such as pull-based demand production,
strategic kanban placement, development of production decision rules to govern the work
process, and worker coordination and training were initiated. Further, a combination of temporal
assembly strategy, increased labor flexibility, and the creation of a more visually controlled
environment were additional action items implemented.
Step Four: Align inventory management with assembly process
Once production demands were established as part of the process, inventory management was
also restructured to provide quantities that statistically fulfills such production demands. The
internal demands and external suppliers were then coordinated based on these statistical needs.
1.3 Instron Corporation as the Research Environment:
Instron Corporation, headquartered in Canton, MA, is a manufacturer of materials testing
equipment, software and accessories used to evaluate the mechanical properties of various
materials such as plastics, metals, textiles, composites, rubber, asphalt, microelectronics
materials, and ceramics. The company is viewed as the industry leader in materials testing
equipment.
Instron's primary product offering is ElectroMechanical (EM) tensile testing machines. Instron
has also been adding to its original ElectroMechanical group through acquisition of additional
material testing equipment companies. Many of these smaller acquisitions have been moved inhouse to Instron's Canton, MA facility. The Wilson Hardness testing equipment group is one
example of a recent acquisition. Manufacturing integration of acquired products with their
existing production methods has become an issue for Instron, requiring a manufacturing process
framework to apply across multiple product lines.
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Instron's manufacturing responsiveness to customer demands has become an important aspect of
their competitive advantage. The creation of manufacturing Centers Of Excellence (COEs)
throughout its worldwide facilities has elevated the importance of such responsiveness to their
worldwide demand. Creating these COEs has been a process to consolidate assembly of each
specific product family to one of Instron's worldwide locations. Given a product is produced in
a COE, that center supplies the respective worldwide demand. Canton, MA has been designated
as the COE for Electromechanical and Hardness testing machines, requiring production
capabilities that are dedicated to providing fast and increasingly accurate order response for all
worldwide customer orders.
1.3.1 EM and Hardness Testers - Examples of Low Volume Products:
Three Instron product families that are assembled in the Canton location are used as examples to
demonstrate the assembly process framework developed in this project. Electromechanical (EM)
(single- and double-column) and Model 2000 hardness testers are the foundations for three of
Instron's complete testing systems. Material gripping devices and accessories are added to these
frames to create total system solutions for customers' material testing needs. They are the
company's highest profit-generating products.
The Electromechanical products are used for tensile and compression materials testing. Product
variants differ according to testing capacity, ranging from 2KN to over 50KN, with eight models
in this range included in this project. A typical double-column Electromechanical machine is
shown in Figure 1. Main subassemblies include the base tray module containing the system's
electronics, vertical columns providing motion through electric motor-driven lead screws, a
moving crosshead mounted between the columns that carries the load measurement cell,
electronic controller interface, and accessories. Each machine is highly configured to customer
specifications, including load capacity, working height and width, accessories, and software.
Hardness models are used to analyze material surface hardness through the application of surface
compression forces. Model 2000, shown in Figure 2, is configured from three options of vertical
size and various load ranges. Similar in design to the Electromechanical products, the main
subassemblies for this model include the base tray which houses the electronics, vertical actuator
with leadscrew design, frame, loadcell, and controller interface.
II
Figure 1: Model 2000 Hardness Tester
Figure 2: Double Column Electromechanical
Tensile/Compression Tester
1.3.2 Original Project Perspective:
Instron's original project objective can be stated as "Create a manufacturing environment that
can produce any model of Electromechanical or Hardness tester on demand with little or no
delay time." In response to this goal, Instron originally conceptualized a single assembly line to
combine all production of Electromechanical and Hardness products, with testers simply
completed in the order of customer demand. Although the initial approach was broad, it did set
the expectations of creating an environment that would fulfill customer demands in a more
timely manner relative to current methods.
1.3.3 Resulting Project Goals for Instron:
The analysis and active change of Instron's physical manufacturing environment and assembly
processes was used in this thesis to demonstrate implementation of the Lean Manufacturing
framework.
There were three project goals established specific to Instron's process. First, reduce assembly
throughput times in the final assembly operations. Second, transform the physical assembly
environment and work process to better leverage the commonality between EM and Hardness
assembly platforms and common parts usage to increase flexibility of output. Third, establish an
inventory policy to better coordinate in-house inventory levels with manufacturing demands,
including revising internal inventory management and improving the coordination with external
suppliers' processes.
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The framework of Lean Manufacturing techniques, including the use of Kanban control in
assembly, daily production schedules based on the demand rate, and decision rules to guide the
work process, enabled these goals to be accomplished. Managers and employees were
encouraged to sustain the process by using these techniques once the project term was
completed. This enabled such techniques to be used long-term to improve manufacturing's
customer order responsiveness, aligning with the corporation's "On-Time" metric that measures
the timely performance of product shipped to the customer.
1.3.4 Pilot Process to Exemplify New System:
The work completed during this project focused on improving the performance of three select
product families at Instron. The project's focus was limited to these products to prove out the
new concepts with the expectation of expanding the learning and general process framework
from this project to the other assembled products within Instron.
1.4 Summary of Thesis Chapters:
This chapter provides an overview of the thesis objective and Instron project goals. A four-part
process using Lean Manufacturing is outlined to realize process improvements in a low-volume
environment. Instron and its products are then briefly described as the research environment
used to demonstrate the implementation process.
Chapter 2 discusses the principles of Lean Manufacturing, including a brief history of its origins,
its evolution and its current applications. Further, prior LFM research in Lean Manufacturing is
outlined with a description of the extension of such research into Instron's unique low-volume
environment. Instron's manufacturing process issues are identified and explained. Finally, a
proposal to implement Lean Manufacturing techniques to improve Instron's manufacturing
process is diagrammed.
Chapter 3 describes the details of assembly process identification and selection. Instron's
manufacturing environment and process are analyzed, and the most appropriate process is
identified. Physical changes required to the work environment to support a chosen lean
manufacturing process are described. Decision parameters are then provided to assist in creating
the desired environment.
Chapter 4 outlines improvements to materials inventory coordination, including changes in the
physical production environment and material handling methodology. Chapter 4 further
discusses problems in each respective area and how each require close coordination with
manufacturing's metrics.
Chapter 5 outlines the process used to implement assembly process improvements. Demand
capacity analysis, single piece product flow analysis using assembly kanbans, level loading the
demand schedule, and using a decision rule framework to govern the daily work structure are
included
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Chapter 6 provides a model for inventory analysis. Inventory policy alignment with both the
parent manufacturer and its suppliers is outlined. Further, Chapter 6 outlines the benefit in
inventory reductions that can be realized by implementing a linear, lean production process.
Chapter 7 provides results and conclusions of the work performed during the project term. The
similarities and deviations of this low-volume Lean implementation vs. a more traditional Lean
Manufacturing environment are outlined. Solutions to sustain the process and recommendations
for future work are also summarized.
Appendix A includes a sample data timesheet used to collect factory process data.
Appendix B outlines a labor capacity model generated from process data and output demands.
Appendix C outlines an inventory analysis model and required spreadsheets to determine
inventory quantities required to support a given output demand level.
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2
LEAN MANUFACTURING AND IT'S APPLICATION IN A
LOW- VOLUME ENVIRONMENT
An overview of Lean Manufacturing, including a brief history of its evolution, an explanation of
the methods through demonstrating the Toyota Production System, and a review of relevant prior
Leaders for Manufacturing research is provided in this chapter. Process limitations in a lowvolume cyclical environment, using Instron's original assembly process as the research example,
are also described. The costs of such limitations are explained to show just how much these
limitations are reducing the potential gain from production's output. Finally, a process overview
to implement numerous Lean Manufacturing techniques in Instron's low-volume environment is
outlined.
Lean techniques can and should be extended to many different functions of an organization
beyond manufacturing, including marketing and sales, product development, and purchasing.
However, for purposes of brevity and clarity, only those processes involving manufacturing are
covered within this chapter.
2.1 Lean Manufacturing Introduction
Throughout the 1990's and into the current decade, there has been great effort in making
significant improvements to the processes used in manufacturing. Lean Manufacturing, as
outlined by the Toyota Production System and described in such leading books as Lean Thinking
and The Machine That Changed the World, provides the manufacturing world with better ways
to produce products. These methods lead to incredible reductions in human effort, inventory
levels, manufacturing floor space, and overall complexity. Lean production techniques are the
basis for improvement efforts conducted at Instron Corporation, and provide the background for
work described in this writing.
Where did Lean Manufacturing originate? The Toyota Production System (TPS), in essence the
original Lean Manufacturing method, was born in Japan out of necessity. In the Post-WWII era,
Japan was in a financial and economic recovery mode that did not allow them to replicate the
capital-intensive automotive production methods of the western world. Nor was their
productivity in line with the western world - it was 1/8 of that in the United States. However,
Japan had growing needs for low cost transportation of diverse vehicle types, from small cars to
large trucks. Furthermore, Japan's post-war workforce was controlled by American-installed
labor laws. These laws strengthened the position of the Japanese workers and called for
employers to acknowledge these increased rights in employment positions, removing the ability
to continue placing workers in low-paying, low-skill jobs. Working within these bounds, Toyota
set out under the direction of Taiichi Ohno to create a production system that used workers to
their fullest potential and minimized capital investment requirements. (Ohno, 1988)
Ohno created a system that removed all wasteful actions and uses multi-skilled workers to
produce varieties of products on demand. Further, this system was designed around quality, with
15
quality designed and assembled into the product instead of adding additional actions to ensure its
presence. These main tenets of Lean Manufacturing and TPS are further explained in the
following summary.
2.2 Key Concepts of Lean Manufacturing:
Although there are numerous definitions of Lean Manufacturing, there are three major concepts
within the implementation of Lean Manufacturing Processes (Suri, 1998):
Elimination of waste, including wasteful non-value added actions and process steps
Implementation offlow to smooth production processes
Implementation ofpull to produce only when product is needed
These lean production concepts are combined into a manufacturing environment that uses a
highly trained workforce to produce products in wide varieties when demanded. Each concept is
now explained in more detail.
2.2.1 Adding Value and Removing Waste:
Lean Manufacturing begins by identifying which efforts and actions in a given process define
value in the end product. Value, in this context, is defined in terms of customer value, whether
this is the end use customer or the next activity in a given process. Lean Manufacturing then sets
out to redefine a given process to only include those steps that add value. Any additional steps
that are classified as non-value added are considered waste and must be removed. Lean
Manufacturing techniques systematically eliminate or at least reduce waste, leading to reduced
cycle times and reduced costs (Jones and Womack, 1996).
When mapping the value of production steps, each step can be classified into three categories.
The first is full value-added, meaning the step creates value in the final product. The second
category is a step deemed necessary to complete the process but which does not directly create
value in the product (termed Type I Waste). Type I waste must be analyzed with
recommendations to minimize their financial and temporal costs. Finally, there is the step(s) that
creates no value at all and should be removed immediately from the process (Type I Waste).
Within the Toyota Production System methods, wasteful actions and methods are grouped into
seven major categories, as outlined below (Suri, 1998):
" Overproduction - Producing quantities that are not needed, visible as undemanded finished
goods.
" Inventory - Producing semi-finished parts between process steps (WIP) that remain unused
for extended periods of time. Purchased components that are held in inventory for extended
time periods are also forms of waste.
" Transportation - Moving parts within and outside of the factory, including moving material
between factories and to different functional processing areas within the bounds of one
factory.
" Processing - Unnecessary machining/assembly/test steps within a manufacturing sequence.
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Defects - Parts produced that need additional rework or are scrapped due to excessive
presence of defects. Manufacturing quality product by actively preventing defects is more
effective in time and cost than defect repair.
" Motions - Unnecessary worker movement on the assembly line, unnecessary robotic
machine motion, or unnecessary transportation are included in wasted actions.
" Waiting - Workers with excess time, waiting for either machines to complete their operations
or parts to be completed. Workers should not have to wait for the machine, rather the
utilization of the workers should be maximized - the machine is considered to be free.
"
In addition to the seven types of tangible wastes listed above, several attributes of waste in
production systems cannot be so easily quantified. Job complexity, shop floor and
interdepartmental confusion, lack of engineering support for new product introductions, order
expediting, rework and repair of nonconforming parts, and worker motivation are examples.
Some, such as shop floor confusion and order expediting, are actually effects of more traditional
manufacturing practices due to extended lead times and multiple jobs waiting in any given work
center. Improvements must be considered for these qualitative measures as well.
2.2.2 Implementing Flow in a Production Process:
Once waste is eliminated from a process (or waste-reduction goals are established), the
remaining production steps are arranged in such a way to focus on the specific product's
manufacturing requirements as a system. In contrast, traditional arrangements focus on the
function of each process step, and tend to group such similar process functions together. Refer to
Figure 3 for visual comparison of these two methods. In a product focused arrangement, all of
the required resources to assemble the product are physically arranged adjacent to each other in a
single area (loop, line, or cell), allowing each step to be processed in required order with limited
backflows or stoppages between process steps. This mentality calls for disregard of the previous
boundaries between functional processes. Physical re-arrangement of the production process
from grouped functional equipment to lines that include elements of each functional category is
most often required. Results allow a product to be produced with a continuous "flow" of
activity.
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Function Process1
Product I
Function Process2
FlowLine
Product 2
Flowline
-0
Product 3
FlowLine
Function Process3
--
Function Process4
Functional Work Area
Product 4
FlowLine
-
Flow Line
Figure 3: Schematic of Functional vs. Flow Line Configurations
In the flow line configuration, product orders are introduced into the manufacturing process one
at a time and completed at a constant rate. Each process step or subassembly is completed for
that product in order, with little or no WIP stored between steps. Capacity for the process is
calculated based on the total takt time. Takt time is the time per unit per process step based on
customers' demand rate. It is the inverse of cycle time. The pace of production is therefore set
based on the pace of sales. Work is evenly distributed in each process step to allow product to
enter and exit the flow process based on consistent takt time increments.
The workforce must become increasingly flexible within a flow process. Since dedicated
functional departments are removed, the people who operated under the old functional realm
must be retrained to gain knowledge of all steps in a production process. Any member of the
workforce will then be able to be moved to any stage of the production line when needed.
Personnel performance measures must also be realigned with the flow process, rewarding
additional training and the broad knowledge of multiple areas as opposed to rewarding functional
expertise in narrowly defined manufacturing categories.
Finally, production "flow" is based on a constant rate of average production. In environments
where the assembly time varies with product type and manufacturing complexity, the demands
placed on the process must be actively managed to maintain average production rates. Leveling
the production schedule helps to accomplishes such average rates. Product variants should be
staggered in the production queue based on required assembly times, smoothing perturbations in
the order queue to achieve consistent average output times.
Leveling production is best explained through an example. Assume three configurations of a
product, X, Y, and Z that are demanded in equal amounts and require 2, 4 and 6 hours
respectively for assembly. If these products were assembled by batch building in lots of three
per day, such as XXX YYY ZZZ, the assembly process would take only 6 hours the first day
assembling the X configurations (leaving excess capacity) but would require 18 hours on the
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third day assembling the Z configurations (requiring greater capacity). Assembling in such a
manner causes delays in producing configurations Y or Z and also places nonlinear demands on
component inventory levels. Delayed shipments (assuming demanded in equal amounts) and
increased levels of inventories to cover cyclical part demands result. Leveling calls for the
assembly order XYZ XYZ XYZ, linearizing labor requirements each day, smoothing inventory
demands, and allowing earlier product shipments of each variant.
Tactics, such as setting "flex ranges" of acceptable order variations to which sales and marketing
must agree, are used to maintain consistent flow by putting constraints on the expectations of the
manufacturing system. Such flex ranges limit the extent of leveling required. Level production
also smoothes upstream production and decreases inventory pile-up throughout the
manufacturing facility, shortening the overall average lead-time, throughput time, and reducing
non-value added activity.
2.2.3 Implementing Pull in a Production Process:
Simply put, the concept of a pull process is "Ship one, build one" (Jones and Womack, 1996). In
theory, units are produced only when demanded, in effect using customer orders that remove
finished units from the end of the process to initiate a "pull" of another unit into the finished
product area. In chain-like action all previous subassemblies are pulled through the assembly
process from end to beginning. By comparison, a traditional "push" process calls for scheduling
production and building inventories at each production step. Using pull, each upstream step
produces parts or subassemblies only when the downstream step demands additional parts or
assemblies. At each step, supplied material is also pulled into the production process when
needed, coordinated with vendors to deliver only in the quantities demanded at the times needed.
"Pulled" material is also known as just-in-time (JIT) delivery.
Information to initiate such pull-based manufacturing actions flows in the opposite direction of
the material flow, often through the use of Kanbans. Kanban is the Japanese word for "sign" or
"card". These cards are used on the factory floor to physically convey information about
production flow. They signal what to produce, when to produce it, and what quantity of it to
produce. Overall, the goal of the pull system is to remove speculative production (that often
results in overcapacity or unfulfilled demand) and provide the ability to produce to actual
demands while reducing WIP levels and cycle times of each production step.
2.3 System Implementation and Management Influence:
The above describes an ideal world within Lean Manufacturing of stable flow of product that is
pulled by demand through multiple production processes. The key word here is "ideal."
Although the concept is far superior to that of mass production, it must be understood that Lean
Manufacturing is a systems solution of continuous improvement that takes time to implement
and refine. Further, it is very people-focused, and changing people's methods and attitudes to
"see"l new solutions is often difficult to do. Implementation requires management to support
this system as internal coaches, becoming the catalysts for change. Without high level support, it
is difficult to develop a strong human infrastructure, potentially leading to functional areas
embracing changes independently and realizing sub-optimized results.
19
Traditional performance metrics are also modified using this process. These new metrics also
need to be embraced by the top management. Indicators of plant performance under a Lean
Production system are often measured by the following metrics, with lean plants being able to
achieve high levels of all four metrics simultaneously:
1.
2.
3.
4.
Customer order responsiveness (reduced lead times)
Productivity increase with cost reductions
Flexibility in model output
Quality improvements
Although stated briefly above, examples of each metric and their interactions will be displayed
throughout this writing.
2.4 Review of Prior LFM Lean Manufacturing Thesis Research:
A number of Leaders for Manufacturing theses have been developed on the theory and
implementation of Lean Manufacturing processes. Through the past ten years, increasing
corporate awareness and desire to transform processes using Lean techniques has prompted
much LFM research. Theses most relevant to the topic have been briefly outlined below.
Arthur Raymond studied the applicability of Lean Manufacturing to a low-volume fabrication
facility at the Boeing Company in 1992 with his thesis, "Applicability of Toyota Production
System to Commercial Airplane Manufacturing." The work provided both a general overview of
TPS application as well as a more specific set of recommendations to apply TPS to part
fabrication shop environments. It was concluded that TPS is indeed applicable in such lowvolume settings; however, it was deemed more applicable to apply it to fabrication processes
rather than assembly processes. His findings further concluded that it is more difficult to
implement lean manufacturing in a complex environment such as Boeing. For instance, a Just In
Time supply system may not work efficiently due to Boeings tremendous product complexity
and distant supplier network. Use of kanbans in Boeing's environment is also limited to
controlling internal production flow, manufacture of small parts, and only signaling delivery (not
production) of complex assemblies. In Raymond's view, production of complex assemblies
required too much lead-time to make the use of kanbans effective. Results further explained the
possible savings from decreasing lot sizes, removing intermediate quality inspections, and
creating more standardized work practices.
Dennis Hager researched lean manufacturing implementation in a low-volume industry in 1992
with his work "Applying Continuous Flow Manufacturing Principles to a Low Volume
Electronics Manufacturer." His work analyzed the causes of poor manufacturing performance in
a turbine engine controls assembly work cell and provided solutions for understanding the
general manufacturing process. Metrics targeted include cycle times, capacity restraints, and
proper scheduling practices. Results showed that capacity within a work center needs to be
clearly understood, and that exceeding capacity leads to detrimental performance including
shipment delays and excess WIP. Further, capacity and scheduling must be coordinated with
controlled variations in demands between time periods. Finally, Hager recommended eliminating
20
schedule revisions after material has been released into manufacturing, reducing the need for
additional expediting time and lowering overall inventory levels. In summary, this was a
functional thesis that described distinct problems and specific solutions to capacity issues in an
existing work center, with recommendations that can be applied by the reader to other
manufacturing applications.
Paul Dul analyzed the "Application of Cellular Manufacturing to Low-Volume Industries" in his
thesis based on research in manufacturing aircraft doors at a major aircraft manufacturer in 1994.
This work compared low-volume production that is historically process-centered with a revised
production system that is product-centered. Justification was provided to argue that traditional
process layouts are outdated and many low volume producers can increase efficiencies by
organizing operations by product. To prove this point, the door assembly cell at an aircraft
manufacturer was transformed into a "product-centered" pilot cell to allow the new process ideas
to be established, with the expectation that adoption in other cells would follow. Both
production cost and lead-time were metrics in this example, and both were reduced when
production was moved to the product-centered system. The work outlined two key principles
used to overcome the limitation that low-volume products do not have enough work to support
dedicated cells. First, products should be designed with common parts to leverage parts in
multiple assemblies. Second, manufacturing cells should be designed to be flexible to
accommodate variations within a part family. The feasibility of product-centered work cells in a
low-volume environment was proven and cost savings justification through Net Present Value
financial analyses was provided.
Mark MacLean summarized "Implementing Lean Manufacturing in an Automobile Plant Pilot
Project" in 1996. This was an example of implementing intermediate lean methods on the
production floor of a large existing auto plant. Methods outlined include revised assembly line
designs, material handling methods, and assembly error reduction methods. Abrupt changes to
an existing union-run mass-production plant result in system shock, and MacLean proposed
taking intermediate steps as preparation to implement a full Lean process was a better approach.
Actions were implemented on a pilot assembly line that was ramped up for a new auto model
introduction, where the risk in implementing a new process was minimized and the ability to
monitor the performance of a system was increased. MacLean concludes his work by explaining
that full transition to Lean Manufacturing must be driven by teamwork and organizational
change, and until management and union leadership promoted such changes, Lean transitions
could not be fully realized.
Barrett Crane also analyzed a low-volume environment in 1996 in his thesis "Cycle Time and
Cost Reduction in a Low Volume Manufacturing Environment." This work outlined the
implementation of a kanban-controlled assembly process specifically designed for a low-volume
application. The work also analyzed cycle time and found that for low-volume applications it is
more feasible to track the overall cycle time as opposed to the cycle time of individual steps.
Here again, a pilot production area was established for one product line to experiment with the
new process, thereby proving out the concepts with minimal negative impact on all production.
Results showed that a kanban process could be successfully implemented in a low volume
environment, providing cost and cycle timesavings as well as a basis to provide feedback for
ongoing continuous improvements.
21
Steve Harman researched "Implementation of Lean Manufacturing and One-Piece Flow at Allied
Signal" in 1997. This work outlined the implementation actions of a one-piece flow production
system in a traditional low-volume sheetmetal production work center. Numerous lean topics
were covered, including material flow, production scheduling using a pull system, and work
center capacity modeling. To improve material flow, a focus was placed on creating dedicated
product-centered "flow-loops" sized for capacity needs. This showed improvements in both
work in process (WIP) and production lead times. A production pull system was also created to
promote linear production, using signal boards and kanban cards as production control
indicators. Finally, a rough-cut capacity model was created to analyze flow loop utilization,
bottlenecks, and one-piece flow. The model was also proposed as a capacity planning tool for
future expansions. Overall, this thesis provided a clear systems view of implementing a lean
production environment. It warned to implement lean practices fully and not in isolated
segments to realize the full benefits. Further it recommended using employee training and
incentives along with fact-based data-driven decision processes for long term lean
improvements.
Jamie Flinchbaugh analyzed the interrelationships between lean manufacturing and factory
design in his 1998 writing "Implementing Lean Manufacturing Through Factory Design." He
explained the difficulties in diffusing lean manufacturing principles as a new technological
system, and that proper factory design initially would alleviate many transition difficulties. Two
tools were demonstrated to better understand and explain factory design and the factory's
operating systems. The first, Axiomatic Design, was used to derive the physical design
parameters of a factory from functional requirements. The second, Queuing Theory, was used to
calculate production throughput performance and variation reduction. It concluded with
reviewing the requirements of starting a new factory and how to minimize the associated risks.
Results showed that design must include establishing independent production areas,
decentralizing manufacturing support activities, and creating modular, scalable processes and
facilities. Further, the greatest throughput improvements were realized through variation
reductions and continuous learning within the production environment.
2.5 First Look at Instron - Identifying Opportunities for Improvement in a Cyclical
Low-Volume Environment:
Previous LFM work has shown that Lean Manufacturing techniques can provide significant
improvements in manufacturing processes in short amounts of time. Continuing this effort, the
work presented in this writing further supports the application of lean manufacturing in the lowvolume environment at Instron. However, before applying lean principles, one must first
identify the specific issues to be addressed within the existing low-volume environment.
"You will not know where you are going unless you know where you came from."
As a capital equipment provider, Instron operates within an inherently difficult sales
environment. The nature of capital equipment sales forces the majority of capital purchases to be
transacted near the end of each quarter, creating quarterly cyclical demands on manufacturing.
22
Instron's own corporate sales metrics further support this difficult environment by measuring the
Instron sales team on quarterly results. Given this internal metric, the sales division sells product
at non-linear rates through each quarter, giving less effort to sales at the beginning of the quarter
and ramping up sales by the end of the quarter (Refer to Figure 4). Great amounts of stress are
placed on the manufacturing process to satisfy such cyclical demands.
Monthly Demand for EM Product-Year 2000
80
E
70
-4-single
60
column
EM
0
40
30
--
W
Double
Column
EM
S20
Month
Figure 4: Electromechanical Demand Volume Analysis Showing Cyclical Demand Patterns
In reviewing Instron's internal operations, the original assembly process required attention to
increase standard work methods and output consistency. Production was driven by customer
orders, which were retrieved from the Instron Business System (IBS) database that links sales,
manufacturing and procurement. Once per week, a list of customer orders sorted by the order
promise date were retrieved from the database and posted in the manufacturing area. All
equipment orders for the upcoming weeks, including machine type, custom specifications, and
due date, were included. Based on order data, operators were instructed to build machines to
fulfill those orders, with success measured on achieving monthly/quarterly quotas and achieving
the promised "On Time" delivery dates.
Restrictions in Instron's system structure were numerous. Methods needed to be clearly outlined
to help consistently achieve the "on-time" dates. All of the steps to produce a machine were
inherently "known" due to the long tenure most employees possessed working in Instron
assembly. It was true that all operators were technically knowledgeable on the assembly
requirements, but process steps were not strictly followed. Therefore, there was a lack of
consistency in method among employees that resulted in limited control of output and limitations
in transferring processes to new employees.
Output was measured on weekly, monthly, and quarterly segments. These time increments were
considerably longer than the time required to complete one unit. Therefore, total assembly
23
output times often varied per order when measured against the extended time increments. This
created non-linear production and shipping patterns that magnified the traditional hockey-stick
effect created by cyclical sales patterns. Partly due to human nature, assembly was always trying
to play "catch up" to the planned number of machines by week's end. Production rates were
slow at the beginning of each respective period and then "ramped up" to compensate at the end
of the period. Once demand per time period overtook actual output per time, it was difficult for
production to catch up. Figure 5 shows an example of how output lagged planned production
during the quarter. Order demand was near exponential, but planned production was linear
through the quarter, offset by pulling orders forward in the production schedule when possible to
accommodate the difference. However, since production was measured in long time increments,
output linearity on a day-to-day basis was not often achieved.
C
.2
350
300
0
250
200
CL
SC
_
E
::
C.)
-+-Planned
Production
Quantity
eActual
1
100
50
li~
10
Production
Quantity
0
1415161718192021 2223242526
Week # in Quarter
Figure 5: Planned vs. Actual Units Production for Electromechanical Product
Second Quarter of 2000
Machines were often built in small batches. The desired number of units was completed at the
end of most weeks, but production output each day was not consistent. Some operators viewed
this batch production as the most efficient way to produce. However, batch production only
provided a local optimum at each workstation, with delays between stations a direct result of
batch building. Process output was inconsistent with the desired metric of achieving low systemwide throughput times. To demonstrate this effect, delay effects from batch production are
presented in Figure 6, showing the extended total process time from building a small batch size
(n=2) vs. building a single unit at a time (n=1) (Mahoney, 1997).
24
MULTIPLE PIECE FLOW - LOT SIZE OF TWO
1
2
3
4
5
CL
wU
6
CO)
Cl)
ILl
SINGLE PIECE FLOW - LOT SIZE OF ONE
0
0.
1
2
3
4
5
6
TIME
Figure 6: Lot Sizing Illustration to Demonstrate Effect of Single Piece Flow
Figure 6 illustrates the time difference required for completing assemblies when single and
multiple piece flow are considered. Each process step is shown on the vertical axis. The
completion time of each unit is represented on the horizontal axis. One box represents one unit
of production at each stage of assembly. Each stage in the top half of the diagram is completed
in batches of two; a step is not initiated until both units have completed the previous step.
Compare this to the time reductions illustrated in single piece flow in the bottom half of the
diagram. Increases in number of units produced per lot show both a resultant increase in time
required to get all machines completed and an increase of work in process between assembly and
test operations. Increases in lot sizes therefore decrease order responsiveness since more time is
required to complete a single unit.
2.6 Cost of Non-Optimized Process:
The above scenarios each contributed to extended production flow time. What were the costs?
Revenue opportunity costs were evident due to shipping product late in each week and month,
therefore delaying revenue inflows. Inventory carrying cost increased for both excess WIP that
25
was located within the production stations and inventory was carried to satisfy the resulting nonlinear "hockey-stick" increases in output at the end of each period. Long flow times also
inhibited the ability of the company to respond to order changes imposed by customers. The
order remained in the "process" longer; therefore, to achieve the scheduled due date, assembly
had to be started earlier, leaving less time for customer changes. Such changes ultimately cost
the company in time to make adjustments to orders in process as well as disrupted the
manufacturing process. A downward spiral of longer lead times, more potential changes to
customer orders, and increased frequency of missed schedule dates due to changes and rework
often resulted.
Intangible costs of long flow times were also considered. The discovery and feedback on
production and/or part quality issues was prolonged. Complexity, additional scheduling support,
worker confusion, and order expediting were all factors that were difficult to quantify but yet
were increased with longer lead times. However, all had to be considered when implementing
system operational improvements.
Flow time had to be carefully considered, and its associated costs had to be included along with
labor, materials and overhead for financial management. Manufacturing had to change its
metrics and analysis methods to account for all relevant costs, more than just focusing on labor
efficiency and capital investments. (Graves et al, 1992). Traditional labor-based cost accounting
did not favor flow time reduction since it may have increased the labor cost per job and the
required capital equipment. However, looking at the labor element in a typical Instron product, it
was a small percentage of the overall cost (Figure 7).
Breakdown of Costs for
Representative EM Assembled
Product
Material
Labor
84/l
6%
Figure 7: Material and Labor Cost Breakdown for Electromechanical Product
2.7 Lean Manufacturing for a Low-Volume Manufacturer:
Having identified the most prominent manufacturing issues, a proposal was made to analyze and
improve Instron's product throughput flow times. Embracing the principles of Lean
Manufacturing, this project provided a framework to guide such improvements in a low-volume
setting. Using this lean approach, the project at Instron was directed by the building process
shown in Figure 8 (Diagram modified from Monden, 1993). The elements shown in this
framework are described in detail throughout the following chapters.
26
Notice that both the physical factory environment and the work process were included in the
building process. Within such a low-volume environment, flexibility was key to achieving flow
time reductions given the inherent variations. This was developed as a system for both the
physical set-up and more standardized work practice. To begin, the required process is defined
and the physical factory environment is modified to accept the newly defined process. These
first phases were developed in Chapter 3.
Initiation of Lean Improvements by Team Actions
Physical Floor
Layout
Point of Use
Inventory
Work Process Identification and
Improvement Implementation
Assembly
Personnel
Physical Flow
Line
Rearrangement
Flexible
Workforce through
Cross Training-
Increased Worker Productivity
Restructured
Inventory
Levels
Standard
Work
Procedures
]
Flexibility in
Output Quarntity
Production
Leveling
Reduced
Lot Sizes
I' T4Single Piece Flow
Assembly Production
Lead Time
Reduction in
Flow Days
Improved Sales
and Marketing
Relationships
Vendor
Relationships
Cost Reductions
Increased Customer Responsiveness
Figure 8: Proposed Framework Using Lean Manufacturing Principles for Instron's LowVolume Production Process
27
28
3
PROCESS SELECTION AND LAYOUT DESIGN
OF A MANUFACTURING ENVIRONMENT
The selection and design of a factory layout must reflect the desired manufacturing process. A
method for process identification is outlined in Chapter 3 followed by an analysis to optimize the
physical factory layout to complement the identified process. Instron's manufacturing
environment was then used as an example to demonstrate such process identification and design
layout adaptation to the desired process.
3.1 Identification of Manufacturing Process:
Numerous factors must be considered in identifying a manufacturing process. Five factors that
have the greatest influence are:
1. Annual product volumes
2. Product variants under consideration
3. Manufacturing's internal and external metrics in relation to customer needs (such as order
response time)
4. The level of vertical integration (final assembly, parts production in addition to final
assembly, or full integration from raw material processing to finished product)
5. Process flexibility to react to volume changes and product substitutions/additions
Using these factors, one can refer to Hayes and Wheelwright's widely acknowledged productprocess matrix that is provided in Figure 9. It relates the manufacturing production process to
the product type and overall corporate strategy (Hayes and Wheelwright, 1979). The matrix
outlines a range of processes from lower volume, highly customized products requiring more
job-shop type manufacturing, to higher volume products with limited options allowing for
smoother line- and continuous-flow processes to be utilized. A product/manufacturing division
within a company can be characterized as occupying a region of the matrix. The distinctions
between each segment are further described below. Although the segments are listed separately,
overall the matrix should be considered a continuum often exhibiting overlapping characteristics.
29
PRODUCT
___
STRUCTURE
FROCESS
ETRUCTURE
v
LOW VOLUMES
LOW STANDARDIZATION
CUSTOMIZED PRODUCT
MULTIPLE PRODUCTS
HIGHER STANDARDIZATION
LOW VOLUMES
FEW MAJOR PRODUCTS
HIGHER VOLUMES
JOB SHOP
DISCONNECTED
LINE FLOW
INSTRON
MFG
CONNECTED
LINE FLOW
(ASSEMBLY LINE)
CONTINUOUS
FLOW
Figure 9: Product Structure is Related to Process Structure
And Varies by Industry and Sales Volumes
30
COMMODITY PRODUCT
HIGH STANDARDIZATION
HIGHER VOLUMES
Job Shop Process: Numerous unique tasks are required to complete a unit of product output,
often resulting from widely dispersed product offerings or specialized product manufacturing.
Volumes of each product are low. Processes incorporate flexible equipment and jobs are often
labor intensive.
DisconnectedLine Flow Process: Product variations can be offered through this process, many
customized with numerous options assembled in a single production area. A process flow
pattern is established, even though discontinuous, with a set of distinct operations lined up in
order. Although each process step should be calculated to balance production times, often steps
result in variations in time; thereby creating a situation where work in process can accumulate
between process steps.
Connected Line Flow Process: The assembly line is one example of a connected flow line
process, characterized by higher product volumes with limited variety. Higher standardization is
evident in the included products and the production method is time-paced throughout the
process. The process is less flexible in accommodating changes over time, often due to high
capital costs of dedicated line equipment.
Continuous Flow Process: High product volumes with little to no flexibility are produced in a
continuous process. Product variation is very limited (often to a single product). Product moves
in continuous motion through all process steps. Examples include chemical and food production.
The matrix in Figure 9 forces a product to be viewed in two dimensions, showings that BOTH
product and process are important elements of a company's strategy. A great new product could
be matched with an incompatible process that requires an excessive set up time or capital,
leading to failure. On the other hand, a product with a stable design and long term production
schedule could be hampered by a non-standardized production process. Therefore, both the
product and process should be considered as part of a company's competitive advantage.
3.2 Decision Parameters to Design the Factory Layout:
Once the optimum process has been identified for producing a product or product family, the
physical factory layout of the manufacturing area must be arranged to support the process.
Parameters to incorporate in the layout include:
*
*
0
*
*
*
*
*
*
Production capacity requirements
Equipment layout to optimize manufacturing's throughput time metric
Number of assembly stations required based on production time requirements and
breakdown of assembly procedures according to the product's inherent design
Commonality between products' designs to combine product variants into a given
manufacturing area
Ability to allow changes in production quantities over time
Ability to expand the layout to incorporate new product introductions
Location of parts inventory with respect to the assembly process
Efficiency of floor space utilization
Ability to enable close worker communication within and between assembly areas
31
Once defined, these concepts form the main components to identify and plan the physical
arrangement of an assembly facility. As demonstration of this identification and physical layout,
Instron's manufacturing environment was analyzed. To make it clear what Instron's process
included, the required assembly steps for the product and process under analysis were first
outlined.
3.3 Instron EM/Hardness Assembly Process:
Figure 10 illustrates the typical assembly process steps used to assemble Instron
electromechanical products. In optimizing the process, it was determined that the current
physical actions directly related to assembling each product (as outlined in Figure 10) were
appropriate to transfer to the new process. However, the flow process, the timing of assembly
starts, and the combination of products built per line were further redefined.
Electronics
Assembly
Base
Load Cells
Tray/Electronics
Assembly
and
Accessories
Tray and
Top End
Frame
Run-in
Frame
Load Cell
Complete
Assembly
System
Audit and
Integration
Cycle
Test
Calibration
External
Inventory
Housings
Update
Assembly
Ship
Product
Top End
Column
Assembly
Figure 10: Assembly Process Map
for Instron's Electromechanical Products
Description of Process Steps:
Base tray assembly: Electronic system controller cards and cables are mechanically fastened into
a pre-formed sheet metal tray. The tray also acts as the product's structural base.
Top end column assembly: Vertical guide rods, milled lead screws, and milled structural beams
are bolted together to form the vertical frame, providing a structure to support and translate the
system's load cell.
32
Integration assembly of the tray and top end: The tray and top end are aligned to form the
complete structural frame. Additional items such as an electric drive motor, drive belts, column
covers, and top stabilizer plate are added to complete the assembly process of the functional
frame.
Frame run-in cycle: The machine is cycled without load during an overnight time period.
Cycling provides the frame a break-in time and is also the first of a series of frame tests.
System Test: Following frame cycling, compliance testing is conducted on the frame and
accessories to ensure system calibration.
Frame test: Tests the integrity of the frame itself, including linearity for its full range of
motion and proper function of all components.
Calibration: Calibrates the load cells that are purchased with the machine. The cells are
tested on the frame to determine overall system-level performance.
System audit: Tests all included accessories, again to ensure overall system performance.
External housing assembly: External protective covers are assembled to the frame. The covers
provide aesthetics and protection to the internal electronic components.
Audit and Inventory Adjustment: The product is audited for completeness and order tracking for
the customer. Inventory utilized in the machine is "backflushed" from the inventory database to
remove it from the on-hand inventory balances.
3.3.1 Classification of Instron's Manufacturing Process:
To classify Instron's processes, it was necessary to first review the key functional attributes of
the production output. As listed earlier, these included production volumes, customer-related
manufacturing metrics, the level of vertical integration, the bounds of products variants included
in the analysis, and the desired level of process flexibility.
Production Volume: Both historical sales and future forecasted sales were used to obtain
expected production volumes. Trends in historical sales provided the baseline demand. In
addition, forecasted regional sales goals provided a more realistic view of the future needs.
Increased demands must also be factored in for any new planned product introductions, with the
expectation that new products typically exhibit higher demand variability during the ramp up
phase. For the three major product categories analyzed in this work, the following demands
were used for the year 2001. Demands equated to approximately 40 machines per week, placing
Instron into a relatively low volume manufacturing category.
Model Type
Tabletop EM
Single Column EM
Model 2000 Hardness
Year Demand Volume Forecast
1000
900
600
Table 1: Future Yearly Demand by Model Type
33
Customer-RelatedManufacturingMetrics: Manufacturing must satisfy the ongoing customer
demand of lower order lead times through the reduction of throughput assembly time. In
addition, they must satisfy the corporate "On-time" target to satisfy the quoted delivery date
promised to each customer. Assembly and test times, along with availability of parts and
included accessories, drive a large part of this value. For this analysis, the time for assembly/test
was the primary target.
Vertical Integration: At Instron in Canton, the current process included only final assembly using
parts and subassemblies supplied by outside vendors. Vertical integration into component
manufacturing was not feasible in the short term due to floor space and capital equipment
limitations. These limitations bounded the analysis, with the potential for increased vertical
integration being outside the scope of the project. However, this does not mean that vertical
integration for future needs should be discarded as an option, only that the strategy would require
additional analysis with a longer-term planning horizon.
Product Variety: Were all products similar in size and complexity? Did they require similar
assembly techniques and equipment? How were they be divided into product families? The
proposed process at Instron included three of Instron's major product families - Single and
Double Column Electromechanical and Model 2000 Hardness - to initiate this project as a pilot
program in one division. These three product families had:
Similar assembly requirements
Similar functional test requirements
* Relatively similar physical size and assembly complexity
Flexibility: Instron's demand varied throughout each quarter, requiring output flexibility in each
assembly area to adjust quantities in relatively short monthly and quarterly periods. Further, new
product introductions had to be able to be integrated into the production area with minimal
rearrangement. Additional product introductions required longer-term flexibility to rearrange
and expand the process with minimal capital requirements.
3.3.2 Process Proposal for Instron:
Instron's EM final assembly process was positioned in the Product-Process Matrix as shown
earlier in Figure 9. Based on the products historical sales and future marketing forecasts of
relatively low quantities (-2500 units/yr), the discrete assembly operations required, five major
product offerings each with numerous configurations, and ease of rearrangement, the
manufacturing requirements were best met using a disconnected flow process with manual
assembly / test operations. The decision parameters outlined earlier then formed the basis to
determine the specific physical layout to achieve an optimum disconnected flow process at
Instron.
34
3.3.3 Instron's Physical Factory Arrangement:
Once the process was identified, arranging the physical environment required careful
consideration of the specific products being produced. Each decision parameter had to be
considered in context of the unique physical product. For Instron's products, some additional
considerations included:
" The products analyzed in this project have a physical size ranging from 18"x18"x36" to
24"x60"x72", requiring substantial floor space to complete all assembly steps and large
transport carts to transfer the product between assembly stages.
" Parts inventory consumes large volumes of storage space (up to pallet-sized space per
part) with some parts requiring the assistance of overhead cranes to lift and position them.
" Demand volumes are similar for all three product families - no product heavily outweighs
the others in volume.
" Although all three product families are similar, they each have several distinct assembly
requirements leading to numerous specific parts inventory requirements and assembly
stations.
" Each product family has numerous unique test requirements in addition to common frame
testing, therefore equipment requirements vary between product families.
Given the context of assembling the specific product in this analysis, the physical environment
was proposed to have one flow line for each product family. In theory one line could incorporate
all three product families based on similar assembly and test requirements. However, given the
size of the product and the volume of space required to store part inventories adjacent to
assembly, it was not realistic to assume that all products could be produced from one line. This
would have resulted in a line of extensive length, making product movement more difficult
between assembly operations. Further, one long line would limit how parts could be optimally
placed next to each assembly location due to their size and the space limitations. The variations
in assembly time per station per product (outlined in Chapter 5) would also have caused
excessive delays between stations and increased complexity in moving product between stations.
Discrete assembly stations with dedicated assembly/test equipment were aligned along each
respective product flow line. Providing short transfer distances between stations, such alignment
minimized non-value added motion and transportation effort. Dedicated assembly and test
equipment allowed each line the capability to completely assemble and test a given product.
Continuous flow was more easily achieved with this dedicated equipment, avoiding queuing
product to wait for assembly/test equipment to be available. Dedicating equipment to each line
went against the metric of maximizing equipment utilization to minimize capital costs, since
equipment may not always be in use for every assembly operation. However, equipment was
positioned to be available when needed to support increased product throughput.
The number of assembly/test stations within each line was determined from two parameters. The
first parameter was takt time, or the time for assembly based on the customer order rate. The
second parameter is the design-based assembly breakdown. In Instron's low-volume example,
products were designed with numerous subassemblies that each required assembly at one
time/one station. As later shown during discussion on process and capacity in Chapter 5, these
35
breaking points in the designs were combined with takt time calculations to finalize the number
of assembly stations within each product family.
Commonality of subassemblies between products was also leveraged in the layout design, with
common assemblies being assembled in one area to supply multiple assembly flow lines. Base
trays containing shared components and electronic card cages common to multiple models were
assembled in a common location to feed into each respective flow assembly line.
To further reduce manufacturing delays, point of use (POU) parts inventory used in production
was located on the factory floor. Material was then available on demand and within reach from
each production station. However, locating the point of use inventory could not hinder the
production process itself. Implementation of this point of use inventory method is further
discussed in Chapter 4.
Flexibility in the layout was required for both short-term variations in monthly demand and the
long-term introduction/removal of products. Relocating workers between lines to reflect demand
changes accomplished short-term flexibility. Long-term flexibility was achieved both by varying
the number of workers and maintaining the ability to reconfigure the assembly equipment with
little effort to allow expansion or contraction. Instron's layout provided flexibility by storing
part inventory on wheeled racks and creating mobile assembly workstations and test equipment.
Last, the layout incorporated the physical attributes of a lean environment. These included clear
visual indications of assembly sequence actions, specific areas for process control mechanisms
such as in-process subassembly kanbans, and efficient utilization of space with no areas to store
"waste" including excess work in process and obsolete parts/equipment. Further, physical
barriers creating distance between the production areas that inhibited workers from
communicating and collectively solving problems were removed (Schonberger, 1986).
3.3.4 Final Layout Proposal:
The redesigned layout proposal is shown in Figure 11. Proposed results included three main
flow lines divided by major product family, supported with a common base tray and electronics
assembly area. This layout best supported single piece production flow for the included
products. Designing adjacent flow lines also supported close worker communication. The floor
space can also be easily reconfigured, providing flexibility and expandability in both the long
and short term.
36
POU INVENTORY
Top End
p
0
v
E
g
Assembl
Common
Base Tray
TnFro
Receiving
T
R
System
Run-In
Assembly
Tnteorntinn
System
Test
Cover
and
Finish
K
A
Nand
lrn
System
B
Top End
Assembl
+
System
AssembI
Elsens A
lArea
.
System
Test
-
POU INVENTORY
LPOU
LseTop
System
Run-In
Cover
and
Fins
Shipping
INVENTORY
End
Assembl
sp[etm
r
eete
.
Exit to
System
Assembly
-
inetn
t
System
Run-In
System plaCover
Test
and
Finish
POU INVE NTORY
_
Figure 11: Schematic of Final Instron Floor Layout Configuration
The proposed layout can be broken into three main elements - assembly workstations, point of
use inventory, and kanbans. Workstations were simply benches and test stations placed where
needed at each assembly station. Not every station required workstations, as shown in the layout
diagram. Most assembly was conducted directly on wheeled carts, only requiring open floor
space to move between work areas. Point of use inventory was located on the assembly floor
along each flow line. Last, strategically placed kanbans to buffer against variations in assembly
and test were placed in the layout. Both the full point of use inventory and kanban usage were
new elements to this production facility, and required careful implementation. Explanation of
point of use inventory control is fully outlined in Chapter 4. Calculations for kanban and
workstation quantities for each line are further discussed in Chapter 5.
37
38
4
COMPONENT INVENTORY STOCKING AND MATERIAL
HANDLING
Materials coordination is one of the most important supporting factors for a lean factory. A
method to implement and control point of use inventory on the factory floor has been outlined in
this chapter to assist in improving materials coordination within a final assembly factory. Insight
into optimal placement of inventory, failure modes that can occur when implementing point of
use inventory, control mechanisms to keep momentum in the point of use system, and the
integration of point of use inventory with Materials Resource Planning has been provided.
4.1 Point of Use Inventory Placement:
Point Of Use (POU) inventory placement is a complementary element of a Lean assembly
environment. The physical process of obtaining parts to use during the assembly process is a
Type I waste, meaning it is a necessary action but it does not provide direct benefit to the end
customer. Therefore, the time required to perform these tasks must be minimized. Locating
inventory stock directly in the assembly environment removes wasted time associated with
having assemblers retrieve parts from various storage locations.
The benefits of creating point of use inventory are far reaching. First, parts are readily available
to the assemblers on demand for use in assembly. Second, point of use placement provides a
clear visual indication of what parts are in stock and what parts have been ordered in excess
quantities. Third, it creates a visual awareness of parts can often run below minimum level due
to high utilization. This visual control is effective for both the operators as well as material
planners. Although in theory inventory levels are calculated, reality shows that nonlinear
demand patterns often result in utilizing all available inventories. Visual indications direct from
the factory floor can help show how much variation exists between inventory levels listed in the
inventory database and the actual levels stocked, minimizing the time required to find such
discrepancies that often lead to part shortages and line stoppages. Worker communication with
the material planners also provides earlier warnings of upcoming material shortages, both
formally through inventory Kanban replacement strategies (quantity calculations discussed in
Chapter 6) and informally through open communications.
4.2 Failure Modes to Consider for Point of Use Inventory:
Although point of use inventory has been deemed an improvement over central stockroom
control, numerous failure modes must be overcome when placing inventory on the assembly
floor:
.
*
Parts utilized in multiple assembly locations for "Platform" products
Ownership and control of new stocking methodology and materials handling
39
4.2.1 Multiple Use Inventory - Optimized Stocking Locations:
Where should parts be located when used in multiple assemblies? Conceptually, products that
share parts under a "platform" structure are superior, saving engineering design time and
inventory carrying costs by limiting part proliferation. However, control of inventory levels for
such parts becomes difficult when demanded in multiple plant assembly locations. This problem
is magnified when point of use inventory is utilized. Ideally, each assembly cell using a specific
part should have its own parts supply. This potentially leads to two scenarios:
.
Stocking a greater than required level of inventory (if multiple locations are stocked with
the same part)
- Wasted operator motion if one has to retrieve parts from a central bin location.
The problem of multiple use locations needs to be coordinated with all products involved. It can
become a chaotic situation in which multiple groups feed from a common part supply. Results
from improper part inventory planning from one group can easily affect the requirements of
another group using the same part - poor planning leads to part shortages which leads to
unaccounted use of the parts purchased for another group.
To deal with multiple-use parts, the following guidelines should be used to partition point of use
inventory usage (Suri, 1998):
- For parts used in a single, dedicated work area, stock one location.
* For high volume parts that are utilized in more than one assembly, two options must be
considered. One option is to have one point of use parts bins in a shared location where
each line using the parts is compromised by having the operators visit the central POU
location to obtain parts. The second option is to create multiple parts bins locations as
needed in each line. This adds both additional materials handling complexity and
increased database accounting as a trade-off to increased part access. Having one "master"
part bin that feeds the other "slave" part locations provides control in this scenario. The
master bin's inventory level triggers additional supply orders.
* For parts that are only used sporadically by multiple locations, it is best to keep them in
central stock locations and allocate them when needed.
4.2.2 Material Handling Ownership and Control:
Control of point of use inventory must be clearly outlined. It transforms a once strictly
functional operation (independent of assembly process) to one that is integral to flow-based
assembly. Ownership of this process must now be directed into a position that is measured as
part of the overall assembly operations success.
Historically, stock room operations are a functional category with similar divisional problems as
engineering or marketing - their actions are measured based on fulfilling their own department's
objectives. Stockroom operations provide a service to the rest of the facility - providing receipt
and delivery of parts to assembly personnel to be used in the assembly processes. However, this
functional operation may not be in alignment with the objectives of making manufacturing a
responsive system. For instance, time restraints placed on stockroom personnel may not allow
adequate time for them to unpack and stock inventory in point of use parts locations. Once parts
arrive on the factory floor, stocking may be left up to the assembly operators themselves whose
actions are measured on building assemblies and not stocking parts. Therefore, if no one owns
40
the complete delivery process, it can fail to provide the required responsiveness. Rather, it
creates confusion as to who completes the stocking process and when the stocking process
actually is completed throughout any day or week.
It is suggested that the inventory stocking process be positioned as an integral part of
manufacturing. Ownership of the process should be under manufacturing's direct control, with
individuals whom report to manufacturing positioned to be fully responsible for its
implementation. This would provide fuller integration of the requirements for manufacturing,
with individuals working to consistent time-reducing metrics as manufacturing. However,
ownership must not be contained to a single person. A single person represents a single point of
failure. This potential failure mode must be eliminated by cross-training multiple individuals or
a team to ensure continuous inventory management coverage.
4.3 Integrating Point of Use Inventory with the External Supply Chain:
The point of use inventory process only improves internal material handling operations. Greater
material control is possible by extending these boundaries to include external suppliers and
having such suppliers directly control point of use inventory replenishment. This would be
particularly useful for high-volume, low-value parts that are not cost effective for the company to
control through materials planning and ordering.
Direct supplier control would allow the supplier to enter the plant and have direct responsibility
for replenishing, tracking and ordering inventory. Point of use inventory is then used directly by
the supplier as a visual indicator of the replenishment needs. This becomes more of a service
from the supplier to the factory; however, it is a win-win for both sides. From the supplier's
view, he has direct control of what is ordered and when - there are less rush orders or orders
inappropriate to the plant's needs. From the plant's view, they no longer need resources to
control the ordering and stocking of such items.
For this supplier management system to be successful, certain criteria must be met. First, the
suppliers must be geographically located in close proximity to the plant. Second, the
manufacturer supports the mentality of sharing inventory data and part demand patterns with
their vendors. Third, the suppliers are required to adjust their deliveries dynamically to keep
inventories at a minimal level for the parent manufacturing company.
4.4 Point of Use Inventory Management at Instron:
An inventory strategy was implemented at Instron based on 100% Point of Use inventory
placement for Electromechanical and Hardness products. All major parts were relocated from
stockroom locations directly into bins, racks and/or pallets at the perimeter of each assembly
flow line. Each rack location on the floor required marking for inventory tracking and each bin
required labeling to identify parts' numbers and minimum inventory quantities. Workers' input
was critical to determine optimal locations to stock inventory. The final inventory locations on
the floor were determined directly by the assembly operators. Figure 12 shows an example of
point of use inventory placement at Instron.
41
Figure 12: Point of Use Inventory Placement on Instron's Factory Floor
Ownership of the point of use process was established by creating a materials-handler position
that reports directly into the manufacturing division manager. This aligned the incentives of the
material handling personnel with the assembly process to achieve a common goal of assembling
and shipping machines in a timely manner, including the provision of parts as an integral part of
the process. The stocking responsibilities were now known and better managed. Assemblers
have been able to obtain parts from receiving more quickly, reducing the number of line
stoppages. Further, a specific contact person was now available within the department for
solving materials problems, including shortages, rejected parts and parts delivered from
backorder status. This opened the communication channels between assemblers and material
handling to further reduce delays.
Direct vendor control of select inventory was also established. Assembly hardware (nut&bolts)
was set up to be delivered and stocked by a local outside supplier. This removed the requirement
to order and control over 250 hardware items. This vendor arrangement further provided direct
feedback to the vendor and real-time control of inventory levels.
4.5 Materials Resource Planning vs. Pull Inventory Policies:
Traditional control of inventory was accomplished by using Material Resource Planning (MRP)
techniques. This was considered an inventory "Push" system in which material was ordered in
advance of need based on demand forecasts. As the future demand was forecasted, MRP
inventory control adjusted order quantities based on the quantity forecasted and vendor lead
times. Inventory deliveries followed, whether or not actual demand warranted material delivery.
This led to potential overloads of inventory if forecasts were greater than actual demand, or part
shortages if forecasts were below actual demand. In either case, there were costs associated with
pushing inventory - in both lost sales and inflated inventory holdings.
42
Revising inventory supply policy to reflect levels to satisfy actual demand was best
accomplished by pulling inventory into production when needed. The physical set-up of point of
use inventory clearly showed the levels of inventory in real-time. The issue then became how to
use this visual display to better control inventory levels. Creating a control system directly at the
point of stock further reduced information delays as physical inventory was consumed.
Feedback on this consumption was derived from inventory replenishment using a kanban card
process to control inventory replenishment.
How was this accomplished? Each SKU (stock keeping unit) had an associated physical card
attached to its point of use stock location. As shown in Figure 13, the card indicated the part
number and the minimum level of inventory that the stock must be reduced to for triggering a
supply order for that part (Calculating such levels are covered in Chapter 6). Once the minimum
level of inventory in the bin was reached, the card was pulled from the bin and provided to the
materials manager, indicating the need to order another lot of parts.
INVENTORY REORDER CARD
Order Dates:
Part Number
Part Name
Order Quantity
Supplier
Stock Location
MIN BIN OIJANTITY
Figure 13: Example Kanban Inventory Card
The importance of proper card system operation cannot be understated. Timely "pulls" of the
cards from the parts bins must be considered as important as the assembly process itself; without
parts assembly operations are not possible. Again, this was a workforce discipline issue that
required clearly stated objectives and training for those using the process on a daily basis.
4.6 Kanban Inventory Management at Instron:
Consistency in approach was very important in inventory control. The team at Instron set out to
create a consistent and visible inventory ordering and control policy. However, with multiple
methods in existence for various parts based on lead times, vendor requirements, and part type, it
was determined that the best approach would be to start with one part category to create a pilot
inventory ordering process that could eventually be extended to all part categories.
43
The pilot was initiated by targeting one of the largest parts suppliers of wire harnesses, which
was originally controlled by Materials Resource Planning. Wire harness products were
transformed to kanban-card control by calculating minimum levels and order quantities
(explained in Chapter 6), and creating cards for each respective point of use parts bin. In cases
that parts were small enough to place in containers, minimum levels for each part were
segregated by bagging the quantity separately as part of the supplier's process. Upon opening
that particular bag of parts, the kanban card inside facilitated visual indication that the minimum
inventory level had been reached.
Physical set-up of kanban inventory control was easily accomplished; sustaining the inventory
process control was more difficult. The pilot process allowed for learning and controlling
problems with using this method. Once the cards were in place, orders would only be initiated
once the cards were pulled out. Training was required for assemblers and the material handler to
understand the process and to take the time to view the levels when accessing the parts.
Acknowledgement from the assemblers and the material handler that they were in control of this
process as part of their daily routine was necessary.
Kanban inventory control was not foolproof. One problem was the dependence on the order
cards. They are physical objects that control the order process. Over time, instances occurred
when a card(s) was misplaced or ignored, leaving inventory short. Another problem was the
ingrained feeling of security tied into material resource planning. These failure points had to be
recognized and driven out over time by commitment to the kanban method by material planners
and the assembly operators.
4.7 Combining Kanban and Material Resource Planning Processes-Mixed Model Solution:
Although it was originally proposed that Instron would move all Electromechanical parts
inventory to full kanban control, some parts did not lend themselves to this demand control.
Kanbans worked well for items that have reasonable lead times - zero to four weeks were
generally acceptable. This was evidenced from the Toyota Production System, which used the
principle of retaining local suppliers that can deliver frequently and in short time. However,
local suppliers and short lead times were not always achievable in the short term. If lead times
were longer than approximately four weeks, kanbans did not work so well given the quantity of
demand in this environment. First, they required large amounts of inventory coverage for the
extended lead-time. Second, long lead-time parts were often special orders (such as foreignsupplied or custom processed), from suppliers who currently did not build to short order and
small lot sizes. Third, the effects of demand variations increased with longer lead times, calling
for greater amounts of safety stock inventory.
This led to the question; can Materials Resource Planning and Kanban processes be combined
effectively? The answer was yes, but with great caution. Operating with two methods went
against having a purely consistent pull system. It sometimes resulted in confusion on the
assembly floor when all parts were not brought in on demand. Kanban would be the dominant
inventory methodology utilized, with MRP-driven inventory used for select components. The
supporting MRP system did provide a superior planning tool for long lead times, and it did
provide accurate tracking of needs for future forecasted demands. However, since it could not
44
control future demands, its use was restricted to those parts requiring its long range planning
capabilities to keep inventory levels at the correct levels.
To remain in line with as much kanban process as possible, kanban cards could be placed in the
point of use locations for material resource planned parts. Although these specific cards would
not drive inventory orders directly, they would emulate the process for assemblers to monitor
inventory levels on the floor and they would provide material planners additional data of actual
demand vs. planned use, given a minimum level is set for these parts. Further, long term process
planning should include reducing lead times and selecting more local vendors, creating an
environment that would move such MRP-controlled parts to the intended kanban process.
With inventory placement reorganized and inventory kanban control established for the majority
of parts, the physical environment was in place to implement the proposed assembly process. It
was now possible to make the whole process "flow". These most critical aspects of
implementing this flow process and inventory management methods are explained in the
chapters that follow.
45
46
~5
IMPLEMENTATION OF A SINGLE PIECE FLOW
ASSEMBLY PROCESS
As stated earlier, a manufacturing process and its physical environment must complement each
other. Given that the parameters to establish the physical environment have been completed, this
chapter described the challenges faced when actually implementing a flow process within a low
volume assembly environment.
Womack and Jones have elegantly phrased flow implementation:
"Once value has been precisely specified, the value stream for a specific product fully
mapped by the lean enterprise, and obviously wasteful steps eliminated, it's time for the next
step in lean thinking - a truly breathtaking one: make the remaining, value-creating steps
flow." (Womack and Jones, 1996)
5.1 Process Flow Definitions:
A major goal of this process improvement was to shorten the required assembly flow time, with
reductions in such time translating to the opportunity for increased order responsiveness. Before
going further into examining the process calculations and implementation, it was important to
define flow time, cycle time and takt time (Schonberger, 1986 and Suri, 1998).
Productionflow time was defined as the total elapsed that it took to produce one unit, from the
start of the first subassembly to the time the completed unit was shipped. This included the
length of active time for each operation plus the amount of waiting or inactive time between each
activity. A synonym used for flow time was throughput time.
Production cycle time was defined as the elapsed time between consecutive product completions.
This was considered the heartbeat of production. It controlled the timing for the entire work
center and was thought of as the time between start of assembly or the time between shipments,
in units/time. This led to the calculation of the system's required takt time.
Takt Time, in time/unit, was defined as the time required to perform each operation (time per
station) to achieve the desired cycle time based on the customer demand rate:
Takt Time = (Available time per shift * Uptime factor)/Average demand per shift
Average demand per shift = average monthly demand/((# days per month)*(# shifts per day)
Uptime factor = %of time during shift that work is actively performed
Average monthly demand = Average number of parts/products required each month
For example, with 8 hours per shift, 80% uptime per shift, 100 parts per month demand, and 1
shift operation, the takt time for each part was calculated as:
47
Takt Time = 8hrs/shift * .80 / (100 parts/month)/ ((20 days/1 month)* 1 shift/day)) = 1.3 hrs/part
For each elapsed duration of 1.3 hours, one part was completed and each process step therefore
finished one task within 1.3 hours to supply to the next respective step.
5.2 Process Implementation at Instron:
Implementing the process as proposed earlier required clear definition and structure, accurate
capacity calculations, and involvement from the entire workforce. The operators particularly
required the process knowledge, leading to understanding what quantity and type of assemblies
to build at any time with the ability to do so in an increasingly self-directed manner. For Instron,
the process that was proposed resulted in single piece flow of assembled product with a specified
daily output quantity to match customer demand. This can be contrasted to the previous output
that was measured in weeks and months. Implementation structure included four main elements:
calculating demand quantities, level-loading production, creating strategically placed kanbans,
and establishing decision rules that governed the daily work practices.
5.2.1 Capacity Analysis
Capacity requirements for each major product were used to calculate the required number of
stations per line and number of assembly operators. These requirements were established by
combining demand with the required assembly/test times. Establishing these times was not
trivial. Although standard times were utilized elsewhere in labor reporting, it was not clear if
they were accurate; they had not been recently updated to reflect learning cycles that could
potentially reduce times over those originally recorded, nor have they been updated to reflect
product design refinements. Therefore the methodology to establish accurate assembly and test
times was to collect data directly from the assembly operations.
Data was collected directly from the assembly operators using prepared time sheets that were
attached to each product assembled for a six-month period. Each operator provided information
including initial start date of the product, assembly and test time durations for each process step,
the additional time required due to non-assignable problems, a short description of these
problems, and the completion date. Refer to Appendix A for an example of this time sheet.
Information available from this data included:
1.
2.
3.
4.
5.
Number of average throughput days from assembly start to final product
Variation in number of throughput days
Active operator time (in hours) required at each assembly and test process step
Variation in active time required for each assembly and test process step
Percentage of time required for non-assignable problems
These values were combined with the required weekly customer demand quantities to determine
the staffing needs for each assembly process. In addition, calculation of each subassembly time
allowed bottlenecks in the operation to be identified that led to optimizing the subassembly
kanban placement strategy. Last, the data was used to demonstrate the baseline value-added time
48
for each machine during the process. The results of the data collection were organized into a
spreadsheet-based planning tool for dynamically calculating the cells' staffing requirements
depending on the output demand per week to be used for present and future line capacity
calculations. This spreadsheet has been outlined in Appendix B.
System time requirements for one example Electromechanical product line were summarized in
Table 2. Using these time requirements, an example capacity calculation from the planning tool
has been presented in Table 3. Inputs included number of product demanded, time per product,
available number of shifts and hours per shift, the line uptime factor, and the amount of overtime
authorized per time period per worker. Output included takt time, number of operators required
per product line, and the minimum number of stations needed to support takt time (equal to
number of operators assuming one operator controls one station at minimum.)
Test
Finish
Audit
Total Time
Tray
Top End
Integration
Mean (hrs)
2.20
1.44
1.00
3.48
0.93
1.45
10.50
Deviation (hrs)
Standard
Time (hrs)
0.26
0.50
0.10
0.80
0.19
0.28
1.99
2.00
1.50
0.70
3.30
0.50
1.50
9.50
Procedure
Per Machine
Table 2: Example System Assembly/Test Times From The Electromechanical Product Line
Total Hours Per Machine
Average Week Demand
Total Hours Required
Available Hours Per Shift
Number of Hours Authorized for
Overtime per Person
Uptime Factor
Number of Shifts per Day
Number of Days Per Week
Calculated Takt Time
(hrs/unit/station)
Number of Operators
(Minimum # Stations)
10.50
10
105.00
8
0
.875
1
5
3.50
3.0
Table 3: Example Calculation Results for Takt Time and # Operators
For Sample Electromechanical Product Line
49
These calculations established the baseline capacity requirements based on average test and
assembly times. However, a problem arose in using these average numbers. Variations within
assembly and test times was inherent in the given product line and its various models. These
variations were attributed to both "assignable" and "non-assignable" causes. Machine models
and accompanying accessories that require additional time for both assembly and testing due to
specific model complexity were "assignable" causes of variation. "Non-assignable" causes
included problems encountered during assembly and test that require additional time to diagnose
and correct. Combined, these variations often resulted in wide distributions of total required
time. The distribution of test times realized for one Electromechanical model has been outlined
in Figure 14 as one example of the variation that existed in the process
0
.E-
15-~
0105
0
4
N
b
'1'
,
~( <0q
Hours to Test
Figure 14: Test Times Distribution for Sample Electromechanical Product Line
This test time data shown in Figure 14 included both assignable and non-assignable causes.
It
was true that the problems resulting from non-assignable causes have to be addressed and
corrected over time. However, the assignable portion of variation cannot totally be removed,
which required the process to be designed with flexibility to account for limited variations. The
next implementation segments, including level-loading the assembly schedule, controlling WIP
and output through kanban placement, and establishing decision rules, provided control while
accounting for variation.
5.2.2 Level Loading the Assembly Schedule
To maintain overall average production times (excluding non-assignable problem times) in the
process with assembly time variability between models, the weekly production schedule was
leveled by sequencing the order of models built by total required assembly/test time. This was
best demonstrated through an example (using sample time variations):
50
Model A: Test Time 3 hours
Model B: Test Time 4 hours
Model C: Test Time 5 hours
Given a demand of three for each model, there were three most likely assembly scenarios that
would have resulted. First, machines were built in ascending test time requirements
(AAABBBCCC). Second, machines were built in descending test time requirements
(CCCBBBAAA). Third, machines were built in a leveled manner (ABCABCABC). The third
approach was most appropriate to reinforce our process of smoothing production flow. It
provided an average time requirement of 4 hours that is repeated 3 times. Output to shipping
was consistent per each set of three machines.
A similar system was implemented at Instron. The list of weekly orders for each respective
product family was first sorted from highest to lowest total dollar value. The order dollar value
exhibited high correlation with the model complexity within a machine family and with the
number of accessories ordered, both which required additional system assembly and test time.
These orders were then ranked in alternating order, assembling one high dollar value system then
one lower dollar value system. This provided a more leveled production process during
assembly over multiple orders.
5.2.3 Pull Production, Assembly Kanbans and Strategically Placed Work In Process
A description of the production flow technique that was established can be simply described as
"Build to the Hole." Assembly was triggered from the end of the line forward, to create a "pull"
activity starting with the end operation. As product was completed in any one station, the action
signaled the preceding workstation to complete another assembly for that station to "fill" the hole
that was created by removing finished product. This utilized the material from upstream
Kanbans setting the chain of production activity in place. This process continued through all
other upstream stations - when subassemblies were removed from the area, they were
replenished from material in the upstream station. Actions (categorized as either assembly or
testing) were triggered by demand from the next downstream workstation, where demand from
the end of the line drove the actions through all earlier stations.
This "pull" production technique has been demonstrated in Figure 15. The process started at the
end of the line (Step 1) with material flowing out to shipping. For each material flow there was a
corresponding information flow that was opposite in direction, which led back to earlier stations
to signal where material was needed.
51
Parts
11
Material
Flow
Subassembly
Kanbans
Material
Flow
Final Assembly
Run-in Kanbans
STEP 3
Material
Flow
STEP 2
Final System
Test
and
Finish
~
[ ~i
Material
Flow
Ship
Unit
STEP 1
~El..
.
ad
Information
.......
Flow =
Information
Flow =
Assemble
Subs to Fill
the Holes
Assemble
Unit to Fill
the Hole
Information
Flow =
Test and
Finish
Another
Machine
Figure 15: Pull Production Technique Which Shows Process Steps and
Opposing Flows of Material and Information
What controlled this type of system within Instron? The system was triggered by a system of
subassembly Kanbans. Kanban by definition means "production card." Cards for this low
volume application were made from 4"x3" plastic-coated clip-on tags. They were attached to the
front shelves of wire racks strategically located in the physical production area as subassembly
work in process (WIP) staging locations. A tag represented a kanban location on the wire shelf
to be filled with an identified subassembly - the number of tags present indicated the number of
subassemblies required to fill the work in process staging to a desired level. Using removable
cards allowed easy modification of the amount of subassemblies in WIP as demands and learning
change. Refer to Figure 16 for an example of a tray subassembly Kanban rack.
52
Figure 16: Electromechanical Kanban Rack Showing Work In Process Staging in
Quantities that Correspond to the Number of Kanban Tags
5.2.3.1 Kanban Quantity Calculation:
The minimum quantity of kanban tickets to display per staging location was calculated as
follows based on demand requirements (adapted from Nahmius, 1997):
KB = ROUNDUP [D * TT * (1+SS)]
where: KB = Number of kanbans
D = Average demand of kanban stock (parts/unit time)
TT = Takt time of process stage (hrs)
SS = Safety stock fraction (dimensionless)
For a low volume environment, it is most appropriate to use parameters that are measured in
weekly demands and hours or days of throughput time since many processes in low volume
environments require hours or days to complete. As an example, the number of kanban tickets
required for EM base trays is calculated as:
D = 20 units per week
TT = 2.7 hours
SS = 10%
53
KB = ROUNDUP [10 units/week * 1 week/35 hrs * 2.7 hours/unit * (1+. 10)] = 1 kanban unit
Initially, kanban levels were set higher than calculated to ensure subassemblies' availability
while the process was introduced. Operators needed to be given time to learn control within the
process and control the resulting work in process. After such learning had occurred, the number
of assemblies staged in kanbans would be reduced over time to the calculated number by
changing the quantity of tags presented on the wire racks.
5.2.3.2 Kanban Locations for Strategic Work in Process (WIP) Placement:
It also had to be determined where these Kanban staging racks were to be placed within the
process. In low volume multistage serial flow assembly environments where variation is reduced
but is still inevitably present, Kanbans are used for three reasons:
1. To create strategic locations of WIP to buffer against production time variations
2. To reduce the frequency of starvation of downstream stages of assembly
3. To limit the amount of WIP that is built up between process steps
Although a principle lean manufacturing technique was to remove interruptions in the steady
state process, a certain level of variation will always exists in this scenario as described earlier.
It was established that a controlled volume of strategically placed WIP buffers would increase
overall flexibility of this production system to better maintain a consistent flow quantity
(Burman et al, 1998). At Instron, this required critical review of the process steps to determine
the optimum kanban placement. It was not optimal to assume Kanban placement at every
process step. This would have resulted in excess WIP and too many control points. Kanban
placement was chosen for three strategic locations, as shown in Figure 17:
1. Frame Run-In
2. Base Tray Assembly
3. Electronics Assembly
54
Electronic
Assembly
A
MK
Base
Tray
Assembly
A
N
Load Cells
and
Accessories
N
Tray and
Top End
Integration
Assembly
Frame
Run-in
Cycle
Frame
Load Cell
Calibration
Complete
System
Assembly
External
T Housings
Audit and
Inventory
Update
Ship
Pr
et
Top End
Column
Assembly
Figure 17: Process Showing Kanban Placement Locations
System Run-In: Kanbans at run-in provided a visual indication to the number of machines
required from assembly each day to be staged for test and ship the following day. To provide
flow, testing required consistent product volumes to be processed through the overnight run-in
cycle every day. If machines were not prepared for run-in one day, the limitation would carry to
the next day since testing machines could not be completed. The run-in kanban provided visual
indication and limitation to that daily requirement.
Base Tray Assembly: Kanbans at base tray assembly were used as a buffer against variations in
test time. Part of the new process included having operators become responsible for both
assembling and testing complete systems, creating a more flexible workforce where operators
would flex between assembly and test stations. However, with flexibility came coordination
problems. For instance, potential testing difficulties led to additional test hours for a given
machine downstream of assembly, utilizing capacity that would have otherwise been rotated to
the front of the line to complete assemblies for the next day. Base tray kanbans were filled when
operations were on schedule and time during normal operations was available. When extended
time was needed further down the line to test product, time to build trays for the next day was
absorbed by temporarily depleting the kanban. This prevented stalling the front of the line.
Kanbans therefore provided both a time buffer to allow output to remain stable and a visual limit
mechanism to control subassembly WIP during normal operations.
55
Common Assembly: Kanbans for small common assemblies used in multiple products provided
additional support from each individual line. The assemblies were simply assembled by flexible
workers to capacities calculated based on total demands. Again, this increased the flexibility of
each line by not having to use time to build these small assemblies, and further controlled the
amount of work in process for common assemblies between the three lines.
Assembling to kanbans in this environment also required coordination to the specific product
orders. Custom system configurations existed, again due to model variations, which influenced
the required subassembly configurations. Because of these configurations, generic assemblies
could not always be built to stage in Kanbans. Sequencing the building of subassemblies within
the Kanbans was therefore required according to specific orders in the leveled build schedule.
The operators began the assembly process for any machine by extracting the customer order data
on a printed sheet, which included the configuration information. The sheet remained with the
assembly throughout its time in manufacturing, including when staged in Kanban locations. This
clarified the model of subassembly in the kanban and provided visual indication as to what
assemblies to produce in downstream stations.
5.2.4 Decision Rules Govern Work Process:
Kanban indicators showed the type and quantity of assemblies to build, but they did not convey
the daily work structure. Along with the visual Kanban indicators, complementary decision rules
were created and applied to the process to guide the operators in making daily decisions on what
to complete throughout a given shift. Indeed, the Kanban "holes" showed the need, but those
needs also had to be prioritized when workers were expected to service multiple process steps.
Having a limited number of rules to govern the process allowed the operators to decide the
specific activities at any given time but still remain within bounds of a process that ensured that
daily quantity requirements were consistently met. As an example, the following decision rules
were set for the EM and Hardness assembly process at Instron:
1. Each day, first test the required # of machines to ship that day. Complete testing and
finishing to allow product shipment.
2. Use subassemblies staged in kanbans to complete fully assembled systems to refill the
run-in cycling kanban.
3. Use remaining time to refill subassembly kanbans.
Rule number one ensured that the first actions of the day were focused on the shipment of
product. This also worked to deplete the Kanban of machines that were run-in the previous night
and reset the visual indicator that forced assembly to "Fill the Hole" at the run-in Kanban
location upstream of testing by the day's end. Rules number two and three created orderly
backfilling of kanbans in order of importance to get the next product completed. These decision
rules were closely integrated with the concept of pull production and kanban control, again to
move material down the line and information back up the line.
Starting at the end of an assembly process was found to be counter-intuitive to some operators
and managers. The original daily routine was often started by building subassemblies, followed
56
by testing the product at undefined times during the day. Often, machines were queued waiting
to be tested; other times no machines were available to be tested on any given day because of
assembly difficulties earlier in the process, leading to zero shipments that day. In the original
process, operators often complained that there was not enough time to complete the work and
there was not enough floor space to handle the work in process. This further created chaos on
the floor since it became difficult to control many random stages of production.
Completing the testing and finishing first, followed by assembly to refill the assembly kanban
locations, ensured a consistent quantity of product shipped every day. This also set the daily line
pace and allows management to more easily visualize the production status at any given time of
each day.
The question arose, why complete the testing first? Why not finish, audit, or assemble first?
Referring to Table 4 for system time requirements, the system bottleneck was the testing
operation (Goldratt, 1992). This was both the most time-consuming sub-process and the one
with the greatest variation. Testing first every day ensured time to satisfy the process bottleneck.
Tray
Top
Integration
Test
Finish
Audit
Total
1.00
10
0.10
3.48
0.93
9
0.19
1.45
14
0.28
10.50
100
NA
End
Mean Time (hr)
% Time
St. Dev. (hr)
2.20
21
0.26
1.55
15
0.50
33
0.80
Table 4: Time Requirements per Process Step for
Electromechanical System Assembly/Test
From Table 4, the testing time requires the greatest concentration of labor capacity, and also
showed the greatest labor capacity variation. Given this variation and the process in which
workers moved from station to station between assembly and test, testing any one model often
consumed labor capacity that would normally be used within assembly. However, once
problems arose in testing, the tray kanbans acted as time buffers so that assembly did not
immediately have to rely on the labor capacity from those workers who were testing.
Table 5 further exemplified the need for WIP buffers in assembly. Given a capacity to produce
two machines per day on one line and labor equal to the average time requirements, it required 3
operators on average. What happened if the "average" was extended due to additional testing?
57
Average Times for Assembly/Test per Day
# Machines/day
Total average time required
Time/day/operator
# Operators
Total time available
Extended Times - Nonassignable Problems
Total average time required
2*St Devs of test time on each machine
Total time required with one machine over average
Time available
Time to buffer in building trays for kanbans
Hrs
2
21
7
3
21
21
1.2
22.2
21
1.2 minimum
Table 5: Example of Extended Time Requirements Given Nonassignable Problems
As shown, an additional 1.2 hours was required within the daily assembly to maintain output of
two machines. This 1.2 hours was buffered into the kanban WIP by having a calculated number
of base trays and small assemblies ready to be consumed.
In summary, all four concepts of capacity management, level scheduling, kanban creation &
placement, and daily work decision rules were designed to work as one complementary system.
With this assembly process in place, the next concern was inventory management to ensure
consistent part supply into the production process. Inventory stock levels required alignment
with the process demands. The next chapter outlined a methodology for inventory control to
align inventory levels with this newly improved manufacturing "pull" process.
58
/
IU /
ALIGNMENT OF INVENTORY AND MANUFACTURING PROCESSES
A chosen manufacturing strategy strongly influences the quantity and type of inventory a
manufacturer carries. The selected manufacturing process (assemble to stock, assemble to order,
build to order) as well as the assembly methods utilized (manual or automated processes) and
process metrics, each have to be identified before calculating inventory quantities. Inventory
policy for an assemble-to-order system that carries purchased parts and subassemblies as
inventory was reviewed and improved through the analysis outlined in this chapter.
The uniqueness of cyclical production demands and how such demands influenced the supply
chain of incoming material was strongly considered in this analysis. Two phases of inventory
control were developed. The first phase established a process to achieve minimum stock
quantities and lot sizes to adequately supply the existing assemble-to-order process with its
cyclical demands. The second demonstrated how inventory levels could be reduced when
inventory management is coordinated with lean manufacturing process management.
6.1 Setting Proper Inventory Control Measures - The Hidden Costs of Independent
Metrics:
Effects of proper inventory control extend beyond internal company boundaries. How a
company controls its inventory affects its ability to satisfy customer demands as well as its
vendor relations. If based on the wrong metric, inventory policy can work against lean
operations in unforeseen ways.
Traditionally, the trend in inventory policy has been to continually reduce inventory levels,
constantly monitored by measuring the number of inventory turns realized per year. It is true
that increasing turns leads to improved cash flow. However, continuing to drive down inventory
levels to achieve higher turns without regard to determining the appropriate level of inventory to
fulfill manufacturing demand often leads to process problems. Unforeseen chaos can occur
when increasing inventory turns are not coordinated with suppliers, who are unable to supply
with increasing shipment frequency given their own capacities and shop metrics. Thus, the
question arises, "How much inventory should be carried?" "As little as possible" is not always
the right answer.
Alignment of inventory levels with manufacturing's assembly metrics is first required.
Manufacturing was primarily measured by "On-time" customer shipments with a secondary
measurement of product throughput time to shipping. Parts to complete assembly must be
readily available in the factory; otherwise flow times increases as assembly is stalled waiting for
parts to arrive. Part shortages are therefore a major concern. One reason for the occurrence of
shortages is that inventory is often "leaned out" too far to support the ongoing assembly process
and its given cyclical variations. It is true that these variations ultimately need to be reduced, but
they exist in the short term, and must be carefully managed to provide desired output.
59
Minimum inventory threshold levels must be established. They must be aligned and coordinated
with both the internal manufacturing requirements and the external suppliers' capacities. The
supplier must be considered an extension of the parent manufacturer, with consideration of the
suppliers' capacities in setting supply lot sizes and inventory delivery frequencies. Otherwise,
the supplier is forced to either hold large amounts of inventory or continuously try to "catch up"
with the needs of the manufacturer while falling further and further behind in his own production
schedule.
A vendor supply is bounded by the agreed upon delivery lead time. Once a lot is pulled from the
vendor's supply, that vendor must be given the full replenishment lead time before another lot is
pulled. If material is demanded before the vendor replenishes his own supply, chaos at both the
vendor and manufacturer can ensue. On the supply side, an "inventory pull" before its time sets
up an "effective" longer lead time felt by the manufacturer, since the lead time for the new order
will include the time until the existing order is completed plus the full lead time for the new
order. At the parent manufacturing site, stockout situations will likely occur since any safety
stock may be inadequate to cover this longer effective lead time. This often follows by forcing
abrupt manual intervention from material planners to try to shorten the supplier's lead times for
that order.
The problems with "pulling" inventory from suppliers more frequently than they can provide to
achieve a high number of turns is demonstrated in Figure 18. This shows inventory stock levels
at the parent manufacturer over six time periods. For simplicity, it assumes lead time (LT) is the
agreed upon lead-time of the supplier, equal to one time period, and the duration of time for one
cyclical demand cycle to be completed is equal to 3 lead time periods. Further, the supplier can
instantly replenish at the end of any lead time period. It also assumes that lot sizes cannot be
changed on every order and suppliers cannot provide partial shipments. Given existing low
replenishment inventory lot sizes to provide high number of turns and low minimum inventory
levels, periods at the end of a time quarter with higher manufacturing demands start to
experience stockout conditions. Since lot sizes cannot be instantly increased (a reasonable
assumption since suppliers need time to react to changes), the manufacturer falls further and
further behind through the quarter. These stock outs can occur at the end of every quarter,
leading to missed shipments and lost revenues for the parent manufacturer. Further, they also
lead to panic ordering, ordering from other vendors, scavenging for parts, and resultant fire
fighting.
60
INVENTORY
QUANTITY
INVENTORY
DEMAND
PATTERN
LEAD TIME
DEMAND+SS
QUANTITY
---
STOCKOUTS
STOCKOUTS
EXISTING
LOT
SIZE
--
EXISTING
MIN
QUANTITY
0-
--....--- --
--
-
- -
.
.-
.
-
LT1
LT2
LT3
LT4
LT5
LT6
LT=LEAD TIME PER LOT OF INVENTORY
Figure 18: Original "Low as Possible" Inventory Levels Showing the Potential of Stockouts
Figure 19 shows how alignment of the parent manufacturer's demand and vendor supply leadtime, through calculating lot sizes and minimum inventory quantities, leads to a more stable
process with little or no stock-outs occurring. The inventory is now carried to satisfy demands
and be within bounds of the suppliers' lead time. As seen, because demand does not utilize the
full lot sizes every time period, there are some times in which lots are not pulled as frequently as
one LT period, shown as excess time periods beyond the LT duration. However, when the
higher demand months arrive, the calculated lot sizes statistically satisfy the demand.
INVENTORY
INVENTORY
QUANTITY
DEMAND
PA
LEAD TIME
DEMAND+SS
QUANTITY
CALCULATED
LOTSIZE
RN
-
CALCULATED
MIN
QUANTITY
- --------
0
-------
LT1
-
LT2
LT3
--
-
LT4
L
TIME BEYOND LEAD TIME THAT
INVENTORY IS AVAILABLE
LT=LEAD TIME PER LOT OF INVENTORY
Figure 19: Inventory Levels Calculated To Statistically Satisfy Demand
and Remove Stockout Conditions
61
LT6
Incorporating the qualitative concepts of inventory management discussed above, the appropriate
lot sizes and minimum levels to achieve balanced inventory control can now be calculated.
These calculations have been outlined in the next section.
6.2 Inventory Management Calculations:
When setting inventory levels, three values have to be determined:
1. The frequency of reviewing inventory levels
2. The minimum level of inventory at which time replenishment inventory is ordered
3. The quantity of individual inventory items to order
6.2.1 Frequency of Inventory Review:
Inventory control is most responsive when reviewed on a continuous basis. This removes all
delays between the time inventory reaches a minimum level and the time that level is reviewed.
Responsibility for this review needs to be established with those who are in contact with the
inventory most frequently, namely manufacturing operators and materials handling personnel.
Further, the importance of proper and timely review needs to be enforced to establish a
procedure that is clearly understood and routinely performed by all personnel. The consequences
of not regularly reviewing inventory levels results in potential inventory stock outs. Therefore
the importance of inventory reviews must be clearly understood.
How should reviews be completed? Each stock location contains a segregated minimum amount
of part inventory and a corresponding stock "pull" Kanban card on which is written the
minimum bin reserve quantity for that part. The Kanban cards act as trigger mechanisms for
stock replenishment. As the segregated minimum inventory quantity is reached, the card is
pulled from the stock location, triggering the purchasing department to order another lot of parts.
Continuously reviewing and ordering inventory when needed versus periodic reviews of
inventory levels minimizes stockout conditions where minimum levels are exceeded due to time
lags between review periods. Further, shorter review times leads to less required safety stock (as
calculated below) because there is less effective "lead time," leading to overall lower required
inventory levels.
6.2.2 Determining the Minimum Reorder Points (ROP):
Bin minimum level is determined by the parts' average usage demand, variations in demand, and
vendor resupply lead times. This minimum reorder point (ROP) is the sum of average demand
over the lead-time (DOLT) plus a level of safety stock (SS) to protect against stockouts that
occur from demand variations.
ROP=DOLT + SS
62
The two components of the ROP need to be calculated separately. Average demand over lead
time (DOLT) is calculated by multiplying the demand per given time unit (m) times the supplier
lead time (LT):
DOLT = (i)
* (LT)
DOLT is the average manufacturing demand over the vendor's resupply lead time. Demand per
given time can be either required forecasted demand or historically calculated demand. The lead
time is the time it takes a vendor to supply the manufacturer with a new lot of materials. If a
vendor builds inventory to stock, then LT is simply the time required to order and ship product
from the vendor to the parent manufacturer. If a vendor manufactures the lot during this lead
time, then LT is defined as the time it takes the vendor to manufacturer the product plus the time
for ordering and shipping.
The DOLT calculation does not consider variations in demand patterns. Such variation could
increase demand to a point that outstrips the supply. To cover such increases in demand through
a given lead time period, safety stock must be added to the average inventory level to buffer
against variations.
Safety Stock (SS) is based on the statistical probability that demand could be higher than
average. It incorporates the standard deviation of demand over the chosen time period and the
chosen probability that the part will remain in stock over the lead time. The stocking probability
is typically between values of 95% and 99% depending on product. This probability is then
translated into a z-statistic value corresponding to a normal distribution at the given probability
level (Vining, 1998). Caution must be used when setting this probability. Setting it too high
(100%) guarantees greater material availability but also increases inventory levels dramatically
based the extreme tails of a normal distribution curve.
The safety stock is calculated from the square root of demand variance (i.e. standard deviation T)
times the square root of the given lead time as a number (z) of standard deviations of demand.
SS = a * z * (LT)
2
The resulting Reorder Point (ROP) value provides a calculated amount of buffered inventory to
ensure stock is available to cover the full probabilistic demand over the supplier's lead time.
6.2.3 Lot Size Order Quantities: Should EOQ Theory Be Used?
The final number to calculate is the order quantity. Once the minimum inventory quantity is
reached, what quantity should be reordered? In theory, this quantity can be determined by using
the Economic Order Quantity (EOQ) method. EOQ balances individual order costs with
inventory holding costs to determine the optimum lot order size that minimizes aggregate
corporate costs of buying and holding inventory.
63
EOQ = (2 * C *D / h)"
In the EOQ formula, "C" is the order cost, "D" is the annual demand, and "h" is the holding cost
(calculated by multiplying standard cost of an inventory item by a holding cost percentage).
What are the realities of using this calculation? For Instron, multiple functional departments
agreed that inventory holding cost per year is 25%. However, a single value for order cost was
not so easy to establish. Ranges of values with up to 100% variation ($40 to $80) were provided
by various Instron departments. The value of order cost has a significant impact on the quantity
of inventory ordered at one time, leading to variations in the number of inventory turns and
dollar value of average inventory held.
Conducting a sensitivity analysis on representative inventory clarifies the variations in inventory
levels that result from changes in the order cost. Using the highest cost and volume SKU from a
representative Instron product Bill of Materials, Figure 20 shows the order quantity variations
that result from using an EOQ calculation when the order cost is varied from $40 to $80 per
order.
24.00
E 22.00
20.00
18.00
0
1600
C
14.00
I
12.00
40
50
60
70
80
Order Cost ($)
Figure 20: EOQ Inventory Sensitivity Analysis Demonstrating Change in
Part Order Quantity When Order Cost is Varied
For this single part, the increase in order cost relates directly to increases in inventory valties. In
this example, inventory would be increased on average by almost $1500 for a part valued at
nearly $500/unit when order costs are increased from $40 to $80.
Combined with an alternate method of adding overhead order costs as burden rates to every
piece of material, choosing one value for individual order costs is not often realistic. This leads
one to question the use of the EOQ formula as a method for calculating lot sizes. Further, EOQ
64
theory does not consider the dynamics of a responsive assembly process nor does it consider the
qualitative measures of varying inventory levels. EOQ-generated lot sizes fail to quantify the
following metrics (Suri, 1998):
1.
Cost of poor quality. Large order quantities that save on order cost may increase the
number of quality defects purchased in each lot per given unit of time. For instance, if a
machine operation causes part flaws and the lot of flawed parts is shipped within a large
order quantity, time is wasted to both produce that large lot of flawed parts and to work down
stored inventory to discover those flaws.
2. Cost of obsolescence. Design changes often call for changes to ordered parts to comply with
updated designs. Parts ordered in large quantities could remain on the shelf long enough to
become obsolete in design or standard, calling for additional rework or scrap costs.
3. Cost of long order lead times. Placing large orders to save order costs may cost more in
time since large orders may have extended lead times, potentially causing greater variations
and order fulfillment problems. This can result in an upward lead time spiral by having even
larger quantities ordered in the future to satisfy the demands over ever-longer lead times.
4. Market value of responsiveness. Sales may be connected to when a customer can receive
the finished product. Long lead times may therefore deter customers. Short lead times
means the product is more readily available and can be attractive to customers. Smaller lot
sizes may help in obtaining parts in less time to fulfill such orders.
Overall, the Economic Order Quantity (EOQ) theory does not incorporate critical considerations
that can lead to higher overall costs than those considered in the initial order process. Due to
these issues, EOQ is not recommended to determine order lot sizes. The question then arises as
to how lot sizes should be generated. Based on experiences at Instron with the desire to maintain
the inventory turns metric yet establish acceptable limits based on demand patterns, it was
determined that material planners' intervention and vendor involvement, combined with
minimum ROP calculations, were the best sources of knowledge to arrive at acceptable lot sizes.
Assuming vendors do not hold finished goods inventory, bounds of lot size calculation are first
established from the minimum reorder quantity based on the suppliers full manufacturing leadtime. For instance, if a supplier requires four weeks to produce a lot of parts plus one week for
shipping, then the minimum order quantity is the DOLT + SS based on five full weeks of lead
time. The vendors' capacities and agreements to hold inventory further influence lot sizes.
Shorter lead times, and therefore smaller lot sizes, are possible if vendor agreements include risk
sharing to hold some inventory for immediate delivery. Further, it may be desired to produce
extremely small lots very frequently. This removes the cost of holding inventory from both the
parent company and vendor. However, the vendors' internal capacities may call for larger than
desired lot sized to be produced, leading to a need for either party to hold the inventory. Last,
internal manufacturing and purchasing time capacities to place, track, receive, and stock
quantities of orders also influence the actual order quantities. It may be true that parts can be
ordered very frequently in low quantities from the vendor, but a given receiving capacity at the
parent manufacturer may not be able to handle the high frequency of incoming small-quantity
orders.
65
6.3 Proper Inventory Level for Instron Electromechanical Production:
For Instron EM, setting proper minimum levels and order quantities was initiated by a divide and
conquer technique between part categories. To demonstrate the inventory management pull
process, one category of wire harness assemblies from a single supplier was used. As part of this
initial work, an inventory model was created to assist in completing the calculations for
statistical demand quantities and reorder points outlined earlier. The model has been included in
Appendix C.
6.3.1 Inventory Classified According to Distribution By Value Calculations:
Every part of Instron's assemblies did not have to be controlled with equal effort to provide
overall inventory management. Using the collective inventory of wire harnesses as a single
example, there were over 400 wire harness inventory items to consider. Controlling such large
numbers of individual items became unmanageable. Distribution By Value (DBV) was the
method used to rank the highest value inventory items to be managed with the greatest scrutiny
and highest frequency, which provided the greatest overall cost savings.
The Distribution by Value method was used on the sample wire harness inventory by first
multiplying the standard cost of each inventory item under analysis with its annual usage. A
Cost-Volume (CV) value resulted from this calculation for each inventory item. The list of
inventory items was then sorted in descending order of the Cost-Volume value. Graphing the
cumulative total of Cost-Volume values vs. the cumulative total number of items led to the
results shown in Figure 21. Refer to Appendix C for the representative spreadsheet calculations
that demonstrated the Distribution by Value method in greater detail.
A
B
C
1.10-
E 1
00 0.90
S0.40
S0.30
0.20
0 010
0.0
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Cumulative Percentage of Analyzed Inventory Items
1.0
Figure 21: Distribution by Value Results for Wire Harness Inventory Items That Shows
Three Groups of Items A,B,C Distinguished by Cost-Volume Values
66
As shown in Figure 21, the first 20% of wire harness parts contributed 65% to the total CostVolume value, with 50% of the parts contributing over 90% of the total Cost-Volume. (Note
these percentage cutoff values were subjectively chosen by the author and can be chosen to
represent any similar range of inventory). After the parts were analyzed, each part was classified
into three categories, A, B, and C. "A" parts were those with the highest Cost-Volume values
and required the most active management on an individual basis. At the other extreme were "C"
parts, typically low cost items such as hardware which would be purchased in higher volumes
and did not require careful inventory value analysis. In between were "B" parts. These would
be managed using the inventory model calculations for minimum quantities and reorder points,
but did not require the individual in-depth scrutiny as that of "A" parts.
After reviewing the Cost-Volume results for all wire harness parts (Appendix C), the first ten
parts were classified "A", the next fifteen parts were classified as "B" and the last 50% of all
items were classified as "C." The "A" classified parts' minimum bin and reorder quantities were
first calculated using the inventory model. Since these were the highest CV parts, each
calculated minimum quantity required further scrutiny and possible minor adjustment by
material planners who used future forecasted data to achieve adequate coverage with the highest
possible inventory turns. Class "B" minimum inventory quantities were simply calculated with
no additional management interaction. Last, for class "C" parts, it was proposed to order such
parts in larger bulk quantities to cover multiple months to avoid actively management of those
parts on a regular basis.
6.3.2 Example Minimum Level Calculations for Class "A" Part:
Using the highest ranked Class A part from the wire harness list, calculations of minimum order
quantity and bin reserve quantity have been outlined below. For more in depth inventory model
review, the model created to calculate individual order quantities based on required demand and
desired statistical coverage has been outlined in Appendix D.
Calculation of minimums began with analysis of the part's monthly usage data (only 11 months
were available from the Instron data system) as shown in Table 6.
67
INVENTORY DEMAND
Usage
Month
Jan
11
21
Feb
Mar
25
15
Apr
May
17
20
Jun
Jul
16
8
Aug
19
Sept
Oct
15
Nov
15
INVENTORY PARAMETERS
Month Demand Average
17
5
Month Demand St Dev
Manufacturing Lead Time
28 days
Resupply Shipping Lead Time
7 days
Stocking Probability
97.5%
Z statistic
1.96
Table 6: Inventory Data for Sample Wire Harness Component
The average demand over the vendor's lead time (DOLT) was equal to the average monthly
value times the lead time converted to monthly units:
DOLT = l7units per month*35days*l2months/365days = 19.5 units
The required safety stock corresponding to the 97.5% in-stock probability was equal to the
standard deviation times the (z) variable times the square root of the lead-time adjusted for
months:
SS = 5*1.96*(35days*lmonth/30days)
1/2
= 10 units
The total of the average demand and safety stock over the lead-time was the minimum lot order
size (rounded to whole number):
Minimum Lot Size = 19.5 + 10 = 30 units
Again, this was the minimum lot size to satisfy 97.5% probability of variations in demand at
final assembly during the vendor's lead time. This was also the minimum bin quantity that
would trigger an order for another lot of parts. It was expected that during the lead time, the
average number of units would be consumed, leaving the safety stock quantity remaining once
the new lot was delivered. In cases where demand exceeded average, some safety stock would
have been consumed.
68
From these calculations, it was proven that two quantities really drove the levels of additional
safety stock inventory. First was the month-to-month variation in demand (from which standard
deviation was derived). Second was the vendor lead time. If either or both of these values was
reduced, the overall amount of inventory was also reduced, which saved on holding costs and
allowing higher inventory turns.
The above numbers showed the importance of well-maintained vendor relationships. In this
example, the original lead-time for wire assemblies was 49 days. This included time for a
purchase order to be initiated and certified by the wire vendor. To shorten lead times to 35 days,
arrangements with the vendor were made to pre-certify purchase orders so that the time delays
due to financial review were removed from lead time values. This simple arrangement allowed a
reduction in inventory safety stock levels of almost 25%.
The inventory analysis outlined in this work considered one subset of a single vendor's parts for
one product division at Instron. It was meant to provide a model for inventory analysis that
should be used to analyze the remaining parts with the Electromechanical and Hardness divisions
and across inventory for other product divisions within Instron.
As demonstrated above, variation in manufacturing demand drove inventory levels. Although
Instron experienced cyclical demand from its customers, reducing this cyclical variation in
manufacturing could significantly reduce required inventory levels. This potential inventory
strategy has been outlined in the next section as a significant means to demonstrate the savings
from using a Lean, linear production method.
6.4 Linearized Assembly Output Enables Inventory Reductions:
The progression towards assembling at a constant rate (described earlier in Chapter 5) brought a
tremendous opportunity for reductions in corresponding inventory levels. The traditional end of
quarter ramp up in sales requires parts inventory that was available to satisfy higher than average
demands. However, carrying inventory at this level throughout the whole year increased holding
costs. If the linear production method was implemented, inventory levels could also be
significantly reduced, leading to increased number of inventory turns per year.
Taking another part that is used consistently in the Electromechanical product line at Instron as
example inventory, cost and volume differences were derived between inventory levels to
support the traditional variations of monthly usage vs. inventory levels to support a linear
production demand pattern. Table 7 summarized the inventory data for this representative part
based on the historical usage of the part for the past 12 months.
69
INVENTORY DEMAND
Month
Usage
Jan
12
Feb
23
Mar
39
Apr
15
May
20
Jun
38
Jul
16
Aug
24
Sept
40
Oct
18
Nov
26
Dec
42
INVENTORY PARAMETERS
Month Demand Average
26
Month Demand St Dev
11
Manufacturing Lead Time
28 days
Resupply Shipping Lead Time
7 days
Stocking Probability
97.5%
Z statistic
1.96
Table 7: Inventory Data for Sample Inventory Item Used to
Demonstrate Inventory Savings from Linear Assembly Methods
Using the inventory calculations presented earlier with a vendor manufacturing lead time of 28
days and a resupply time to Instron of 7 days (assuming the vendor holds one lot of material on
the shelf that was ready to ship) and 97.5% stocking, the following inventory levels were
derived:
Minimum Lot Size Analysis (Based on Full Lead Time of One Lot):
Demand Over Full Lead Time = 26 units/month*35days*month/30days = 30.4 units
Safety Stock Over Lead Time = 1.96*11 *(35days*month/30days)1' = 22.9 units
Minimum Reorder Quantity = 53.6 = 54 units
Minimum Bin Reserve Level (Based on Resupplv Time from Vendor Stock):
Demand Over Resupply Time = 26units/month*7days*month/30days = 6.1 units
Safety Stock Over Resupply Time = 1.96*11 *(7*month/30days)1 = 10.4 units
Minimum Bin Level Reorder Point = 16.5 = 17 units
These values were compared to those that resulted using linear demand at final assembly. As
described earlier, linear demand was based on both prior historical aggregate demand and future
forecasted demands. The production schedule would be set to produce 6.0 units per week with
an allowed demand variance in any week of 1.0 machine with the same 97.5% in-stock
probability. Based on this demand pattern with such controlled variation, the following revised
values of minimum lot size and minimum bin reserve quantity were calculated.
70
Minimum Lot Size Analysis (Based on Full Lead Time of One Lot):
Demand Over Full Lead Time = 6 units per week*35days*52weeks/365days = 30.0 units
Safety Stock Over Full Lead Time = 1.96*(1.0*35days*52weeks/365days)/2 = 4.4 units
Minimum Reorder Quantity = 30.0 + 4.4 = 34.4 = 35 units
Minimum Bin Reserve Level (Based on Resupplv Time from Vendor Stock):
Demand Over Resupply Time = 6 units per week*7days*52weeks/365days = 6.0 units
Safety Stock Over Resupply Time = 1.96*(1.0*7days*52weeks/365days) 2 = 1.9 units
Bin Minimum Level = 6.0 + 1.9 = 7.9 = 8 units
This analysis has shown that reduced production demand variations translated into significant
inventory lot size and minimum bin level reductions. Translated into Instron's metric of
inventory turns, this one part demonstrated a potential for a 53% increase in number of inventory
turns per year.
Average Yearly Demand
Reorder Quantity
Minimum Bin Quantity
Potential # Turns per Year
Cyclical Demand
312
54
17
5.8
Constant Demand
312
35
8
8.9
How was leveled control in inventory initiated? The production side was already discussed,
requiring involvement from manufacturing, marketing and sales to provide consistent sales
tactics and understand limitations of demand increases. The supply side had additional
requirements for creating such a consistent system. The demands on the supplier needed to be
reasonably stable within a defined time period. This stability was accomplished in agreement by
both vendor and parent manufacturer on an acceptable range of demand variance (increases or
decreases) over a given time horizon. In the example above, the variance was limited to plus or
minus 1/6 the level of parts normally ordered in any one week. The orders that resulted from an
increased or decreased demand had to be fulfilled without affecting the supplier's lead-time. If
increases greater than the agreed upon amount were necessary, the time for the supplier to ramp
up inventory levels was provided with an agreed upon time horizon.
This chapter provided an inventory analysis method to align inventory levels with production
output demand, variations in demand, supplier lead times, and statistical stock-out occurrences.
In addition, the inventory analysis was used to demonstrate the potential reductions in inventory
and corresponding increases in inventory turns that were realized when the Lean linear
production method was implemented. The reduction in inventory was another tangible benefit
that also clearly demonstrated the importance of an integrated Lean Manufacturing system
implementation.
71
72
77
RESULTS AND RECOMMENDATIONS
A Lean Manufacturing process was successfully developed and implemented for a low-volume
assembly manufacturing operation. Numerous improvements were realized through the
application of Lean Manufacturing in the experimental setting of Instron's assembly operation,
and have been outlined in this chapter to share the success. A great deal of learning also
occurred during this project on the implications of implementing Lean techniques in a lowvolume cyclical environment. This knowledge has been outlined to further the advance of Lean
practices into other low-volume environments, with the similarities and differences between pure
theoretical Lean Manufacturing and the process developed in this work clearly distinguished.
Sustaining process improvements beyond the six-month period was also a crucial aspect of
project success, and the methods to ensure such success have been outlined. Last,
recommendations for future continuous improvement opportunities at Instron have been
provided to complement the improvements completed during the project period.
It must be noted again that it was important to closely integrate the three principles of the
project's focus to achieve the final results:
1. Production process improvements
2. Changes to the physical production environment to support the process
3. Inventory management methods
All three principles were strongly co-dependent, and process improvement would have been suboptimized if they were not completed together.
7.1 Results at Instron - Flow Time Decreased by 40% in Electromechanical Production:
The pull based production process that was implemented using kanbans and POU inventory
placement showed significant flow time savings. To clearly quantify the savings, flow time for
each machine was limited to the time in manufacturing operations. It assumed that orders had
been approved for manufacturing and that the process was completed once a machine was sent to
shipping. Using one product line to quantify improvements - Single Column Electromechanical
products - manufacturing flow time was reduced by an average of 40%. Just as important, the
variation in this flow time was also reduced by over 18%. These results were calculated from
production over the last two quarters of year 2000. The third quarter data was derived from the
old production process before changes were implemented. The fourth quarter data was derived
after both point of use inventory placement and the kanban production process were
I
implemented. In both quarters, outliers were removed from the data after identification of
assignable causes that resulted in extended flow time for those particular units (Devor, 1992).
Reductions in assembly flow throughput time per individual unit were clearly identified over this
six-month project term. Figure 22 shows the summarized data for flow throughput days per
machine produced during this time. The improved production process was started at the
73
beginning of quarter four in 2000. Dramatic shifts were seen even at the start of the new process
period. This was reasonable because the start of each quarter generally showed less demand with
a ramp up expected through the quarter, which allowed the new process to most easily be
implemented and tracked starting at the beginning of a quarter.
U)
cc
Individual machines built in order through quarter
Figure 22: Flow Time in Days For an Example Electromechanical Product
Quarter 3 vs. Quarter4 in Year 2000
7.2 Additional Improvements at Instron:
The amount of physical floor space utilized for the new process was also reduced. 1200 sq. ft. of
underutilized space was removed from the original assembly area, resulting in a 15% reduction
in required floor space for an equivalent manufacturing output.
A more qualitative savings occurred in production scheduling and worker task prioritization
through the use of pull production. The system alleviated many of the problems facing
manufacturing planners in coordinating the sequence of machines into assemble and determining
which orders were waiting to be either started or were already started within process. Limiting
the number of subassemblies per kanban and operating to a daily production output, the new
system allowed greater visual indication of expected WIP and units ready to ship. Further, the
operators were provided with a straightforward method to prioritize their own daily actions of
assembling and testing. This led to reducing the confusion in coordinating daily activities on the
factory floor and the confusion between production planners and assemblers in determining
which orders to schedule and work on.
Sources of variability were identified and controlled more easily using the lean processes. The
reasons for such variations were numerous, but three most important considerations were
variations in assembly times of each model produced, variation in test times of each model, and
availability of in-stock parts inventory.
74
Using the pull production assembly method with a daily output schedule, single piece flow of
product through each assembly line, kanbans to stage subassemblies, leveled order flow into
production and daily decision rules to govern the work process all greatly assisted in reducing
the effects of variation in test and assembly time. The particular focus on a daily production
schedule elevated the importance of achieving a consistent output every day to prevent having to
satisfy large demands at the end of each quarter.
Inventory availability became critical to maintain consistent assembly flow. Such necessity
drove the need for close coordination of production demands with inventory supply availability
& supplier lead times. In response to managing these inventory requirements, this thesis
provided a methodology to calculate inventory levels aligned with production demands.
Inventory was first classified according to the Cost-Volume value of each inventory item, with
the highest Cost-Volume items receiving the greatest attention for inventory level maintenance.
Inventory levels were then derived from average demands, variations in demand, supplier lead
times, and statistical service levels. After the baseline inventory quantities for the existing
demand were calculated, it was demonstrated that inventory levels could be further reduced
through reducing demand variations and vendor lead times, the two major contributors to high
inventory requirements.
Lean Manufacturing processes also contributed to potential financial gains. Improved order
responsiveness of the production process led to two potential unit sales increases. First, given
lower lead times, customers may be more inclined to make first time purchases of Instron
product since capital funding is often available to customers for only short periods of time.
Second, a significant business is developing in the market for replacement Electromechanical
and Hardness machines. In this scenario, customers own older Instron equipment in need of
repair. Instead of repairing such machines, Instron offers a replacement program with a new
model. From the customers' view, this would often be an optimal solution if new equipment is
available with short lead time to ensure the customer maintains testing functionality with
minimal down time. Therefore, flow time reduction in production can directly impact increased
sales and the company's bottom line financial results.
7.3 Sustaining the Process Improvements:
This project initiated the beginning of an ongoing improvement effort. The current and future
process developments must be "owned" by management and the workers who will be using these
methods every day. Actions to ensure this ownership were intentionally started at the beginning
of the project. Two internal employee teams were created - one including management and one
including all of the operators, to not only allow for learning and buy-in of a new process, but to
end up with a group of people with the knowledge to sustain the work after the project term
ended.
Internal ownership of the top level work structure and integration between manufacturing and the
suppliers was accomplished through continuation of the management team formed at the
beginning of the project. The manufacturing managers were trained in the strategic use of
kanbans as well as the inventory management tools created during the project. Further, they
were trained how to visualize and monitor the ongoing process to ensure consistent daily output.
75
Last, they were educated on how to maintain the process when consistent assembly could not be
completed due to inventory or quality issues - for which fallback plans were created to use in the
short term that would complement the steady state process once the problems were resolved. For
instance, one fallback plan outlined how to begin multiple orders by assembling ahead of
schedule if the testing function cannot be completed due to equipment or part quality issues.
Once the problem was resolved, extended time was to be provided for completing test cycles to
regulate production and the kanban levels back down to their steady state calculated capacities.
Ownership of the work process itself was transferred directly to the workers within the
Electromechanical/Hardness department. The use of the subassembly kanbans and decision rules
provided a framework for the operators to control daily output, as well as a reference for
discussions of output with management. It also provided a forum to suggest modifications to the
work structure. Last, it provided a standardized method that they have already learned that could
be applied to the assembly of new products when introduced into manufacturing.
Sustaining the process also called for maintaining consistency of actions within the department.
Daily morning communication meetings on the factory floor were initiated during the project.
These meetings were used to ensure that the workers realize the importance of the new process
as well as have them experience continued involvement from management. Output was also
monitored daily to ensure consistent output with the plan's expectations as well as ensure that the
process did not revert back to "fire fighting" the steep incline of demands at the end of quarter.
Linearity proved its purpose at the end of the fourth quarter of 2000, reducing overtime and flow
days through the line.
Finally, to sustain the momentum gained during the project, any problems that inhibited
consistent output had to be resolved quickly. Lack of parts' availability on the floor was one
instance experienced repeatedly. If parts were not available for linear production, it was seen as
a failure of the system since workers could not obtain their daily quotas. Therefore, resolution
was required quickly to ensure the process did not break down over time.
7.4 New Models Arrive in Manufacturing:
The production process was structured to sustain variations in type and quantity of products
produced. Three main elements were incorporated at Instron to ensure process flexibility as new
products are introduced in the future:
1. The physical parameters were able to be modified and/or expanded with little cost or time
penalty.
2. The workflow process provided common baseline parameters through kanbans and daily
production decision rules, yet allow for modification as needed to suit specific products.
3. Workers were trained with multifunctional skills to allow labor flexibility for new roles
or tasks as product needs changed.
The modularity in the process and layout in this study allowed for product variations to be
absorbed with minimal disruption. Physical arrangement of part locations could be modified and
expanded since all inventory was now staged on wheeled racks and pallets. Floor space was not
76
initially filled to capacity on each flow line - allowing future expansion within reasonable limits
for expected new product introductions. Further, the process of single piece flow driven by
kanban locations and decision rules was a generic process structure that could be applied to
many manufacturing applications. In this example, the decision rules and kanban
location/quantities were specifically chosen. These choices could be adapted as needed for a
particular production environment, which allowed translation to new product types and varied
output volumes. For instance, reasonable volume increases were possible by adding labor to the
same process since the number of stations that have been set up exceeds the number of workers
by 100% (Electromechanical required 3 operators and had 6 major work stations). Finally, the
workforce was being trained for cross-functional tasks, allowing each employee to use skills
from assembling existing products on new production models.
7.5 Comparison of the Low-Volume vs. the Original Lean Manufacturing Process Goals:
The methods proposed in this project were targeted to a low volume environment to create a
leaner production system. Some of the theoretical elements of Lean Manufacturing have been
adapted to fit the low volume environment, and some elements were only partially used. As a
reference to directly compare the this lean implementation to theoretical Lean Manufacturing
principles, Table 6 has listed the major characteristics of this low-volume process, which were
then categorized according to how completely they fulfill the standards in a theoretical Lean
Manufacturing System. SIMILAR implied the low volume process directly incorporated the
Lean Manufacturing priciple, PARTIAL PROCESS suggested that the low-volume process was
moving toward becoming a Lean process, and VARIANT explained that the process uses in the
low volume environment deviated from the precise definition of Lean Manufacturing, but was
used to best accommodate the unique low-volume production environment.
77
Table 8: Comparison of Theoretical Lean Manufacturing Techniques
and the Low-Volume Lean Process Outlined in this Project
Process layout
Theoretical Lean Manufacturing
Low-Volume Lean Manufacturing
Process Principles
Process Principles
Product-focused using flow-based
production/assembly with physical
alignment of each process step
SIMILAR: Product-focused using
flow-based assembly with physical
alignment of each process step per
product family
Lot sizing
Produce only to demand with small
SIMILAR: Produce to customer
lot quantities
orders with single lot quantities
Pull production
methods
Only produce to fill kanbans when
downstream stages demand product
Ability to vary
model production
Ability to complete quick
changeovers between models on one
line
Standardized process
Minimal to No WIP allowed: Ideally
no buffers between stations, buffers
removed when possible to reduce
inventory to minimum possible
SIMILAR: Produce to fill kanbans
when downstream stages remove
product based on daily demands and
decision rules
SIMILAR: Ability to produce any
model within one product family in
one line
SIMILAR: Standard process with
accompanying daily decision rules
SIMILAR: Using Heijunka method
to level load customer orders to
balance daily and weekly production
requirements
PARTIAL PROCESS: Increased
mutual understanding of Instron's
and suppliers' needs and processes
PARTIAL PROCESS: Waste
reduction concentrated on
throughput time, product
transportation between stations,
assembly actions, inventory, and
worker movement
VARIANT: Inventory order
quantities calculated to coordinate
with production demands using
suppliers in their existing locations
VARIANT: Flexibility to
accommodate product variations
with line pace set around an average
takt time
VARIANT: Minimized and
strategically placed WIP in
calculated amounts to buffer against
assembly time variation between
levels
products
Process rules
Production
planning
Supplier interface
Using Heijunka (production
evenness) to balance daily
production requirements with
demand
Cooperative partnerships established
Cost reduction
through
elimination of
waste
Waste reduce in overproduction,
throughput time waiting, internal
plant product transportation,
processing, inventory, worker
movement, and defective products
Inventory
management
Just In Time delivery using local
suppliers
Production rate
WIP inventory on
floor
Line pace set with strict adherence
to takt time
78
7.6 Future Recommendations for Continuous Improvement:
1. Worker Training & Involvement:
Worker cross-training ensures greater process sustainability. The flexibility of the operators is a
key element of process success. During the project term, limited cross training was initiated.
However, cross-functional workers within a product line allowed complete flexibility for each
person to "Build, test, and ship" a product. This training must be formalized as part of the
ongoing process, with operators required to cross train as part of their yearly success
management goals. No longer are specialists needed in the production process. However, this
"specialist" mentality can remain in the absence of formal cross-functional training, both out of
fear of job loss and lack of understanding that ability in multiple tasks is more desirable from a
flexible manufacturing management standpoint.
Worker involvement is also critical. Incentives to train and become accountable for the process
must be put into place to make it personally desirable for each worker to learn and to motivate
the process changes. Increasing the diversity and challenge of each assembly position, with
incentives to match those challenges, would also aid in retaining the best workers. Given the
tight labor economy, it is best to ensure a challenged workforce that is well compensated to deter
workforce migration.
Further, involvement should extend beyond the manufacturing department. Who better to
provide manufacturability input to newly designed models than the operators who will later be
responsible for assembling those new products? Allowing operators time away from the line to
review and provide input to new designs would allow faster ramp up for new products once they
arrive in manufacturing. This would allow the removal of assembly-related flaws in the design
before it is released. Early design involvement would also allow the operators to become
familiar with the product before it gets moved to manufacturing, creating "experts" for the new
models to train others on the line. Such proactive planning is needed to provide even faster
customer response time for future new products.
2. Determine the Validity of System Testing:
Variation exists in the entire production system. The activity that shows the greatest variability is
testing. On average, testing requires 33% of total system production time and accounts for 40%
of its variation. Further, testing is a legacy of the process, completed to ensure quality of
assembly and proper operation of internal electronics. Given today's higher standards of
electronics assembly and workers' ability to self-check assembly quality, it is questionable
whether testing, as a separate function, is still necessary. Therefore, it is recommended that test
data be reviewed statistically using process run charts for each test function to determine the
frequency of problems solved through testing, and to determine where process is in control and
not in need of the current testing function. Using this data, quality management can be employed
to select test functions to remove. At the extreme, removing all testing would allows for further
flow time decreases by 33%. In addition, output capacity would increase by up to 33%, given
the existing level of labor, allowing for product line expansions without incurring additional
labor costs.
79
3. Extension of ManufacturingProcess Concepts to Other Divisions:
Instron should consider expanding the concepts of pull production and inventory management to
their other product divisions. These concepts have been proven within the low volume
environment. The company can therefore further amplify its manufacturing strength by
maintaining consistency in process control and inventory management across all divisions.
Replicating the common process framework would allow the creation of corporate wide
production metrics, greater flexibility of workers who have common process knowledge between
divisions, and greater coordination between division planners and outside suppliers.
The inventory analysis presented in this thesis modeled one group of parts from
Electromechanical production to demonstrate the inventory management methods used. This
model can further be used as a template for continued inventory management in all other part
groups within EM and the other divisions. To limit the extent of this analysis, ABC
classification should first be applied to each product line to determine the highest value inventory
items for each. Management focus should then be placed on controlling these high value items.
In theory, the production "pull" process should extend throughout the value chain, from raw
material to finished product. This project was used as a pilot program to initiate the process only
within manufacturing. It is important to recognize that further optimization can be realized if
efforts within Instron's pull process are also coordinate with major suppliers. Significant
amounts of lead time could be removed if vendors align their own production cycles with
Instron's manufacturing demand patterns. This requires close coordination and mutual
understanding of each process.
It must also be acknowledged that manufacturing time is only a fraction of the total time
currently required to fulfill a product order within Instron. The time to initiate an order and
approve the order for manufacturing requires an additional time through sales and order entry.
Applying lean initiatives to remove wasteful actions in the sales functions would eliminate
potentially greater amounts of time for order fulfillment, including excessive waiting for
customers' credit approvals and time to route orders from sales to manufacturing. These premanufacturing functions must also be viewed as "waste" in improving overall customer
responsiveness, and customer order fulfillment must be analyzed and "leaned out" as one system
if optimum results are to be achieved.
80
/
/
DATA TIMESHEETS
The following sample datasheet is representative of those used to collect data from the assembly
process for each unit produced throughout the project period.
81
ELECTROMECHANICAL / HARDNESS
PROCESS IMPROVEMENT DATA TIME SHEET
Model Number
Customer
Assembly Start Date
Date
Procedure
Order Due Date
ie Duration
Assembly End Date
82
Problk
til
A
/
LABOR CAPACITY MODEL
An example spreadsheet is provided that outlines the calculations for labor capacity
requirements. It includes input of weekly production quantities and the amount of overtime
labor allowed for increasing capacity to the desired level. Output provides the number of labor
hours required per unit of time as well as the number of operators required to complete the
desired production quantity.
83
Tabletop EM Product
Integration Test+Calibrate Finish
2.2
4.1
1.9
0.7
1.2
1.2
1.3
3.5
1.0
Assembly/Test Time Data
Mean
Stdev
Standard Times
Tray
2.4
0.4
3.0
Top End
2.6
0.5
1.4
Labor Capacity Calculations
Weekly Demand Quantity
# Hrs Authorized for OT/Week/Person
10.0
0.0
INPUT
INPUT
Hrs per Machine
Total Hrs Required / Week
# Operators Required
13.7
137.3
3.9
CALCULATED FROM ASSM DATA
CALCULATED FROM INPUT
CALCULATED FROM INPUT
Tray
2.2
Top End
1.4
0.3
0.6
2.0
1.7
Labor Capacity Calculations
Weekly Demand Quantity
# Hrs Authorized for OT/Week/Person
10.0
INPUT
INPUT
Hrs per Machine
Total Hrs Required / Week
# Operators Required
10.5
105.0
3.0
Audit
0.5
0.1
2.5
Total
13.7
1.9
12.7
Audit
1.5
1.3
2.5
Total
10.5
2.0
9.7
Single Column EM Product
Assembly/Test Time Data
Mean
Stdev
Standard Times
0.0
Integration Test+Calibrate Finish
0.9
3.5
1.0
0.2
0.7
0.1
0.5
2.3
0.7
CALCULATED FROM ASSM DATA
CALCULATED FROM INPUT
CALCULATED FROM INPUT
Hardness Model 2000 Product
Assembly/Test Time Data
Mean
Stdev
Standard Times
Labor Capacity Calculations
Weekly Demand Quantity
for OT/Week/Person
Authorized
# Hrs
Integration Test+Calibrate Finish Total Time
Tray
1.4
Actuator
1.7
1.0
2.4
1.4
0.4
0.5
0.2
0.5
0.2
1.2
3.0
1.9
2.1
1.0
f.J
INPUT
INPUT
CALCULATED FROM ASSM DATA
Hrs per Machine
8.0
Total Hrs Required / Week
47.7
CALCULATED FROM INPUT
# Operators Required
1.4
CALCULATED FROM INPUT
Comments:
Spreadsheet used to calculate labor requirements for each product family assembly process
Capacity based on test time data collected directly from assembly process
Mean times used to establish labor assuming leveled production
7 hour work day time basis
Overtime used as additional capacity when required
84
8.0
1.0
9.1
APPENDIX C
Z~~~
7
INVENTORY ANALYSIS MODEL
ANQ
4PREb1AD
HEETS
Two example spreadsheets are outlined in the following pages. The first is a sample template for
calculating the monthly demands and Reorder Points for a single inventory item based on
demand and statistical safety stock requirements. It is separated into two sections:
1. User inputs based on historical demands and desired stocking probabilities.
2. Resulting calculated outputs for ROP and Min levels
The second set of four pages is one spreadsheet that outlines one set of parts purchased from one
select vendor. Data includes historical usage, cost-volume analysis for ABC classification,
statistical demands calculated from historical usage and stocking probabilities, reorder point lot
size results for the supplier, and internal reorder point values.
85
Inventory Analysis Worksheet
SPREADSHEET INPUTS
PART INFORMATION
PART #
#
NAME
STANDARD COST $
VENDOR MFG LEAD TIME (DAYS)
RESUPPLY LEAD TIME (DAYS)
NAME
154
28
7
LEAD TIME OPTION
SELECT "1" FOR SEPARATE RESUPPLY AND MFG LEAD TIMES OR "o" IF ONLY USING
MFG LEAD TIME
f
HISTORICAL DEMAND
fMonthly Demand for Last 12 Months
JAN
12
FEB
18
MAR
2
APR
19
MAY
21
JUNE
24
JULY
13
AUG
11
SEPT
10
PREVIOUS YEAR TOTAL USAGE
OCT
9
NOV
15
DEC
18
150
STATISTICAL VALUES
DESIRED PROBABILITY OF BEING STOCKED
0.9750
SPREADSHEET CALCULATED OUTPUT RESULTS
DEMANDS OVER THE PAST 12 MONTHS
HIGHEST MONTH DEMAND - PAST 12 MONTHS
AVERAGE MONTH DEMAND - PAST 12 MONTHS
TOTAL USAGE - PAST 12 MONTHS
STANDARD DEVIATION OF DEMAND - PAST 12 MONTHS
24.0
14.3
172.0
6.1
DEMAND OVER PAST 2 YEARS
AVERAGE MONTH DEMAND - PAST 2 YEARS
13.4
STATISTICS
STATISTICAL Z VALUE BASED ON DESIRED PROBABILITY
1.96
DEMANDS OVER LEAD TIME TO GET MINIMUM REORDER QUANTITIES
HIGHEST DOLT - WITHIN PAST 12 MONTHS
AVERAGE DOLT - WITHIN PAST 12 MONTHS
28.0
16.7
AVERAGE DOLT - PAST 2 YEARS
15.7
REQUIRED SAFETY STOCK FOR AVERAGE DEMAND OVER LEAD TIME
TOTAL DOLT+SS = MINIMUM REORDER QUANTITY
12.8
29.0
MINIMUM BIN LEVEL TO TRIGGER NEW ORDER
AVERAGE DEMAND OVER RESUPPLY LEAD TIME
3.9
SAFETY STOCK FOR RESUPPLY LEAD TIME
INSTRON INTERNAL MIN REORDER POINT
3.2
8.0
86
00
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0
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4
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-CS
COST VOLUME CALCULATIONS
STATISTICAL VALUES BASED ON USAGE
Line
Number
1999 Cost
Volume
Amount
Cumulative
Cost
Volume
Cumulative
Part
Percentage
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
39163
25461
12603
8955
7939
7832
6915
5951
5478
5131
5012
4248
4098
3668
3288
3233
3137
3102
3094
2880
2796
2598
2578
2319
2272
2091
2019
1986
1724
1527
1440
1430
1361
1357
1313
1291
1084
969
602
559
498
432
412
338
322
291
237
61
24
24
19
0.20
0.33
0.39
0.44
0.48
0.52
0.55
0.58
0.61
0.64
0.66
0.68
0.70
0.72
0.74
0.76
0.77
0.79
0.80
0.82
0.83
0.84
0.86
0.87
0.88
0.89
0.90
0.91
0.92
0.93
0.94
0.94
0.95
0.96
0.96
0.97
0.98
0.98
0.98
0.99
0.99
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.020
25
17
5
0.975
1.960
0.039
28
20
8
0.975
1.960
0.059
14
535
64
17
59
109
2290
97
6
339
37
8
43
71
1585
67
5
112
18
5
12
34
455
24
0.975
0.975
0.975
0.975
0.975
0.975
0.975
0.975
1.960
1.960
1.960
1.960
1.960
1.960
1.960
1.960
49
29
12
0.975
1.960
29
14
59
14
6
41
9
5
12
0.975
0.975
0.975
1.960
1.960
1.960
32
15
10
0.975
1.960
11
7
2
0.975
1.960
32
19
10
0.975
1.960
17
127
8
75
5
33
0.975
0.975
1.960
1.960
49
88
49
30
50
30
11
22
11
0.975
0.975
0.975
1.960
1.960
1.960
29
14
9
0.975
1.960
63
30
14
0.975
1.960
34
29
15
14
9
7
0.975
0.975
1.960
1.960
17
10
6
0.975
1.960
31
16
7
0.975
1.960
34
16
10
0.975
1.960
9
44
32
45
4
44
3
27
17
25
2
27
2
11
8
11
1
11
0.975
0.975
0.975
0.975
0.975
0.975
1.960
1.960
1.960
1.960
1.960
1.960
32
15
10
0.975
1.960
17
212
14
15
57
17
17
8
103
6
6
39
8
8
5
47
5
4
12
5
5
0.975
0.975
0.975
0.975
0.975
0.975
0.975
1.960
1.960
1.960
1.960
1.960
1.960
1.960
34
16
10
0.975
1.960
17
3
4
8
2
2
5
1
1
0.975
0.975
0.975
1.960
1.960
1.960
3
1
1
0.975
1.960
2
5
5
1
2
2
1
2
2
0.975
0.975
0.975
1.960
1.960
1.960
Highest Average
Month
Month
Usage
Usage
0.078
0.098
0.118
0.137
0.157
0.176
0.196
0.216
0.235
0.255
0.275
0.294
0.314
0.333
0.353
0.373
0.392
0.412
0.431
0.451
0.471
0.490
0.510
0.529
0.549
0.569
0.588
0.608
0.627
0.647
0.667
0.686
0.706
0.725
0.745
0.765
0.784
0.804
0.824
0.843
0.863
0.882
0.902
0.922
0.941
0.961
0.980
1.000
Total
197160
88
Stdev
Month
Usage
Probability
Stocked Z Value
ESTiMATED REORDER QUANlMES BASED ON DEMAND AM) SUPPUER LEAD TIME
Line
NUmber
Supplier MIg
Lead Time
Days
Resupply
Lead Time
Days
Average
DOLT
Year 2000
Average
DOLT
Year 1999
Average
DOLT
Past 2 Yrs
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
28
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
19.8
23.3
7.0
395.5
43.2
9.3
50.2
82.8
1849.2
78.2
33.8
16.3
7.0
47.8
17.5
8.2
22.2
9.3
87.5
35.0
58.3
35.0
16.3
35.0
17.5
16.3
11.7
18.7
18.7
3.5
31.5
19.8
29.2
2.3
31.5
17.5
9.3
120.2
7.0
7.0
45.5
9.3
9.3
18.7
9.3
2.3
2.3
1.2
12
2.3
2.3
23.0
16.8
7.2
318.2
47.3
9.7
32.8
112.8
1868.8
21.4
20.1
7.1
356.9
45.2
9.5
41.5
97.8
1859.0
70.3
36.8
14.0
6.9
39.7
15.5
7.9
21.8
9.1
91.7
38.0
61.8
38.0
14.0
39.4
17.9
18.9
10.3
20.9
15.5
3.7
35.2
21.0
28.2
2.5
35.2
15.0
9.4
122.7
6.9
6.3
36.9
9.0
9.3
18.8
9.1
1.4
2.5
0.9
0.6
1.5
1.5
62.5
39.8
11.7
6.8
31.6
1a4
7.7
21.5
8.8
96.0
40.9
65.3
41.0
11.6
43.8
18.3
21.5
8.9
23.0
12.3
4.0
39.0
22.2
27.3
2.6
38.9
12.4
9.4
125.2
6.8
5.5
28.3
8.8
9.2
18.9
8.8
0.5
2.6
0.7
0.1
0.6
0.6
89
Safety Stock
Total
For Ave
DOLT+SS
Demand
IVIn Reorder
9.9
17.4
9.8
237.4
38.6
11.0
26.0
72.2
963.2
49.9
24.7
19.8
9.8
26.3
20.7
5.2
20.8
10.7
70.6
23.2
46.8
22.8
19.7
29.0
19.9
14.2
11.8
14.2
20.9
5.2
22.8
16.2
23.3
2.2
22.5
20.6
11.0
99.7
9.8
8.1
25.5
10.7
11.0
22.1
10.7
2.7
2.2
2.1
1.4
3.4
3.4
32
38
17
595
84
21
68
170
2823
121
62
34
17
67
37
14
43
20
163
62
109
61
34
69
38
34
23
36
37
9
59
38
52
5
58
36
21
223
17
15
63
20
21
41
20
5
5
4
3
5
5
INTERNAL MINIMUM QUANTITY ROP
Line
Number
Resupply
Lead Time
Days
Averaged
DOLT
Past 2 Yrs
Safety Stock
For Ave
Demand
Internal
Min ROP
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
7
4.29
4.02
1.42
71.37
9.04
1.91
8.29
19.56
371.80
14.07
7.36
2.80
1.38
7.94
3.09
1.58
4.37
1.82
18.35
7.59
12.37
7.60
2.79
7.88
3.58
3.78
2.06
4.17
3.09
0.75
7.05
4.20
5.65
0.50
7.04
2.99
1.88
24.54
1.38
1.25
7.38
1.81
1.86
3.75
1.82
0.28
0.50
0.18
0.13
0.29
0.29
1.99
3.49
1.95
47.49
7.72
2.21
5.20
14.43
192.63
9.98
4.95
3.97
1.95
5.27
4.14
1.04
4.16
2.15
14.12
4.64
9.36
4.56
3.94
5.80
3.98
2.84
2.36
2.83
4.17
1.03
4.57
3.24
4.66
0.44
4.50
4.13
2.20
19.93
1.95
1.62
5.10
2.15
2.20
4.42
2.15
0.54
0.44
0.42
0.29
0.69
0.69
7
8
4
119
17
5
14
34
565
25
13
7
4
14
8
3
9
4
33
13
22
13
7
14
8
7
5
8
8
2
12
8
11
1
12
8
5
45
4
3
13
4
5
9
4
1
1
1
1
1
1
90
Annotated Bibliography
Burman, M., S. Gershwin, and C. Suyematsu, "HP Uses Operations Research to Improve the
Design of a Printer Production Line," Interfaces, Vol. 28, Jan. Feb. 1998. This article describes
the use of buffers in a production line to increase productivity by preventing blocking and
starvation of any one segment in a production line. It also describes how this method deviates
from a traditional Lean/JIT system where no buffers are used.
Cochran, David S., Class notes and selected papers from Course 2.812 "Design and Control of
Manufacturing Systems," 1999. Selected topics on lean manufacturing with examples of theory
applied to manufacturing processes with some actual implementation results.
Crane, Barrett, "Cycle Time and Cost Reduction in a Low Volume Manufacturing
Environment," Masters thesis, MIT Leaders for Manufacturing program, 1996.
Devor, Richard, Tsong Chang, and John Sutherland, Statistical Quality Design and Control,
Macmillan Publishing Company, 1992. This text provides in depth methods of statistical data
analysis. Its methods were used to analyze assembly and test data taken from Instron's assembly
process. It also was used to explain "assignable" vs. "unassignable" causes of variation in data.
Dul, Paul, "Application of Cellular Manufacturing to Low-Volume Industries," Masters thesis,
MIT Leaders for Manufacturing program, 1994.
Fine, Charles and Hax, Arnoldo, "Manufacturing Strategy: A Methodology and an Illustration,"
Interfaces 15:6, Nov-Dec 1985. Article that provides examples of reviewing a manufacturing
environment as a system and the importance of using that as part of a corporate strategy.
Flinchbaugh, Jamie, "Implementing Lean Manufacturing Through Factory Design," Masters
thesis, MIT Leaders for Manufacturing program, 1998.
Goldratt, E.M., The Goal, North River Press, 1992. This book outlines the Theory of
Constraints, used to analyze capacity and distinguish between constraints and non-constraints
within a production environment. This theory was used at Instron to identify the process
bottleneck and how this needs to be managed to reduce overall product flow time.
Graves, Stephen and Jackson Chao, "Reducing Flow Time in Aircraft Manufacturing," Working
paper as part of MIT Leaders for Manufacturing program, 1992. This paper analyzes the full
costs of extended assembly flow times in the low-volume aircraft manufacturing environment. It
describes the cost impacts for the various stages of total flow time and provides regression
analysis to rank the major factors.
Hager, Dennis, "Applying Continuous Flow Manufacturing Principles to a Low Volume
Electronics Manufacturer," Masters thesis, MIT Leaders for Manufacturing program, 1992.
91
Hammer, Michael, "Reengineering Work: Don't Automate, Obliterate," Harvard Business
Review, July-August 1990. Short article describing how an environment should be reengineered
as a system and not looked at as an existing system that needs improvement.
Harman, Steve, "Implementation of Lean Manufacturing and One-Piece Flow at Allied Signal,"
Masters thesis, MIT Leaders for Manufacturing program, 1997.
Hayes, Robert H. and Wheelwright, Steven C., "Link Manufacturing Process and Product Life
Cycles," Harvard Business Review, January-February, 1979. This article outlines Hayes' and
Wheelwright's product-process matrix and describes how a company should position itself based
on different processes.
Jones, D.T. and Womack, J.P., Lean Thinking, Simon and Schuster, 1996. This is a world class
book on the teachings of implementing lean operations. It provides many real corporate
examples of how the principles of lean manufacturing have been successfully implemented, as
well as provides a basis to show how lean principles can be applied throughout all of an
organization's functions. Copies of this book were provided to all of Instron's manufacturing
management staff to allow the potentials of lean processes to be better understood. The results
and interest in the book's methods was incredible, providing a strong start at Instron to changing
people's mentality of how they could make improvements in their own processes.
Krafcik, John, "Triumph of the Lean Production System," Sloan Management Review, 1988.
Article that describes the success of the TPS and Lean Systems.
MacLean, Mark, "Implementing Lean Manufacturing in an Automobile Plant Pilot Project,"
Masters thesis, MIT Leaders for Manufacturing program, 1996.
Mahoney, Michael R., High-Mix Low-Volume Manufacturing, Prentice-Hall, Inc., 1997. This is
an industrys-sponsored book from Hewlett Packard that explains real world experiences of the
author through many engineering and manufacturing projects that he has completed. Throughout
these experiences, underlying principles of low volume industries are discussed along with the
alignment of manufacturing and overall organizational strategies. It provides many concretes
examples of JIT manufacturing, the Theory of Constraints, and Production Scheduling practices.
Mishina, Kazuhiro, Toyota Motor Manufacturing, USA, Inc., 1992. This is a case study of
applying the Toyota Production System to a Toyota plant in the United States.
Monden, Yasuhiro, Toyota Production System: An Integrated Approach to Just-In-Time,
Industrial Engineering Press, 1993. Descriptions and examples of TPS process integration.
Nahmius, S., Production and Operations Analysis, 3 edition. McGraw-Hill, 1997. This is a
textbook outlining factory operations and planning. It was useful to outline the basics of kanbancontrolled processes and setting kanban quantities. The book also provides sections on inventory
control for both known and uncertain demands, with a section on low volume demands
applicable to Instron's market.
92
Ohno, Taiichi, Toyota Production System: Beyond Large-Scale Production, Productivity Press,
1988. This reference directly describes the Toyota Production System from its origins at Toyota
directly from its founder Ohno.
Raymond, Arthur, "Applicability of Toyota Production System to Commercial Airplane
Manufacturing," Masters Thesis, MIT Leaders for Manufacturing program, 1992.
Schonberger, Richard J., World Class Manufacturing - The Lessons of Simplicity Applied,
Macmillan, 1986. This book outlines many simple techniques to apply lean manufacturing
principles to actual production processes. It also provides many descriptions of successful
implementation of such techniques in American companies.
Suri, Rajan, Quick Response Manufacturing, A Companywide Approach to Reducing Lead
Times, Productivity Press, 1998. This book compares and contrasts the Lean Manufacturing
(Toyota Productin System) that is focused on reducing waste to shorten lead times with a revised
method called Quick Response Manufacturing that is focused on shortening lead times which
provides reductions in waste as a result. It is truly a variant of the TPS, with many examples
simply reversed to fit the QRM model. Topics include manufacturing time response, capacity,
material planning and replenishment, and supplier relations. Further, it extends the concepts
from manufacturing into product development and general office operations. It is a clear reading
source that can be used to complement a Lean Manufacturing initiative.
"Toyota Motor Manufacturing, USA, Inc.," Case study from the Harvard Business School,
Revised 1995. Article that reviews examples of successful TPS implementation.
Vining, G., Statistical Methods for Engineers, Duxbury Press, 1998. This is an introductory text
on statistics. It was used as a reference to provide information on normal distributions and zstatistical calculations.
93