The Value of Accurate Automated Data Co flection to
Manufacturing
MASSACHUSETTS INSTI1tE
by
OF TECHNOLOGY
Alfonso Alexander Perez
OCI Z6
Bachelor of Science in Mechanical Engineering
Massachusetts Institute of Technology, 2013
2014
LIBRARIES
Submitted to the Department of Mechanical Engineering
in partial fulfillment of the requirements for the degree of
MASTER OF ENGINEERING IN MANUFACTURING
at the
MASSACHUSETfS INSTITUTE OF TECHNOLOGY
September 2014
C2014 Alfonso Alexander Perez. All rights reserved.
The author hereby grants MIT permission to reproduce and distribute publicly paper and
electronic copies of this thesis document, in whole or in part, and to grant others permission to
do so.
Signature redactedAuthor..............................................
r Perez
Afq Ae
Departmen t of Mechanical Engineering
August15, 2Q14
Certified by ...........................................
Signature redacted
Sta*ly B. Gershwin
Senior Reumprcbi Scientist
,4l eej,"':&pW~ior
Accepted by .............................
Signat
a ure redacted
..... Sg
David E. Hardt
Ralph E. and Eloise F. Cross Professor of Mechanical Engineering
Chairman, Committee for Graduate Students
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2
The Value of Accurate Automated Data Collection to
Manufacturing
by
Alfonso Alexander Perez
Bachelor of Science in Mechanical Engineering
Massachusetts Institute of Technology, 2013
Submitted to the Department of Mechanical Engineering on August 8 , 2014,
in partial fulfillment of the requirements for the degree of
Master of Engineering in Manufacturing
Abstract
The purpose of this thesis is to demonstrate the value of implementing a novel, accurate
automated data collection system and to describe a practical method for doing so. This thesis
addresses the value of accurate automated data collection to manufacturing, focusing on a
combined passive power monitoring and auto RFID data collection system implemented within
the CNC turning and CNC milling departments at the Waters Corporation Machining Center in
Milford, Massachusetts. A detailed study of the machining center revealed that inaccurate data
was being used to plan production. This thesis demonstrates a graphical approach and an
analytical method which can be used to determine manufacturing systems statistics such as
average part cycle time and average state dependent setup time.
This project addressed the problem by implementing a passive power monitoring and
auto RFID data collection system used to monitor ten selected part types and four machines in
the turning and milling departments. In order to prevent human error during the experiment, the
author developed a series of training guides for Waters to use to avoid data fidelity issues. Since
the Waters machining center is a highly metallic environment, many logistical issues were faced
during the implementation phase. These logistical issues include an overlapping RFID antenna
layout, operator training, and operator compliance. The present data collection system has an
opportunity cost of approximately $85,775 per year. Based upon the implementation cost of the
experimental setup used for this project, the cost to implement power monitoring and auto RFID
at full-scale will be $60,000 in raw materials. The combined passive data collection system is
expected to pay off 9 months after implementation in addition to data resolution and
standardization benefits.
Thesis Supervisor: Stanley B. Gershwin
Title: Senior Research Scientist
3
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4
Acknowledgements
The author would like to thank the following people for your love, unwavering optimism, and:
To my sister for inspiring me.
To my father for teaching me to shrug off the bad shots and to keep my spirits high.
To both of my grandfathers for teaching me to build houses and control electricity at a young
age.
To both of my grandmothers for teaching me that a real man knows how to cook.
To Chris, Forrest, and Mateo for endeavouring to revolutionize automated manufacturing.
To Arjun Chandar and Greg Puszko for your collaboration and creative inspiration.
To Dr. Gershwin for your unrelenting pragmatic skepticism.
To Dr. Hardt for teaching me how to get things under control.
To Dr. Hart and Jamison for the shared additive manufacturing teaching experience.
To the MIT-Lemelson foundation for making graduate school possible.
To the MITES program for teaching me how to be a successful MIT student.
To the Gordon Engineering Leadership Program for inspiring me to lead with confidence.
To Waters Corporation for sponsoring my research.
To Kerry McNamara and Gabriel Kelly for showing me the ropes and opening doors.
To the Pergatory group for stressing the importance of detailed design thinking.
To Abhishek Syal for helping me figure things out.
To Jimmy Pershken for helping me sum things up.
To Aunt Jill for your keen eyes.
To my mother for having the patience to wait until the end.
5
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6
Table of Contents
Abstract ...........................................................................................................................................
3
A cknowledgem ents.........................................................................................................................
5
List of Figures...............................................................................................................................
10
List of Tables ................................................................................................................................
12
Definition of Key Term s...............................................................................................................
13
Introduction.............................................................................................................
15
1.1
Statem ent of purpose....................................................................................................
15
1.2
Background inform ation about W aters Corporation..................................................
16
1.3
Waters' expansion into contract m anufacturing...........................................................
16
1.4
Waters' m achining center of excellence .....................................................................
18
1.5
Typical flow of parts and production at Waters.............................................................
21
1.6
Issues uncovered during prelim inary investigations .................................................
23
1.7
Outline............................................................................................................................
25
Chapter 1
Chapter 2
Problem statem ent...................................................................................................
M anufacturing data presently collected .....................................................................
2.1
26
26
2.1.1
Lot cycle time .....................................................................................................
27
2.1.2
M achine spindle tim e..........................................................................................
27
2.1.3
Setup tim e .........................................................................................................
28
Issues w ith present data collection system .................................................................
2.2
30
2.2.1
Financial impact....................................................................................................
30
2.2.2
Efficacy in accuracy.............................................................................................
31
2.2.3
D ata resolution......................................................................................................
32
Problem statement..........................................................................................................
32
2.3
Chapter 3
D ata collection concept overview ........................................................................
33
3.1
M anufacturing data collection requirem ents...............................................................
33
3.2
M achine state and duration ..........................................................................................
35
3.3
Part location and duration ..........................................................................................
36
3.4
Combined value of machine state and part location ...................................................
37
3.4.1
Graphical representation.....................................................................................
7
38
3.4.2
Chapter 4
4.1
A nalytical approach and governing equations ....................................................
42
M ethodology ...........................................................................................................
45
M ethods to capture machine state and duration........................................................
45
4.1.1
H uman/M anual ...................................................................................................
45
4.1.2
Com puter vision.................................................................................................
45
4.1.3
Power m onitor......................................................................................................
46
4.1.4
M achine statistics.................................................................................................
46
4.2
Methods to capture part location and duration...........................................................
46
4.2.1
H uman/M anual ...................................................................................................
47
4.2.2
Bar code ..................................................................................................................
47
4.2.3
Com puter vision.................................................................................................
47
4.2.4
Auto RFID ..............................................................................................................
48
4.2.5
Pressure pads...........................................................................................................
49
4.2.6
Photogate/optics...................................................................................................
49
4.3
W eighted decision m aking and system selection...........................................................
Chapter 5
Implementation ...................................................................................................
49
53
5.1
Implem entation of power monitoring system ............................................................
55
5.2
Implem entation of auto RFID system ........................................................................
57
5.3
Logistical issues during implementation....................................................................
59
5.3.1
Selecting the correct RFID tag .............................................................................
60
5.3.2
Power m onitoring obeys Faraday's Law .............................................................
63
5.3.3
Process plan for using RFID in a metal environment .........................................
65
Results and discussion ........................................................................................
68
Chapter 6
6.1
D emonstration of average cycle time data .................................................................
70
6.2
D emonstration of state dependent setup tim e data......................................................
73
6.3
D ata analysis challenges.............................................................................................
77
Chapter 7
Conclusion ..............................................................................................................
79
Chapter 8
Future work.............................................................................................................
82
8.1
Next steps for W aters.................................................................................................
82
8.2
Research agenda.............................................................................................................
84
Appendix.......................................................................................................................................
8
86
A-1
Dent ELITEpro XC Anatomy [7]...............................................................................
86
A-2
Cargo Track product specification [13] .....................................................................
87
A-3
Data collection process plan........................................................................................
89
A-3.1
Staging area.............................................................................................................
89
A-3.2
Staging area close up ............................................................................................
89
A-3.3
CNC Turning - M1302-2 correct orientation ......................................................
91
A-3.4
CNC Turning - M 13 02-2 incorrect orientation.................................................
92
A-3.5
CNC Turning - M1309 correct orientation .......................................................
94
A-3.6
CNC Turning - M1309 incorrect orientation .....................................................
95
A-3.7
CNC Milling- M1210 correct orientation ..........................................................
97
A-3.8
CNC Milling- M1210 incorrect orientation ........................................................
97
A-3.9
CNC Milling - M1210 incoming rack..................................................................
100
A-3.10
CNC Milling - M1211 incoming rack ..............................................................
106
A-3.11
CNC Milling - M1211 correct orientation........................................................
108
A-3.12
CNC Milling - M1211 incorrect orientation.....................................................
109
A-3.13
CNC Milling - Utility Operator correct orientation..........................................
110
A-3.13
CNC Milling - Utility Operator incorrect orientation ......................................
111
References...................................................................................................................................
9
112
List of Figures
Figure 1-1: The Waters machining center of excellence. .........................................................
19
Figure 1-2: Map of Machining Center. 1 = CNC milling, 2 = CNC turning, 3 = valve cell, 4 =
column cell, 5 = m odel shop.....................................................................................................
20
Figure 1-3: Map of operation steps for production process in CNC turning and CNC milling.
Consider initial operation, utility operator station, and downstream as three separate machines. 23
Figure 2-1: State independent setup time vector......................................................................
29
Figure 2-2: Generalized state dependent setup time matrix......................................................
30
Figure 3-1: Generalized column vector form of average cycle time for k part types......... 34
Figure 3-2: State dependent setup matrix applied to real system with k part types................... 34
Figure 3-3: Possible machine states versus time......................................................................
35
Figure 3-4: Example graph of part location and duration........................................................
36
Figure 3-5: Generalized graph of machine power consumption plotted against time.............. 38
Figure 3-6: Generalized graph of process type versus time......................................................
40
Figure 3-7: Graphical representation of combination of generalized auto RFID and power
m onitoring data. ............................................................................................................................
41
Figure 4-1: Typical auto RFID system. [5].............................................................................
49
Figure 5-1: Map of Waters' machining center of excellence indicating the location and type of
system in each location.................................................................................................................
54
Figure 5-2: Dent clamp on current transformer unit.................................................................
55
Figure 5-3: Image of speedway revolution RFID reader with support for up to four antennas. .. 58
Figure 5-4: Photo of experimental layout used to determine the effect of distance and RF
shielding on RFID tag read rate .................................................................................................
61
Figure 5-5: Normal probability plots of read rate at a distance of l6feet from the small antenna.
.......................................................................................................................................................
62
Figure 5-6: Normal probability plots of read rate at a distance of 16feet from the large antenna.63
Figure 5-7: Depiction of incorrect installation of current transformer. .....................................
64
Figure 5-8: Depiction of correct installation of current transformer. .......................................
65
Figure 5-9: Plots of varying RFID antenna field comparing metal backed and plastic backed tags.
.......................................................................................................................................................
10
66
Figure 5-10: Photos taken in Waters' machining center of excellence CNC turning department.67
Figure 6-1: Map of the Waters CNC Turning department indicating data collection locations... 69
Figure 6-2: Map of the Waters CNC Milling department indicating data collection locations.... 69
Figure 6-3: Plot of CNC Turning M1309 power monitoring data (7/16-7/18)..........................
70
Figure 6-4: Plot of CNC Turning M1309 RFID data (7/16-7/18). ...........................................
71
Figure 6-5: Plot of CNC Turning M1309 power monitoring and RFID data (7/16-7/18)........ 72
Figure 6-6: Plot of CNC Turning M1309 power monitoring data (7/21-7/22)..........................
74
Figure 6-7: Plot of CNC Turning M1309 RFID data (7/21-7/22). ...........................................
75
Figure 6-8: Plot of CNC Turning M1309 power monitoring and RFID data (7/21-7/22)........ 75
Figure 7-1: Cost comparison between present data collection system and proposed data
collection system versus time ..................................................................................................
81
Figure 8-1: Sample statistics of the generalized average lot cycle time vector........................
85
Figure 8-2: Sample statistics of the generalized average state dependent setup matrix........... 85
11
List of Tables
Table 2-1: List of functional requirements from Waters with relative priority level. ............... 26
Table 4-1: Weighted decision matrix used to compare various data collection methods......... 50
Table 4-2: AtlasRFID comparison of RFID and bar code........................................................
52
Table 5-1: Example of raw current data. ..................................................................................
56
Table 5-2: Example of raw current data manipulated to output power and utilization. ............ 56
Table 5-3: Sample RFID system data in raw form. ..................................................................
59
Table 5-4: Sample RFID system data in manipulated form using Microsoft Excel..................
59
Table 6-1: Comparison of SAP, Filemaker, and Experimental part cycle time data................ 73
Table 6-2: M1309 machine utilization rates using various power thresholds (7/16-7/18)..... 73
Table 6-3: Comparison of SAP, Filemaker, and Experimental setup time data. ......................
76
Table 6-4: M1309 machine utilization rates using various power thresholds (7/21-7/22)..... 76
Table 6-5: Example CNC Milling RFID data indicating that machinists did not follow the data
collection process plan..................................................................................................................
12
78
Definition of Key Terms
Note: manufacturing environments tend to use these terms to refer to different concepts, so
these definitions should be considered only within the context of this thesis. These key
terms and definitions are used verbatim from Chandar [1].
Bottleneck- Machine with the lowest production rate in a production process, limiting the
production rate of the process as a whole.
Capacity- Available hours for manufacturing operations, including time for setup, operations,
maintenance and repair. Total capacity is the sum of capacity of each machine in a department.
For the purposes of this thesis, the capacity for each machine considered is 135 hours per week.
Cycle Time- Average length of time between the completion of two successive units in a process.
Lot/Batch- Both refer to a group of parts being produced together, and may be used
interchangeably for the purposes of this thesis.
Machining Time/Process Time/Operation Time/Run Time- All refer to the total amount of time
required to complete a lot or batch of parts in a production process, including productive
operations but excluding setup time.
Machine Utilization- Percentage of time that a machine is performing productive operations.
ManufacturingLead Time- Total time from the moment an order is placed until it is delivered to
the customer. Depending on the department, the "customer" may refer to the product's end user,
the distribution department, the finished goods inventory buffer or the next department in a part's
production process.
Part- A single unit produced during a production process.
Part Family- A group of part types with similar setups and process plans. The setup time of
transitioning from one part type in a family to another is much less than that of transitioning to
non-family part types in the system.
Part Type- A set of (theoretically) identical parts repeatedly produced over time, referred to with
the same part material number or SKU number.
Productive Hours- Time spent performing productive operations, such as cutting, polishing or
finishing. Excludes hours devoted to setup, maintenance and repair of machines.
Setup- Set of steps taken to prepare a machine for a production run, such as a tooling change,
fixture change, material change or machine calibration. Unlike productive hours, setup hours can
be considered fixed with respect to lot size.
13
Stock-Keeping Unit (SKU)- Waters Corporation terminology referring to independent part types,
used interchangeably with "part type" in this thesis.
Utilization- Fraction of available time that parts are being produced. For the purposes of this
thesis, this is measured by fraction of available time that a machine is up and its spindle is
drawing power.
14
Chapter 1
1.1
Introduction
Statement of purpose
The purpose of this thesis is to demonstrate the value of implementing a novel, accurate
automated data collection system designed to capture, in real time, the data necessary to optimize
lot sizes and supermarkets for high-volume parts in a manufacturing environment, and to
describe a practical method for doing so. This thesis enumerates the financial benefits of an
accurate automated power monitoring and auto RFID data collection system and provides a
practical guide for manufacturing systems engineers to use during implementation. The rest of
this chapter was written in collaboration with Arjun Chandar [1] and Gregory Puszko [2].
This thesis is based upon a team project conducted by the author, Arjun Chandar [1] and
Gregory Puszko [2] in collaboration with the Massachusetts Institute of Technology and Waters
Corporation in Milford, Massachusetts, between February and August of 2014. As part of its
operation,
Waters
Corporation
manufactures
high-precision,
high-performance
liquid
chromatography parts and part families at its Machining Center in Milford. In 2013, the Milford
facility reported approximately $18
million worth of internal accounting credits from
manufacturing. The heads of global manufacturing and finance set 2014 production targets for
the Milford manufacturing facility to approximately $21 million of internal accounting credits
from manufacturing. The purpose of the project was to determine, implement, and test a scalable
method to increase productivity or otherwise provide value to Waters through manufacturing
system improvements. Furthermore, Waters management sought a continuous improvement plan
to increase the Milford manufacturing facility's reported internal accounting credits from $18
million to $21 million. These objectives were achieved through a series of improvements in the
two primary upstream departments in the facility: CNC turning and CNC milling. The
improvements focused on reducing total setup time through optimized lot sizing [1], a
supermarket [1], and improved scheduling policies [2] using an accurate automated data
collection system to compare results.
Lastly, the author suggests several additional uses of the machine utilization, average
cycle time, and average state dependent setup time data that is captured using an accurate
automated data collection system outside of the scope of the team project.
15
1.2
Background information about Waters Corporation
Waters Corporation is an analytical instruments company that develops test equipment
used in pharmaceutical, industrial, and academic research laboratories. Their two main product
divisions are their biochemical and chemical analysis division, based in Milford, Massachusetts,
and their physical testing division, based in Manchester, England and Wexford, Ireland. The
biochemical and chemical analysis division produces liquid chromatography instruments, which
comprise about a $6 billion global market and are the largest source of revenue for Waters.
Additionally, Waters develops high-end mass spectrometry instruments. The physical testing
division produces thermal analysis, rheology and calorimetric instruments. Each division
develops and manufactures the stand-alone products as well as all the consumables, chemicals,
and accessories to feed or support their particular instruments. Waters also maintains a global
support network of authorized service centers around the world that manage local installation
services, training, technical support, repair, and replacement part services.
This thesis describes the development of a passive, scalable data collection system used
to monitor the manufacturing system changes made by Chandar [1] and Puszko [2] to improve
the productive output of the Machining Center at the chemical analysis division based in
Milford, Massachusetts.
1.3
Waters' expansion into contract manufacturing
Before 2001, 100% of the production and 100% of the assembly operations for products
in the chemical analysis division were done in the Milford facility. In the face of rising demand
in 2001, Waters began expanding and outsourcing their production of components to local and
overseas contract manufacturers, primarily in Singapore. This expansion, however, was done
without downsizing the production capacity or manufacturing staff in the Milford facility. The
primary objective of the outsourcing strategy was to leverage contract manufacturers to
manufacture and complete the less critical, low-value-added components and operations.
Simultaneously, the Machining Center in Milford would focus on the critical, high-precision,
high-value-added parts and operations in order to maintain a strong engineering knowledge base
and protect process trade secrets. This strategy was pursued by Waters in order to increase the
total capacity of the chemical analysis division and generate the supply base necessary to meet
16
growing demand for new products, while still maintaining the same quality standards and control
over the various manufacturing processes.
Today, 85% of production is done by local and
overseas contract manufacturers, while 15% is done in Milford. Since 2006, Waters has realized
a growth of nearly 1000% in sales revenue in the chemical analysis division, owing successful
product delivery to this expansion into contract manufacturing. The Milford facility still
performs most of the assembly steps for their products, including all major assembly operations.
The Machining Center in Milford focuses its operations on approximately 1500 distinct
products (internally referred to as stock-keeping units or SKUs). Given the variety of production
equipment, fixtures, tools, and trained machinists that Waters has in the Milford facility, the
company has the ability to produce most of the components used in its products that are currently
fabricated by contract manufacturers. Parts are selected to be made in-house or by a contract
manufacturer based on the cost to produce the part, the quality/tolerances required for the part,
the raw material (all parts produced in the Milford facility are metal), and the current stage of the
product life cycle of the instrument using the part.
Under Waters' current production system, few pieces of machinery in Milford are
dedicated to a single product or family because there is a constant shifting of components from
buy (having the component produced by the contract manufacturers and then purchased by
Waters), to make (producing the component in-house at the Milford facility), and vice versa.
The decision to shift from buy-to-make or make-to-buy is predicated on a number of important
factors including product life cycle stage and geometric complexity. For example, a new product
line that has grown out of its initial release production and requires higher production volumes to
meet demand typically shifts from make-to-buy. Additionally, when management deems a part
less costly to outsource due to its uniqueness to Waters, the decision is made to shift from maketo-buy. On the other hand, a product at the end of its life cycle typically shifts from buy-to-make;
in other words, the production of the specific part type is insourced and production planning
adheres to make-to-order inventory policy. Furthermore, a product which experiences quality
issues by a contract manufacturer induces the Waters manufacturing engineering teams to
investigate the root cause of the problem by taking control of its production; shifting from buyto-make.
17
Since management at Waters holds a fiduciary responsibility to increase the revenue per
share for the organization as a whole, management at Waters prioritizes increasing the output of
the Milford machining center, leveraging its ability to shift from buy-to-make for high gross
margin SKUs in order to do so. As described in section 1.1, Waters wants to increase the
finished-goods output of the Milford facility by 18% from 2013 to 2014 while, at minimum,
maintaining the present on-time delivery rate. Given the complexity of hiring and training skilled
machinists, Waters desires to achieve this goal without increases in direct labor cost.
Management believes that the increase in finished goods production from the machining center
will be realized through a combination of increased demand for liquid chromatography products
and shifting of products from contract manufacturers to the Milford facility. The desire for this
shift forms the basis for the productivity improvement goal specified at the outset of section 1.1.
1.4
Waters' machining center of excellence
The Waters advanced manufacturing center houses a 50,000 sq. ft. "machining center of
excellence" which produces 2.7 million parts annually covering 28,000 SKUs, a 29,000 sq. ft.
advanced instrument assembly and accessory kitting operation facility which produces over
130,000 finished good assembles, spare parts, and accessory kits, and an 8,500 sq. ft. class
10,000 clean room for optics, micro valves, and critical clean parts. This thesis focuses on the
operations of the machining center of excellence (also referred to as the "machining center"),
which produces precision-machined metal components for the final assembly of instruments and
consumables.
The photo in Figure 1-1 is of the column cell and model shop, which comprises about
one-third of the total machine shop space. The machining center currently operates 24 hours per
day, 6 days per week, with its baseline standard for full machine utilization being 22.5 hours per
day, 6 days per week. The machining center is divided up in a job shop format, where machines
that are of the same type (lathes, NC mills, lapping machines, etc.) or produce a unique family of
components (i.e. a certain line of consumables for an instrument) are grouped together.
The
main departments in the job shop are CNC Turning, CNC Milling, the Valve Cell, and the
Column Cell. A majority of parts begin their fabrication life cycle in the CNC Turning or CNC
Milling departments. The valve cell exclusively produces a line of both selector and check valves
used in the pumps for the liquid chromatography instruments. Production machines found in the
18
valve cell consist of lathes, mills, EDM wire drillers, and lapping machines. The column cell
exclusively produces a line of consumables for the liquid chromatography instruments.
Production machines found in the column cell consist of specialized Swiss style CNC lathes with
automatic long-stock feeders.
Figure 1-1: The Waters machining center of excellence.
Each of these departments has its own utility area. Each utility area consists of deburring
machines and simple cleaning machines used to perform secondary operations on parts produced
in the department. The machining center also houses a model shop which maintains its own CNC
and manual mills and lathes, a fused deposition modeling 3D printer, and micro-machining
capabilities. The model shop is primarily utilized by the New Product Integration Division in
Waters to prototype parts currently undergoing development, and is not used for the production
of high volume (>100 parts annually) products for customers or high volume fmished goods.
19
41
rvm1
~~
I..,
Th~~a*~t
wIN
Figure 1-2: Map of Machining Center. 1 = CNC milling, 2 = CNC turning, 3 = valve cell, 4= column cell, 5= model shop.
20
Each department is further segregated into work centers which consist of one or more
machines. When performing an operation, undergoing a setup, or undergoing a teardown, each
machine is staffed by one machinist. Each machinist is trained to run any machine within his or
her work center. Each division has a section leader who is responsible for the direct supervision
of the machinists in each department. Section leaders have the same baseline duties as
machinists, however, the section leaders are additionally responsible for developing the
production schedule for their department on a day-to-day basis, assigning each job to a particular
machine and machinist in the department, and running debrief meetings at the end of each shift.
Section leaders base production scheduling decisions on the demand requirements of the
Schedule and Planning (SAP) system. Each department has one or two section leaders working
per shift. Department supervisors directly manage the section leaders and are responsible for
managing the operations of their specific department and ensuring that production is on schedule.
The supervisors are not tasked with machining, but work directly with the machinists and section
leaders on a day-to-day basis.
1.5
Typical flow of parts and production at Waters
A majority of parts produced by the Machining Center first go through the turning
department, so consider the example of a typical part flowing through turning. The Schedule and
Planning (SAP) system Waters uses plans out the production schedule for all parts produced by
the machining center on a quarterly basis.
For a typical make-to-stock part, production is
triggered by the SAP system when the inventory level of the part is expected to drop below some
minimum value based upon expected future demand. On the other hand, a production order is
triggered for make-to-order part when an order is placed into the system by a customer. The SAP
system then triggers the creation of a production job which is physically represented by a paper
routing sheet. This routing sheet indicates which specific SKU needs to be made, the quantity of
parts to be made, the start date and end date of the job necessary to deliver on-time, and all of the
operations (including fabrication and inspection) the part must undergo before being completed.
When the job first begins, the physical routing sheet will be delivered to the department
where the first operation takes place. The section leader will take all the process sheets at the
beginning of the day and determine which parts should be completed on each machine, with
which operator, and in what order.
When production of a particular part is to begin, the
21
machinist will take the routing sheet, go to his or her assigned machine, and set up the machine
for the particular job. Standard setup times available in SAP indicate that setups can take
anywhere between 10 minutes and 8 hours.
The machinist then collects the necessary raw material for the job from the raw materials
inventory location. In the case of CNC turning, the inventory location is positioned in a central
location. Once the machinist has collected the raw material and returned to the machine, the raw
material is loaded into the machine and the machine is programmed to begin the operation. A
job or machine may need constant supervision or not; this is usually dependent on the raw
material that is placed into the machine. For example, some machines that accept long stock will
utilize an automatic feeder so that the machine will output the finished part when it is complete,
and then autonomously reset the stock into the chuck and begin processing the next part in the
lot. On the other hand, pre-cut blanks have to be individually loaded into the chuck and then
unloaded after the operation is finished. Typically, if a machine or job does not require constant
supervision, the machinist will use this time to set up or perform basic maintenance on other
machines within the department.
The machinists are tasked with inspecting the finished parts coming off the machine and
determining which are good ("conforming") and which do not meet the specification
("nonconforming" or "scrap" parts). The machinist then indicates the number of good and bad
parts on the setup sheet. The nonconforming parts are marked as scrap. The supervisor then
inspects the red-tagged parts in order to determine whether or not they are acceptable, need to be
reworked, or must be abandoned. In many cases, parts are marked as scrap due to surface
roughness or imperfections. Depending on the part type and machine, the post-process inspection
by the machinist will take place either after each part comes out of the machine or will take place
once the entire batch is complete. Along with this basic inspection, most parts also undergo a
critical inspection at some point during their production cycle.
After a batch is complete, the machinist delivers the parts and the routing sheet to the
utility area within the department. In self-contained departments such as the valve cell or column
cell, the operators themselves are responsible for delivering batches to downstream operations
since these departments do not have a utility area. However, for the higher-volume, higher-mix
areas of turning and milling, the only person who moves the parts out of the utility area is the
22
utility operator. The utility operator is in charge of the utility area for the particular department,
responsible for inputting the completed operation statistics (how many good and scrap parts were
produced) into the SAP system.
Once the completed operation statistics have been entered into SAP, the routing sheet
indicates whether or not the part needs to undergo a secondary operation such as a deburring
process or a simple clean process. If so, the utility operator will perform these operations and
then deliver the finished parts and routing sheet from the utility area to the next department
where they sit in an incoming goods buffer. If a part does not need a secondary operation, then
the finished parts and the routing sheet will still be delivered by the utility operator from the
utility area to the next department where they sit in an incoming goods buffer. In this way, the
utility operator can be thought of and modeled as another machine in the production line (see
Figure 1-3). The routing sheets for those parts will then be placed into the stack of incoming
process sheets for that department, and the section leader will decide when the part should
continue its operations, on what machine, with what operator. This process continues until the
part is complete.
Figure 1-3: Map of operation steps for production process in CNC turning and CNC milling.
Consider initial operation, utility operator station, and downstream as three separate machines.
1.6
Issues uncovered during preliminary investigations
The generalized production process flow in Waters machining center described in Section
1.5 indicates that several roadblocks exist which may inhibit Waters' ability to achieve the
targeted goal of an 18% increase in productivity.
23
Issues arise even before an order is place as a result of inaccurate demand forecasting.
Inaccuracies in demand forecasting lead to either a surplus or a backlog of parts and raw material
inventory which ultimately leads to downstream day-to-day changes by the section leader to the
production schedule output from SAP. Since individual section leaders are ultimately responsible
for day-to-day prioritization of parts, machines and machinists, adjustments to the SAP schedule
will not be done in a uniform way since section leaders make decisions individually. These
decisions are subject solely to each section leaders' intuition rather than to written standard
protocols for optimizing production scheduling decisions. Over even a short term, in many cases
less than a week, this can result in considerable conflicts in the scheduling of parts.
Moreover, the sequential production of dissimilar parts can require long machine down
time in order to perform a fixture setup or tool setups. The result of excessive setups directly
limits the amount of time machines can be operating to produce parts. Having too many setups
over a given period of time directly reduces the total available productive hours.
As described in section 1.5 all parts in turning and milling must go through their
respective utility operator station prior to leaving for downstream operation. The utility operator
works an 8-hour shift, 5 days per week. Preliminary investigations reveal that Waters'
manufacturing system does not account for the bottleneck that is created by the utility operator
station due to the limited hours in the utility operators shift. Essentially this means that the
manufacturing system at Waters has a built-in minimum 16-hour delay for any part that passes
through CNC milling or CNC turning.
Additionally, SAP uses standard setup times and standard cycle times in order to generate
the production plan. These standard setup times and standard cycle times are updated when
Waters accounting department performs a manual cost roll. The cost roll entails sending
accounting staff to the machining center with a stop watch in order to capture a sample average
cycle time and a sample average setup time. Preliminary interviews with accounting staff
indicated that Waters performs these cost rolls once every five years. Due to limited accounting
staff labor capacity, the cost roll generalizes the standard cycle and setup times based upon
similarity to other processes. This suggests that the data used by SAP to plan production is not
only out of date, but is also prone to inaccuracies caused by human error. Furthermore, the
24
standard average setup times in SAP do not account for state dependency. For more information
regarding state dependent setups, refer to section 2.1.3.
1.7
Outline
Chapter 2 details the preliminary investigations by Chandar [1], Puszko [2] and Perez
describes data collection issues uncovered. Chapter 3 presents a novel method for processing
passively collected part location and machine state data to yield manufacturing statistics for the
work by Chandar [1] and Puszko [2]. Chapter 4 describes the methodology used to determine the
combined data collection system architecture. Chapter 5 details the process of implementing
commercially available current monitoring and auto RFID systems in order to serve as guide to
replicate the data collection system. Chapter 6 discusses the data passively collected to monitor
the experiments conducted by Chandar [1] and Puszko [2]. Chapter 7 describes the value of
implementing a passive current monitoring and auto RFID data collection system. Chapter 8
provides a roadmap of future work for both Waters Corporation and researchers.
25
Chapter 2 Problem statement
The present data collection system in place at Waters Corporation is insufficient for the
needs of management and for the purposes of the team project. This chapter describes the
manufacturing statistics that Waters manufacturing staff currently capture and use during the
course of normal business in order to plan production. Additionally, this chapter details the
shortcomings of the present data collection system used by Waters. Management at Waters
imposed several core functional requirements for any system improvements proposed by the
team. Table 2-1 below indicates the core functional requirements as well as the relative priority
level.
Table 2-1: List of functional requirements from Waters with relative priority level.
No new machines/floor space
1
Limit line downtime during implementation
1
Payoff; demonstrate break even point,
NPV, cost of capital
Low risk profile
1
System-wide, scalable, expandable
2
Management: $18M-+$21M 2014 goal
2
Reproducible when MIT gone
3
Fits culture
3
(~l18% capacity Increase)
2.1
Manufacturing data presently collected
Presently Waters machining center supervisors capture various manufacturing systems
statistics that are useful for both production planning and monitoring the present state of the
manufacturing system. These statistics are used by the department supervisors to make long term
capacity planning decisions and to make capital equipment expenditure decisions. Presently, the
machining center captures average lot cycle time, machine spindle time, scrap rates, and average
setup time. For the purposes of this thesis the author will not comment heavily on scrap rate
26
...
..
....
...
.
.. .... ..
..
statistics because issues that arise which pertain to scrap are primarily driven by manufacturing
process issues (tool wear, operator error etc.) rather than manufacturing systems issues. This
section describes how Waters machining center captures lot cycle time, machine spindle time,
and setup time. In general, the manufacturing systems statistics captured by Waters are
inaccurate over time and costly to collect.
2.1.1
Lot cycle time
Waters machining center uses two systems to record lot cycle time data. One system,
described in section 1.4, is SAP. The primary issue with the use of SAP as a data collection
system is that it does not enable the user (machinist, utility operator, etc.) to input actual data.
The SAP system used in Waters machining center solely enables the user to designate a lot of
parts as complete in the database. When the department supervisors extract lot cycle time data
from SAP, the system merely outputs the standard lot cycle time and the time that the lot was
entered into SAP as complete. All lot cycle time data in SAP is likely out of date and prone to
inaccuracies caused by human error because the standards are set using the cost roll procedure
described in section 1.6.
In an effort to circumvent the issues created by using SAP as a database, Waters
machining center staff have adopted another system call Filemaker. This system enables
machinists and operators to capture real-time lot cycle time data rather than relying on the
standards set in SAP. This system, however, is not without its own flaws. The manufacturing
system statistics found within the Filemaker database are equally as prone to human error as the
data found in SAP. The primary benefit of information collected in the Filemaker database over
the SAP data base is that the values are based upon recent recordings by machinists in the case of
the former as opposed to the cost roll statistics in the case of the latter. Based upon the
limitations of both systems, the manufacturing systems engineer should conclude that neither
database system is comprehensive nor accurate enough for Waters' needs.
2.1.2
Machine spindle time
Each CNC machine at Waters records spindle time data. Spindle time is defined by each
CNC machine tool manufacturer. As a result, the data captured at Waters is not recorded using a
standard reporting language. Therefore, making a comparison between machine spindle time
27
data is not meaningful. In recent years since the expansion to contract manufacturing, Waters
machining center has invested heavily in capital equipment from various suppliers. In order to
make smart capital equipment purchasing decisions based upon capacity, machining center
management began to capture machine spindle time. Preliminary investigations revealed that the
rationale for capturing machine spindle time data is to determine the utilization of each machine.
In order to capture the spindle time data from these automated machines, the machinist or
operator is required to put the machine into an idle state. Ironically, in order for the machining
center management to determine the machine utilization, the machine must be put into a state
where it cannot be utilized.
As stated above, many of the machines found in the machining center come from various
machine tool manufacturers and suppliers. Each machine has its own unique user interface,
programming language, and data tracking systems. Investigations reveal that each machine has
its own unique method to calculate and output spindle time. Due to the fact that a standard
reporting language for spindle time does not exist across the various machines, it is not
reasonable to compare spindle time, and therefore utilization values, between differing machine
types.
2.1.3
Setup time
Waters machining center uses two systems to record setup time data. One system,
described in section 1.4, is SAP. The primary issue with the use of SAP as a data collection
system is that it does not enable the user (machinist, utility operator, etc.) to input actual data.
When the department supervisors extract setup time data from SAP the system merely outputs
the standard setup time and the time that the lot was entered into SAP as complete. Therefore, all
setup time data in SAP is likely out of date and prone to inaccuracies caused by human error
because the standards are set using the cost roll procedure described in section 1.6.
In an effort to circumvent the issues created by using SAP as a database, Waters
machining center staff have adopted another system call Filemaker. This system enables
machinists and operators to capture real-time setup time data rather than relying on the standards
set in SAP. This system, however, is not without its own flaws. The manufacturing system
statistics found within the Filemaker database are as prone to human error as the data found in
SAP. The primary benefit of information collected in the Filemaker database over the SAP data
28
...............................................
....
base is that the values are based upon recent recordings by machinists in the case of the former as
opposed to the cost roll statistics in the case of the latter.
Presently, Waters captures state independent average setup times. The set of state
independent average setup time values can be simply expressed as a column vector. Figure 2-1
below represents the state independent average setup time vector, where & is the average state
independent setup time and k is the number of part types.
Si
S2
0
Sk-1
Sk
Figure 2-1: State independent setup time vector.
Under this data collection structure each value in the vector represents the collected
average state independent setup time for that part type. This data set is useful for a limited
number of cases where all part types have approximately equal state dependent setup times. In a
general sense, the setup time from 1 -> 2 -+ 3 a 1 -+ 3 -> 2. The manufacturing systems
engineer and production planner who aim to reduce machine idle time from setups must fully
understand the state dependence of their setups in order to make well informed production
planning decisions described by Puszko [2].
Figure 2-2 below represents the state dependent average setup time matrix in a general
form. Where s is the average state independent setup time and k is the number of part types. The
subscripts represent the previous setup state and the future setup state of the machine. For
example, S1,k represents the amount of time it takes to setup the machine from part type 1 to
part type k. The benefits of using state dependent setup times during production planning, as
29
opposed to state independent setup times, are enumerated by Pinedo [3] and discussed by Puszko
[2].
S1,1
S1, 2
S 2,1
S 2 ,2
S1,(k-1)
S2,k
S(k-1),l
Sk,1
S1,k
Sk,2
S(k-1),(k-1)
S(k-1),k
Sk,(k-1)
Sk,k
Figure 2-2: Generalized state dependent setup time matrix.
2.2
Issues with present data collection system
As mentioned previously, the author has discovered and described several issues with the
present data collection system used in Waters machining center. The presently collected data and
the associated issues have three common themes. These themes are the cost of active data
collection for Waters Corporation, the lack of efficacy in the accuracy of the data collected, and
the minimum resolution of the data collected. Refer to Chandar [1] and Puszko [2] for a
description of the short comings of SAP related to lot sizing and production planning.
2.2.1
Financial impact
The primary costs of the present data collection system can be directly attributed to
machine value per hour and operator cost per hour. As described in section 2.2.2, the machines
must be put into an idle, non-value added, state in order to capture the spindle time. Recall from
section 1.4 that there are 5 distinct machining groups at Waters. In order to estimate the cost of
capturing spindle time data, assume 4 machinists spend 30 minutes per day capturing spindle
uptime data from all machines in their section. Preliminary interviews with Waters accounting
staff indicate that on average each machine in the machining center costs $67/hour to operate;
however, the average value of machine is conservatively estimated by Waters' accounting
30
department to be four times greater than the operating cost. Therefore, we assume that each
machine produces $250/hour in productive output when performing value added operations. In
addition to the cost and value of the machine, a machinist must be present to record the data. The
average wage rate for machinists is $25 per hour; however, the true cost to Waters includes
fringe benefits and payroll taxes. Therefore, the average operator cost is conservatively estimated
by Waters' accounting department to be $40/hour.
Spindle data cost =
$250 value * .5hours +
hour
Spindle data cost
day
= $12
day
+
4 machinist *
day
$40 wage * .5 hours
hour
day
= $205 per day
(1)
(2)
Preliminary investigations indicate that, on average, it takes 1 minute per lot to record lot
cycle time and setup time data in Filemaker. On an average 24 hour day, 60 lots pass through the
factory. In order to determine the total cost of capturing cycle time and setup time data, multiply
the wage rate by the time taken to record data per lot by the number of lots per day.
Cycle/Setup time data cost =
$30 wage * 1 hour * 60 lots
hour
60 lots
day
Cycle / Setup time data cost = $30 per day
(3)
(4)
In order to determine the total financial impact of capturing spindle time, cycle time, and
setup time, add the spindle data cost per day and cycle/setup time cost per day.
Total daily data collection cost = spindle + cycle / setup = $235 per day
Total annual data collection cost =
2.2.2
$
day
* 365 days
year
$85,775
year
(5)
(6)
Efficacy in accuracy
When considering the accuracy of human collected data, the manufacturing systems
engineer must question the efficacy of the data. As described in sections 2.1.1 and 2.1.3 the
manufacturing systems engineer has low confidence in the accuracy of data output by SAP when
reporting standard setup times and standard cycle times. The reasons for this lack of confidence
in SAP data is two-fold. Firstly, the standard setup times and cycle times are recorded manually
by several different humans. As with any human data collection system there is not only the risk
that a single human deviates from his or her normal behavior, but the more common risk that
31
each individual human behaves uniquely. Secondly, these data in SAP can be up to five years out
of date and do not reflect any recent process changes that may have improved or diminished the
mean value of a specific cycle time or setup time.
The Filemaker data collection system is similar to SAP in that the manufacturing
engineer must question the accuracy of the data because of the human element required to
capture data. That is to say that the data collected in the Filemaker system is not only susceptible
to deviations by a single operator, but also at risk from operator-to-operator deviations.
As described in section 2.1.2, the manufacturing systems engineer must question the
interoperability and comparability of spindle time data collected from various production
machines. Simply put, machine type 1 does not speak the same language as machine type 2.
However, Waters uses these data assuming that the values are similar in form in order to
determine the overall utilization of the factory.
2.2.3
Data resolution
The final area of concern for the data presently captured by Waters is the resolution of the
data. The finest resolution of data found in SAP increments is at 30 minute intervals for both lot
cycle time and setup time. The resolution of the Filemaker data is on the order of 5 minute
minimum intervals. In the case of the spindle uptime data, the machines simply output a
percentage value which is the ratio of spindle uptime divided by the time period since the last
check. The final concern surrounding data resolution is that all of the data presently captured by
Waters is done so after the fact. This means that the data is not available in real time for
management to analyze without having machinists manually capture and report the data.
2.3
Problem statement
The work done by Chandar [1] to optimize lot sizes and Puszko [2] to reduce time spent
performing machine setups requires the use of accurate, high efficacy average part cycle time
and average state dependent setup time data. Additionally, management at Waters requires
accurate, high efficacy machine utilization data in order to make capacity planning and capital
equipment purchasing decisions. Waters' present data collection system is designed to capture
lot cycle time, state independent setup time, and machine spindle uptime. Therefore, the present
32
data collection system in place at Waters Corporation is insufficient for the needs of management
and for the purposes of the team project.
Chapter 3 Data collection concept overview
This chapter outlines the data collection system requirements of Waters Corporation in
the context of both normal day-to-day operations and the project of Chandar [1], Puszko [2] and
Perez. In this chapter, the author proposes a novel method to automatically capture and process
manufacturing data in order to determine the required manufacturing systems statistics in real
time. The graphical approach and analytical method presented in this chapter can be used to
determine the manufacturing systems statistics required by Chandar [1] and Puszko [2]. The data
collection method is useful to a manufacturing organization that would benefit from using these
manufacturing systems statistics to improve production planning and increase factory throughput
in order to avoid the costs described in section 2.2.
3.1
Manufacturing data collection requirements
Waters presently collects data on average lot cycle time, average state independent setup
time, and spindle uptime. In order to continuously improve the production planning in the
machining center, the manufacturing systems engineer know with average cycle times, average
state dependent setup times, and machine utilizations.
In a generalized form the set of average cycle time values can be represented as a column
vector. Figure 3-1 below represents the generalized form of the average cycle time vector where
t
.. is the average cycle time for a specific part type and k represents the number of parts in
this set. According to Chandar [1], it is crucial to capture accurate real-time average cycle time
data in order to continually improve and optimize the lot sizing techniques used by Waters
management.
33
t2
tk..4
tk
Figure 3-1: Generalized column vector form of average cycle time for k part types.
Recall from section 2.1.3 that the generalized form the set of average state dependent
setup time values can be represented as a matrix. Figure 3-2 below represents the generalized
form of the average state dependent setup time matrix where T-- is the average setup time where
1 represents the previous part type made on the machine and 2 represents the part type to be
made next on the machine. It is true that the state dependent setup matrix is square and of size k.
AccordinF to Puszko [2], it is crucial to capture accurate real-time average state dependent setup
time data in order to continuously improve and optimize the production planning schedule used
by Waters management.
0
S1,2
S2,1
0
S1,(k.-1)
--
S2,k
0
0~
0
S(k-1),
Sk,
S1,k
Sk,2
Sk,(k-1)
(k-1),k
0
Figure 3-2: State dependent setup matrix applied to real system with k part types.
34
In the case of a real manufacturing system rather than a generalized manufacturing
system, it is always true that the square average state dependent setup time matrix has a diagonal
of zeroes. That is to say that it takes no time to set up from part type 1 to part type 1. Recall from
section 2.1.2 that Waters manufacturing center supervisors and management also capture spindle
time in order to determine the approximate machine utilization. Ideally utilization data would be
captured directly and in a standard format across all machine types.
3.2
Machine state and duration
As described in section 2.1.2 Waters currently captures machine spindle time data in
order to approximately determine the utilization of a specific machine. Utilization can be simply
thought of as the ratio of time spent performing operations to the specific period of time in
/
question. Figure 3-3 below depicts the three possible states of a machine: ON / UP, ON
DOWN, and OFF / DOWN.
Machine
State
ON / UP
ON / DOWN
Time it)
Figure 3-3: Possible machine states versus time.
A machine is considered to be in the ON / UP state when operations on a part occur. A
machine is considered to be in the ON / DOWN state when an operator performs a setup,
maintenance, or the machine is idle. Lastly, a machine is considered to be in the OFF / DOWN
state when the machine has experienced a failure or the machine is idle. In most cases it is
difficult to draw a distinction between the ON / DOWN state and the OFF / DOWN state.
35
/
Section 3.4 describes a combined data collection method used to distinguish between the ON
DOWN and OFF / DOWN states. When the specific state of a machine is plotted against time,
the manufacturing systems engineer is able to calculate the machine utilization by determining
the ratio of time spent in the ON / UP state and the time period under consideration. The
engineer can determine utilization using equation 7 below, where
in the ON / UP state, where
tOFF / DOWN
tON
/
DOWN
/ UP
is the total time spent
is the total time spent in the ON / DOWN state, and
is the total time spent in the OFF / DOWN state.
Utilization =
3.3
tON
tON
/
UP + tON
(7)
tON / UP
/ DOWN + tOFF / DOWN
Part location and duration
As described in sections 2.1.1 and 2.1.3 Waters currently captures average lot cycle time
data and average state independent setup time data in order to plan production. Lot cycle time is
the duration of time that a lot of parts spends at the machine while operations are performed.
State independent setup time data is the duration of time that it takes to setup a specific part type.
In both of these cases, the manufacturing systems engineer must determine for what period of
time an operator spends machining parts or performing a setup. Figure 3-4 below represents a
time series of process type data that has several possible states: downtime (D), lot cycle time (P),
and maintenance/setup (MIS).
D
P2
P1
P3
Time (t)
Figure 3-4: Example graph of part location and duration.
36
........
............
..
....
..
......
....
....
Note that for the information in the graph above to be useful to the manufacturing
systems engineer, the time duration data must specify the difference between each unique part
type, a maintenance protocol, a setup, and downtime. It is most practical to capture lot cycle time
by part type (tp), time duration of a maintenance protocol (SM), and time duration of a setup type
(ss). Under such a data collection paradigm, the author defines downtime
(tD)
as all other time
spent. By definition,
Total time spent = (tp) +
(SM)
+ (SS) + (tD)
(8)
Unless the data collection system has been designed to specifically differentiate between
setup time data and maintenance time data in addition to cycle time data, the manufacturing
systems engineer cannot confidently differentiate between maintenance and setup data. The
engineer can only make assumptions based upon the order of the data in order to differentiate
setups from maintenance. Therefore Figure 3-4 depicts the situation in which the manufacturing
system engineer only knows (sM/s). However, by definition, sM/s = sM + Ss.
3.4
Combined value of machine state and part location
This section presents a visual representation of the data collection methodology detailed
in section 3.3 in the context of a power monitoring system and auto RIFD system. The graphical
approach described in this section depicts the combined value of the power monitoring system
and the auto RFID system. It further details the analytical method necessary to extract the desired
manufacturing statistics described in section 3.1.
For the sake of clarity in explanation, the author describes the visual representation of the
data collection methodology in the context of a power monitoring system and an auto RFID
system. The reader should note that the data necessary to perform the analytical method
described in section 3.4.2 can be captured in a multitude of ways. Chapter 4 explores various
possible methods that can be used to capture these data and explains why the author selected the
combined power monitoring and auto RFID solution.
37
Graphical representation
3.4.1
Figure 3-5 below represents machine power consumption plotted against time. When a
power monitoring system is used to capture data in real time in a manufacturing system, the
peaks and valleys are sharp. Additionally, the local power maximum values vary continuously
during a production cycle because each unique stage in the manufacturing process consumes a
different amount of power. For sake of clarity and explanation the figure below has been
smoothed out. Refer to Figures 6-3 and 6-6 in order to see the plot of real data that support this
graphical theory. For more information on power monitoring refer to section 4.1.3.
Local power
maximum 2
ON/UP
Local power
maximum I
ON/UP
Machine
OFF / DOWN
Local power
minimum
STATE UNKNOWN
Power
(W)
20
2
1 2 3 4
3
.-
5
-
-
1
-----------------
Co)
#
Time (t)
Figure 3-5: Generalized graph of machine power consumption plotted against time.
Figure 3-5 above denotes four specific states of a machine that are necessary to identify
in order for the manufacturing systems engineer to capture the necessary manufacturing
statistics. The plot of power monitoring data can be used by the manufacturing systems engineer
to determine if a machine is operating and for how long. The easiest machine state to
characterize is the OFF / DOWN state. Since the power consumption is nearly zero, the
manufacturing systems engineer can conclude that the machine was off for the duration shown.
Again, the manufacturing systems engineer can conclude that during the periods in which
the machine power consumption achieves a local maximum that the machine is in the ON / UP
38
..............
......
........
state. In the case of Figure 3-5 above, the machine is considered in the ON / UP state for the time
periods marked 1-3 and 1-7. In the case of a simple manufacturing system where a machine is
dedicated to manufacture a single part type (i.e. one SKU), the manufacturing systems engineer
knows that time periods 1-3 and 1-7 correlate to the process time for the same part type. Issues
arise when considering cases where the machine produces multiple part types. In Figure 3-5
above the peaks within period 1-3 have a much longer duration than the peaks within period 1-7.
Furthermore, the peaks in period 1-7 have a higher maximum value than the peaks in period 1-3.
While the manufacturing systems engineer may conclude that these periods correlate to separate
part types, he or she cannot reach this conclusion with 100% confidence. For example, the
difference in duration and peak power consumption between periods 1-3 and 1-7 could arguably
be the result of a manufacturing process that is out of statistical control, the result of operator
error, or the result of random error.
Additionally, the manufacturing systems engineer experiences similar confidence issues
when determining the state of a machine during the observation of a local power consumption
minimum. If the engineer only observes the short time span between peaks 2 and 3 in Figure 3-5,
he or she will likely conclude that the machine is in the ON / DOWN state during that period.
However, when presented with the entire data set in Figure 3-5, the manufacturing systems
engineer loses confidence in his or her assessment. If the local power minimum is known to
occur immediately between two peaks, the engineers concludes that this time was spent
removing a finished part and installing raw material for back-to-back operation. Furthermore, the
reader should remain skeptical that horizontal lines in Figure 3-5 which represent the three
discrete machine states described in section 3.2 have been selected properly.
From the assessment of Figure 3-5 it should be realized that more information is needed
to conclusively determine the state of the machine in each time period as well as the operation
performed in each time period with 100% confidence. It is for this reason that the engineer must
know which operation occurs at each moment in time. Figure 3-6 below depicts a generalized
graph of process type versus time. The engineer can capture this data using a variety of data
collection methods detailed in section 4.2. In a general sense, the plot below represents the
amount of time that a specific part spends at a known location. It can be thought of as a method
39
of tracking inventory. The author will refer to this system as an auto RFID system (refer to
section 4.2.4 for more information about auto RFID).
[roces
Machine
Stwte
P2
MA
P
4.-
4.-
- -------------Time (t)
Figure 3-6: Generalized graph of process type versus time
As outlined in section 3.3, the reader knows that the auto RFID system monitors the
duration of the part type (i.e. P1, P2, P3) or process type (M/S) that is present at a known
location. Figure 3-7 below is a graphical representation of the combination of Figure 3-5
(generalized graph of machine power consumption plotted against time) and Figure 3-6
(generalized graph of process type versus time).
Using Figure 3-7, the engineer can both determine the average cycle time for each part
type and distinguish between part types P1, P2 and P3. Furthermore, the engineer can determine
the number of distinct parts, n, produced within a lot, by counting the number of peaks within
each production region. Additionally, the manufacturing systems engineer can confidently
determine the average state dependent setup time to go from P1 to P2. Note that if the auto RFID
system is not able to distinguish between a maintenance protocol (M) or a setup (S), then the
engineer cannot determine the average state dependent setup time. For example, during practical
implementation the manufacturing systems engineer can program unique RFID tags used to
distinguish between a setup and maintenance protocol. This is depicted in Figure 3-7 above as
the time span marked as M/S.
40
- 11",1...........
I...
....
...............
Process
Type
PI1
nj = 3
z= 7
2
2 3 4 5 6
P2
Power
(W)
I
Ma
I- ITMF-I*I-
:1
I!
Time (t)
Figure 3-7: Graphical representation of combination of generalized auto RFID and power monitoring data.
41
..........
........
....
......
.
I
3.4.2
Analytical approach and governing equations
This section presents several governing equations which enable the manufacturing
systems engineer to precisely compute the necessary manufacturing statistics that are described
graphically in Figure 3-7 in section 3.4.1. In order for the reader to assess the integrity of the
governing equations below, the author presents the following variable definitions:
t = cycle time
s = setup or maintenance time
n = number ofparts in lot
N = number of lots made or setups performed
k = number of uniquepart types
i= specific part type
j
= specific lot number or setup number
The average cycle time of a specific part type is the total amount of time spent producing
all parts of that type divided by the number of parts made of that type. Using the power
monitoring data set and auto RFID data set, equation 9 below enables the manufacturing systems
engineer to compute the average cycle time for part type i (Pi).
N tij
Average cycle time = Pi
= N
(9)
The average state dependent setup time of a specific setup type is the total amount of time
spent performing all setups of that type divided by the number of setups performed of that type.
Using the power monitoring data set and auto RFID data set, equation 10 below enables the
manufacturing systems engineer to compute the average state dependent setup time for setup
type a,b
(sa,b),
where a and b are any integers from 1 to k.
N
Average state dependent setup time from a to b = sa,b =
42
..........
....
....
..........
2:;=j0a~bj
(10)
The total uptime of a machine is the sum of the time spent producing all k part types.
Using the power monitoring data set and auto RFID data set, equation 11 below enables the
manufacturing systems engineer to compute the total uptime for the machine.
Up time = ZN
Zl
Planned down time (equation 12 below) is the sum of all time spent on machine setups
and machine maintenance.
Planned down time =
l t(M/S)j
(12)
Unplanned down time (equation 13 below) is the remainder of time not spent on part
operations, setups, or maintenance.
Unplanneddown time = Zj=1 tDj
(13)
Recall from section 3.3 the definition of down time (equation 14 below) as the sum of
planned down time and unplanned down time.
Down time = Unplanned + Planned =
X ..1
t(M/s)j + Zj=1 tDj
(14)
Recall from section 3.2 that utilization is the ratio between uptime and total time.
Equation 15 below enables the manufacturing systems engineer to compute machine utilization
using power monitoring and auto RFID data.
Up time
Utilization= Total
time
Up time
Down time+Up time
15)
Substituting equation 11 above for uptime and equation 14 for down time into equation
15 yields equation 16 below.
Utilization=
~
t1
j=i tM/Sj+ Xj 1 tDJ+Y21,]
(16)
tij
Equations 9 and 10 are the average values of a sample data set from the related
population. However, mean values alone are insufficient to fully characterize a manufacturing
system. Due to time limitations during the testing of the system described in chapter 5,
43
discussion of additional manufacturing statistics (sample cycle time variance and sample state
dependent setup time variance) is presented as future work in chapter 8.
44
Chapter 4 Methodology
The proposed novel method to automatically capture and process manufacturing data
detailed in chapter 3 was described assuming the use of power monitoring and auto RFID. In
general, there are many methods of data collection that enable the manufacturing systems
engineer to capture process type versus time data and machine state versus time data. This
chapter describes the rationale as to why the power monitoring and auto RFID systems were
implemented in Waters machining center of excellence. During the course of preliminary
investigations, Waters management enumerated several core functional requirements for the
project specified in chapter 2. The decisions made in this chapter were constrained by the core
functional requirements enumerated by Waters management. The manufacturing systems
engineer may use the method detailed in this chapter in order to select the best combined data
collection system for their specific application.
4.1
Methods to capture machine state and duration
This section contains various methods that Chandar [1], Puszko [2], and Perez believe
meet the basic requirements of the machine state data collection system described in chapter 3.
The subsections below define each method, describe the benefits, and explain the concerns raised
by Chandar [1], Puszko [2], and Perez.
4.1.1
Human/Manual
This method of data collection involves having a dedicated workforce to monitor and track the
state of each machine at all times. The only benefit of this system is that no complex data
collection system would be necessary; however, there are several drawbacks. The primary
drawback relates to the data accuracy detailed in section 2.2.2. Furthermore, in order to have a
high data collection sampling rate (within an order of magnitude of 1 Hertz) each machine would
require a full time person dedicated to capturing data. The cost to hire a workforce large enough
to effectively capture the needed data would outweigh the potential benefits of the data.
4.1.2
Computer vision
A computer vision data collection system involves the use of one or more cameras to
capture video data around the factory at all times. One of the benefits of a computer vision data
45
collection system is that it can, if designed properly, run autonomously with no human
interaction necessary on a day-to-day basis. Additionally, a computer vision system can also be
designed to capture part location and duration information. In essence it can capture all of the
information needed to compute the manufacturing statistics using the equations in section 3.4.2.
The main drawback of a computer vision data collection system is that expertise is
required in programming, machine learning, and computer graphics. Furthermore, once a system
has been designed and programmed that is able to recognize specific part types and machine
states it must then be trained by a human to differentiate all of the various possible combinations.
4.1.3
Power monitor
A power monitoring system is used to measure the amount of power consumed by a
machine over a period of time. The primary benefit of a power monitoring system is that once
installed the unit can capture data autonomously. Additionally, commercially available power
monitoring systems are able to capture data at rates up to 1 Hertz and with minimum resolution
on the order of 0.01 W [4]. As a secondary benefit, commercially available power monitoring
systems can be used with any machine that draws power from a source, meaning that the data
captured in a large scale manufacturing system would have a standard reporting language.
4.1.4
Machine statistics
The method of using machine reported statistics to monitor machine state is described in
section 2.1.2. This method outputs the percentage of time that the spindle of a machine tool is
spinning; however, this method does not provide high resolution data. It is infeasible to sample
this data collection system at a higher rate because of the cost to the manufacturing system.
Recall from section 2.2.1 that in order to capture spindle time data the machine must first enter
the ON / DOWN state resulting in a negative financial impact for the manufacturing company
due to the fact that the machine is unusable during the period.
4.2
Methods to capture part location and duration
This section contains various methods that Chandar [1], Puszko [2], and Perez believe
would meet the basic requirements of the part location data collection system described in
46
............
....
...
......
-
.
..........
....
....
..
..
..........
.. .....
..................
....
..
....
...
................
......
. .........
...
chapter 3. The subsections below define each method, describe the benefits, and explain the
concerns raised by Chandar [1], Puszko [2], and Perez.
4.2.1
Human/Manual
This method of data collection involves having a dedicated workforce to monitor and
track the type of part or process at each machine at all times. The only benefit of this system
would be that no complex data collection system would be necessary, however, there are several
drawbacks. The primary draw back relates to the efficacy in data accuracy detailed in section
2.2.2. Furthermore, in order to have a high data collection sampling rate (within an order of
magnitude of 1 Hertz) each machine would require a full time person dedicated to capturing data.
The cost to hire a workforce large enough to effectively capture the needed data would outweigh
the potential benefits of the data.
4.2.2
Bar code
A bar code system uses a scanner and a bar code in order to capture the location of a
specific part type or process. The primary benefit of commercially available bar code scanners is
that they have been well characterized. The main drawback of a barcode system is that in order
to capture information using a bar code system, the manufacturing systems engineer must rely on
human operators to remember to scan the object. In other words, the accuracy of a bar code
system is only as high as the accuracy of a human data collection system.
4.2.3
Computer vision
A computer vision data collection system involves the use of one or more cameras to
capture video data around the factory at all times. One of the benefits of a computer vision data
collection system is that it can, if designed properly, run autonomously with no human
interaction necessary on a day-to-day basis. Additionally, a computer vision system can also be
designed to capture machine state information. In essence it can capture all of the information
needed to compute the manufacturing statistics using the equations in section 3.4.2.
The main drawback of a computer vision data collection system is that expertise is
required in programming, machine learning, and computer graphics. Furthermore, once a system
47
has been designed and programmed that is able to recognize specific part types and machine
states it must then be trained by a human to differentiate all of the various possible combinations.
4.2.4
Auto RFID
Auto RFID (Radio Erequency Identification) systems are commercially available data
collection systems most commonly used to track inventory. Figure 4-1 below depicts the
elements necessary for all auto RFID systems. Each auto RFID system has a tag, an antenna, a
reader, and a computer. The tag stores all of the SKU or process specific data that is necessary to
differentiate one tag from another. The antenna sends low energy radio frequency signals that are
used to detect if a tag is present within the signal field. Each antenna is capable of detecting the
presence of multiple tags at a time. The reader is programmed to control the radio frequency
signals output through the antenna and to record the tag ID of each tag within the signal field.
The computer stores and processes the data captured by the reader. The primary benefit of an
auto RFID system is that it is simple to program, install, and run autonomously. The primary
drawback of an auto RFID system is that radio frequency signals are subject to noise in a highly
metallic environment.
Readeror
Interrogator
'00
Antenna
Tag or
Transponder
48
.......
.....
.........
.........
.
..
..
..
Figure 4-1: Typical auto RFLD system. [5]
4.2.5
Pressure pads
A pressure pad data collection system is analogous to a scale. This data collection system
would measure the weight of each tray of parts at a known location. The primary drawback of
this data collection system is that it requires a significant amount of calibration. Even if the
weight of each tray of parts is known in the system, it is very difficult to predict how this
changes over the course of a specific machining operation (i.e. a part gets lighter after it has been
machined).
4.2.6
Photogate/opties
A photogate/optical data collection system is similar in concept to a computer vision
system. A photogate/optical data collection system uses a laser and receiver to detect if an object
has crossed the beam of the laser. The difference between a photogate/optical data collection
system and a computer vision system is that it cannot be trained to distinguish the difference
between various part types. This data collection system would be exclusively used to determine
if a tray of parts is at a machine or not.
4.3
Weighted decision making and system selection
Table 4-1 below contains all of the data collection methods described in sections 4.1 and
4.2 in the leftmost column. The top row of the table indicates the selection criteria used to select
a system to implement. These selection criteria were selected based upon the functional
requirements enumerated by Waters management at the outset of the project. The manufacturing
systems engineer may use the method detailed in this chapter in order to select the best system
for their specific application.
It is worthwhile to note that even though the author did not select the computer vision
system due to implementation challenges, it stands as the only automated data collection system
that can capture both part location and machine state data. The rightmost column of table 4-1
above indicates that the power monitoring system is the best machine state data collection
method. On the other hand, the best data collection for part location is tied between auto RFID
and bar code.
49
According to AtlasRFID [6], one of the leading suppliers of commercial REID solutions,
"one advantage of RFID is that the technology doesn't require line of sight. RFID tags can be
read as long as they are within range of a reader. Barcodes have other shortcomings as well. If a
label is ripped or soiled or has fallen off, there is no way to scan the item, and standard barcodes
identify only the manufacturer and product, not the unique item."
Table 4-1: Weighted decision matrix used to compare various data collection methods.
Cost
Passive
SAP
ntegration
Flexibility/
Robustness
Real time
Total
0
0
0
0
0
0
0
-
+
+
0
0
+
0
+3
+
+
+
-
+
-
-
+
+2
RFID
+
-
0
+
+
-
+
+
+3
Pressure pads
+
-
+
-
+
-
0
+
+1
Power monitor
-
+
+
+
+
-
+
+
+4
Machine stats
-
+
-
0
-
0
0
-
-3
Photogate/ optics
+
-
+
0
+
-
-
+
+1
None
-
-
-
+
+
0
-
-
-3
Data Collection
Methods
Part
location
Machine
status
Human/ manual
0
0
Barcode
+
Computer vision
Data
granulrity
(#parts/sec)
Additionally, AtlasRFID provides a comparative table 4-2 below that indicates relative
strengths and weaknesses of RFID and bar code [6]. The table compares the read rate (better
known as sampling frequency), line of sight, human capital requirements, read/write capability,
durability, and security.
For the purposes of this project and thesis, the author decided to implement the auto
RFID system because of the lack of accuracy of the bar code system. In addition, most
commercial RFID systems can sample at a frequency of > 1 Hertz. Issues surrounding the
50
implementation of the auto RFID and power monitoring systems are described in detail in
section 5.3.
51
Table 4-2: AtlasRFID comparison of RFLD and bar code.
RFID
Read Rate
Line of Sight
Bar Code
High throughput. Multiple Very low throughput. Tags
(>100) tags can be read can only be read manually,
one at a time.
simultaneously.
Not required. Items can be
oriented in any direction, as
long as it is in the read range,
and direct line of sight is
never required.
Definitely required. Scanner
must physically see each item
directly to scan, and items
must be oriented in a very
specific manner.
Human Capital
Virtually none. Once up and Large requirements. Laborers
running, the system is must scan each tag.
completely automated.
Read/Write Capability
More than just reading. Read only. Ability to read
Ability to read, write, modify, items and nothing else.
and update.
Durability
High. Much better protected, Low. Easily damaged or
and can even be internally removed; cannot be read if
attached, so it can be read dirty or greasy.
harsh
very
through
environments.
Security
easier
Much
High. Difficult to replicate. Low.
Data can be encrypted, reproduce or counterfeit.
or
protected,
password
include a "kill" feature to
remove data permanently, so
information stored is much
more secure.
52
to
Chapter 5 Implementation
This chapter describes the implementation of the passive data collection system at Waters
used to determine machine utilization, average state dependent setup time, and average cycle
time. The implementation covers the use of an automated power monitoring system designed to
determine machine state and the use of an auto RFID system designed to track part locations.
This chapter discusses the specific commercially available products selected to test the data
collection concept presented in chapter 3. The data collection system was designed and
implemented in order to determine the average part cycle time and the average state dependent
setup times for the 10 unique SKU's described by Chandar [1] and Puszko [2]. Due to limitations
in the budget and a short timeline for implementation during this project the parts in question
were only tracked and monitored during their operations in the CNC turning and CNC milling
departments in Waters' machining center of excellence. Figure 5-1 below is a map of Waters'
machining center of excellence with areas marked where the specific instruments are located.
Additionally, this chapter details procedural issues experienced by the author during
implementation as well as means to address these issues. Manufacturing systems engineers may
use this section of the thesis as a model for replication of the experiment or an implementation
guide under comparable conditions.
53
I
r;7A
I
I t- I
-
I
-
E-
rAil
b
, 40"
I
I
L
r
R-1
M-WII"I
.....
,.
...
I-
Figure 5-1: Map of Waters' machining center of excellence indicating the location and type of system in each location.
54
5.1
Implementation of power monitoring system
As indicated by the map in Figure 5-1 above, 7 machines were equipped with a
commercially available power monitoring solution. The primary factors used when deciding
which system to purchase were cost, lead time, and functionality. While several products were
found to have the desired functionality, only one product had a cost within budget that could be
delivered quickly for pre-installation testing. The power monitoring product selected is the Dent
ELITEpro XC model. Detailed product specifications are included in Appendix section A-1. The
system has support for up to four channels simultaneously, meaning that it can record the power
consumption of four unique machines. Two Dent ELITEpro XC's were purchased to provide
sufficient coverage for the 7 machines. Implementation is simplified and no machine downtime
is required when using clamp style current transformers as seen in Figure 5-2 below [7]. Section
5.3.2 describes challenges experienced during installation as well as visual instructions for
operators to use.
High Pertormanc* 150A Clamp On
CT
Range:.5A- 300AAC
DENTs affordable and compact 150A EZ
Clamp current translbrmers are the
one-handed operating solution for temporary
or long-term studies. They are designed to
work in tight places and ciamp around cables
or buss bars.
StandardFetes:
" .5A- 300AAC range
" Better than 1% accuracy amss the full
range
" Millivolt output
Figure 5-2: Dent clamp on current transformer unit.
Each Dent ELITEpro XC unit was programmed to output data over WiFi. Each time the
machining center supervisors wish to review the data, they must connect to the power monitors
55
over WiFi in order to access the data. Table 5-1 below shows an example of raw current data
logged by the power monitor when programmed to sample at 1Hz.
Table 5-1: Example of raw current data.
Number
1
2
3
4
5
6
7
8
Date
5/19/2014
5/19/2014
5/19/2014
5/19/2014
5/19/2014
5/19/2014
5/19/2014
5/19/2014
End
Time
Line 1 Avg.
Amp
M1208
12:09:57
12:09:58
12:09:59
12:10:00
12:10:01
12:10:02
12:10:03
12:10:04
Line 4
Avg. Amp
M1211
Line 3
Avg. Amp
M1210
Line 2
Avg. Amp
M1209
5.51
5.54
5.52
5.5
5.54
5.61
5.58
5.54
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.35
0.35
Table 5-2 below shows an example of raw current data that has been manipulated using
Microsoft Excel to yield power data with resolution of 1Hz. The data captured are manipulated
to this form in order to create the plots shown in chapter 6.
Table 5-2: Example of raw current data manipulated to output power and utilization.
Number
1
2
3
4
5
6
7
8
Date
5/19/2014
5/19/2014
5/19/2014
5/19/2014
5/19/2014
5/19/2014
5/19/2014
5/19/2014
Threshold values
End Time
Line 1
Line 2
Avg.
M1208
Avg. Watt
Line 3
Avg. Watt
Line 4
Avg. Watt
M1209
M1210
M1211
12:09:57
12:09:58
12:09:59
12:10:00
12:10:01
12:10:02
12:10:03
12:10:04
OFF /DOWN
ON / DOWN
1322
1329
1324
1320
1329
1346
1339
1329
<.5
< 1
0
0
0
0
0
0
0
0
<.5
<1
0
0
0
0
0
0
0
0
<.5
<1
0
0
0
0
0
0
84
84
<.5
< 1
ON / UP
>1
>1
>1
>1
56
I
Elapsed
.
time
5.2
8 seconds
Utilization from
12:09:57 to
12:10:04
100%
0%
0%
0%
Implementation of auto RFID system
As indicated by the map in Figure 5-1, 9 locations were required to be equipped with a
commercially available RFID antenna. When selecting an auto RFID reader and software
solution, the manufacturing systems engineer must consider the desired number of antennas per
reader, the number of readers necessary for the implementation, and the minimum gain of each
reader. Each of the previous design factors contribute to the overall implementation cost. The
further the antenna is from the reader, the higher the gain is required due to losses in energy
along the length of a coaxial cable. Cables longer than 25 feet come at a higher price than
standard length cables under 25 feet in length. Commercially available RFID readers come with
1, 2, 4, or 8 antenna ports. The primary factors used when deciding which system to purchase
were cost, lead time, and functionality. While several products were found to have the desired
functionality, only one product had a cost within budget that could be delivered quickly for preinstallation testing.
Antenna
Inputs
Data output
via USB
57
Figure 5-3: Image of speedway revolution RFID reader with support for up to four antennas.
The commercially available system chosen for this application is the 4-channel Impinj
Speedway Revolution shown in Figure 5-3 [8] with Speedway Connect software and Multireader
software. The system has support for up to four channels meaning that it can record the RF field
data for four antennas simultaneously. The author suggests that the manufacturing systems
engineer initially purchase a testing platform, such as the Impinj Speedway Revolution, to test
the system for compatibility before full scale implementation. Factors to consider during
implementation are described in sections 5.3.1 and 5.3.3. The manufacturing systems engineer
must also consider operator safety when routing coaxial cables in a factory. The author
recommends that cables be routed overhead when possible to avoid operator issues. However,
the final decision must consider that added length can be caused by overhead cranes or power
lines which ultimately leads to high cost cables with weak signal strength. For the purposes of
this experiment three Impinj Speedway Revolution 4-channel systems were installed to
accommodate the 9 antenna locations. For this experiment, RFID readers were not network
accessible due to limited network access points within the machining center. Although the
networked RFID reader is a key step in a completely automated solution, Waters' IT network
security concerns and practical time constraints prohibited a network connection. Therefore,
Toshiba® 32 GB fat32 formatted USB drives were used to store the data locally on each reader.
The reader logs data in .CSV file format at a programmable sampling rate at up to 1000 Hz. As
the sampling rate becomes faster, the file size grows faster. Preliminary experiments showed that
sampling the RIFD data at 1000 Hz resulted in more than 4,000,000 data points in a 24-hour
period from one long range antenna and one tag. Storing data files of increasing size on a USB
requires the manufacturing systems engineer to manually switch the USB drives frequently. The
tradeoff between sampling rate and file size prompted the selection a sampling rate of 0.20 Hz.
Selecting ideal antenna locations within a factory is a challenging task, especially in a
highly metallic environment. The manufacturing systems engineer must consider the human
factors involved within the manufacturing system in order determine antenna and tag locations in
a highly metallic environment. For example, Waters' machining center has an overhead crane in
the CNC milling area used during setups to transport fixtures. In other areas of the factory metal
carts are used to transport large castings, tools, fixtures, and raw material. If any of these metallic
58
objects are left between an antenna and a tag for an extended period of time, the manufacturing
systems engineer is less confident in the fidelity of the data. Therefore the machinists and
operators in the machining center need to be trained to adhere to operational procedures. Details
relating to the specific operational procedures implemented in Waters' CNC milling and CNC
turning departments are found in Appendix section A-3. Table 5-3 below shows sample RFID
system data in its raw form. The raw form of RFID data is not immediately useful to the
engineer.
Table 5-3: Sample RFID system data in raw form.
Antenna
1
4
2
Tag ID
EPOCH time
28900330813
1.40549E+15
40501310906
1.40549E+15
28900330801
1.40549E+15
Using equation 17 below, the manufacturing systems engineer can easily convert from
EPOCH time to a specific time and date in Microsoft excel.
Time =
EPOCH time-(4*3600)
86400
+ 25569
(17)
Table 5-4 below shows sample RFID system data in its manipulated form. The
manipulated form of RFID data is immediately useful to the engineer. The manipulated RFID
data is used to determine the part location and duration for each SKU as proposed in section
3.4.1.
Table 5-4: Sample RFID system data in manipulated form using Microsoft Excel.
Antenna Tag ID
1 28900330813
4 40501310906
2 28900330801
5.3
EPOCH time
1405493978029680
1405493988225980
1405493991231110
Time
Date
2:59:38
16-Jul-14
2:59:48
16-Jul-14
2:59:51
16-Jul-14
Logistical issues during implementation
This section details procedural issues experienced by the author during implementation as
well as means to address these issues relating to selecting the correct RFID tag, installing the
59
Dent ELITEpro XC power monitoring solution, and using an auto RFID system in a highly
metallic environment. Manufacturing systems engineers may use this section of the thesis as a
model for replication of the experiment or an implementation guide under comparable
conditions.
5.3.1
Selecting the correct RFID tag
Commercially available RFID tags come in many different varieties. The three main
types are low frequency, high frequency and ultra-high frequency. Low frequency tags are
designed to operate at less than 1 foot from the antenna and are passively powered by the
antenna. High frequency tags are designed to work at up to 20 feet and are passively powered by
the antenna. Ultra high frequency tags are designed to operate at distances over 100 feet;
however, they must have their own internal power source. For the purposes of this project ultrahigh frequency tags were not considered because of the added cost per tag and the risk of
overlapping antenna fields over a long operating range.
In order to successfully implement an auto RFID system in a machining center the
manufacturing systems engineer must determine the best type of tag for the application. Given
that one of the primary project functional requirements during the course of implementation was
to integrate the data collection system seamlessly into the current factory layout, the author
determined the ideal locations for all of the tag monitoring locations holding the factory layout
constant. As mentioned previously the project had a limited budget; therefore the possibility of
using more than one tag per tray of parts (i.e. one low frequency and one high frequency tag) was
eliminated. Accordingly, the author determined that the minimum operation distance within the
factory given the specific location of the machines to be tracked is 6 feet. Furthermore, the
author determined that the maximum operation distance is 20 feet.
The author selected and tested over 30 unique tag types. In order to decide which RFID
tag performed the best, a baseline experiment was performed to determine if each of the tags
could be read at a distance of at least 6 feet from the antenna. The initial experiment indicated
that only 5 of the 30 tags could be read at a distance of at least 6 feet. The remaining 5 tags were
then tested to determine which tag type would perform best over long distances. Additionally,
the tags were tested with and without a 1 0"x 1 0"x %" piece of 6061 aluminum behind the tag in
order to determine the effect of RF shielding (see section 5.3.3 for more details on RF shielding).
60
....
......
The experiment was performed using both long range and short range antennas. During all
experiments the gain of the antennas were programmed to the maximum allowable values set by
the manufacturer. Figure 5-4 below is a photo taken of the experimental setup used to determine
the effect of distance and RF shielding on RFID tag read rate.
Figure 5-4: Photo of experimental layout used to determine the effect of distance and RF shielding
on RFID tag read rate.
The Impinj speedway revolution reader and the multi-reader software package was used
to determine if the antenna detected the tag at 16 feet, either with or without metal. Average read
rate, defined in equation 18 below, is the key measure used to compare various tags.
Average Read Rate =
Total tag read count
Elapsed time
(18)
If the author determined that the tag maintained an average read rate of at least 25 over a
15 second period, then the data was recorded at that distance in both cases. If the antenna failed
to detect the tag at 16 feet both with and without metal behind it, the author discarded the data
because the tag could not possibly be used during implementation. In the event that a tag was
61
unreadable at 16 feet, the author moved the tag one foot closer to the antenna until the desired
average read rate of 25 over a 15 second period was observed.
Once the longest distance from the antenna was determined to meet the minimum
required read rate, the author performed 4 replications of the experiment at that distance. The
data were recorded and analyzed. The remainder of this section discusses the results and analysis
of the experiment.
Figure 5-5 below contains a series of normal probability plots created using the results of
each experiment with the small antenna. Only the tags that were detected at 16 feet using the
small antenna are plotted below. In order to create the plots below, the author assumed that the
data collected behaved normally and therefore a Gaussian fit is used.
0.36
0.36
0.36
0.31
0.31
>-0.31
C 0.21
0.16
0.11
C 0.21
o 0.16
0.11
0.26
0.26
0.26
S0.21
0.16
0.11
0.06
0.06
0.06
0
10
20
30
0
40
----
--
0.21
0 0.16
-
0
30
40
Read rate at 16 feet
10
20
30
40
Read rate at 16 feet
Xerafy Medium
Metal
0.36
0.31
0.26
0.21
---
--
0.11
0.06
20
0
40
0.31
0.26
0 0.16
10
30
0.36
0.21
-
0.11
0.06
20
Xerafy Cargo Track
No metal
Xerafy Cargo Track
Metal
0.31
0.26
10
Read rate at 16 feet
Read rate at 16 feet
0.36
Omni ID Max
No metal
Confidex
Metal
Omni ID
Metal
0 0.16
0.11
0.06
-
0
10
20
30
40
Read rate at 16 feet
0
10
20
30
40
Read rate at 16 feet
Figure 5-5: Normal probability plots of read rate at a distance of 16feet from the small antenna.
Figure 5-6 below contains a series of normal probability plots created using the results of
each experiment with the large antenna. Only the tags that were detected at 16 feet using both the
small antenna and the large antenna are plotted below. In order to create the plots below, the
author assumed that the data collected behaved normally and therefore a Gaussian fit is used.
62
Omni ID
Metal
Confidex
Metal
0.36
0.31
0.26
a, 0.21
0 0.16
0.11
0.06
C
a,
0
0
10
20
30
0
0
40
C
a,
4..
U'
C
a,
0
10
20
30
Read rate at 16 feet
20
30
0
40
40
4..
U'
C
a,
0
10
20
30
20
30
40
Xerafy Medium
Metal
0.36
0.31
0.26
0.21
0.16
0.11
0.06
0
10
Read rate at 16 feet
Xerafy Cargo Track
No metal
0.36
0.31
0.26
0.21
0.16
0.11
0.06
0
10
0.36
0.31
0.26
0.21
0.16
0.11
0.06
Read rate at 16 feet
Xerafy Cargo Track
Metal
0
No metal
0.36
0.31
0.26
0.21
0.16
0.11
0.06
Read rate at 16 feet
)m.
4..
U'
Omni ID Max
40
Read rate at 16 feet
0.36
0.31
0.26
0.21
0.16
0.11
0.06
0
10
20
30
40
Read rate at 16 feet
Figure 5-6: Normal probability plots of read rate at a distance of 16feet from the large antenna.
From these results and analysis the author concluded that tag 4, the Xerafy cargo track
tag, is the best choice to operate at a maximum distance of 16 feet from either the small or large
antenna with a metal or non-metal backing. For more information about the Xerafy cargo track
tags please refer to the product specification sheet in the Appendix section A-2.
5.3.2
Power monitoring obeys Faraday's Law
The Dent ELITEpro XC power monitor uses a clip on current transformer to measure
electric flux through the current transformer. Faraday's law [9], equation 19 below, states that
the total amount of electrical current passing through a closed 2D surface (i.e. the cross section
of a wire) can be computed by measuring the electric flux across the surface. If equal, yet
63
opposite in polarity, electric flux are contained within the surface than the resulting current will
be zero.
Current = I =
dt
= (ffJ - dS
(19)
Most modem machine tools use high-voltage power lines. Each power line to the
machine has either a positive or negative polarity. Some machine tool high voltage power lines
are contained within one cable. Figure 5-7 below can be used during the implementation phase
and training of the operators to explain an incorrect installation of the current monitor. The white
circles with +/- indicate the polarity of the equal magnitude electric fields. The grey exterior
circle represents the wire encasement. Using Faraday's law the engineer knows that Dent
ELITEpro XC clip on current transformers cannot contain both the positive and negative sides of
a cable. If both wires are contained within the wire clamp, Faraday's law predicts that the current
sensor will output zero.
Figure 5-7: Depiction of incorrect installation of current transformer.
Figure 5-8 below can be used during the implementation phase and training of the
operators to explain a correct installation of the current monitor. Again, the white circles with +/indicate the polarity of the equal magnitude electric fields. The grey exterior circle represents the
wire encasement. The Dent ELITEpro XC wire clamp must contain either + or -, but not both.
64
Figure 5-8: Depiction of correct installation of current transformer.
Since the Dent ELITEpro XC outputs current data, the manufacturing systems engineer
must convert the current into power using Joule's law [10], equation 20 below, by multiplying
voltage and current. The simple conversion from current to power enables the manufacturing
systems engineer to create graphs similar in form to the generalized graph of machine power
consumption plotted against time as shown in section 3.4.1.
Power= -- V= IV
dt
5.3.3
(20)
Process plan for using RFID in a metal environment
As alluded to in section 5.3.1 the manufacturing systems engineer will experience issues
when implementing an auto RFID data collection system in a highly-metallic environment such
as that of Waters' machining center of excellence. Figure 5-9 below depicts the effects that metal
can have on both the antenna and the tags [11]. The left column depicts the effect on the antenna
and the right column depicts the effect on the tags. The figure indicates that metal behind either
the antenna or tag completely shields the RF signal from propagating behind the device. These
plots are used by the engineer to determine the mounting configurations of the RFID tags and
antennas.
65
%0*d 4iW
00
40
4
I~
30
On Plastic
so
So
3100
4M
1
60
40
IlK)
A)
0
so)
100
100
140
too
.110
110
2e
/4
120
140
)
110
1 114
1
20
0
1M
1W
18D
Large Metal
sheet
0
V10
0
10
310
40
,t07
40
80
00
4100
60
40
go
100
too
20
110
10
130
IM0
140
DID
2100
"o
140
2W
110
oo
110 160
X0 1W
180
Figure 5-9: Plots of varying RFID antenna field comparing metal backed and plastic backed tags.
Through basic bench level experiments, such as the experiment detailed in section 5.3.1,
the manufacturing systems engineer learns that care must be taken when implementing the auto
RFID data collection system in a highly metallic environment. Figure 5-10 below depicts the
implementation of a large antenna in the CNC turning area at Waters. The leftmost photo has
been post processed to include a green directional arrow and checkmark which indicate the
correct orientation. The rightmost photo has been post processed to include a red directional
arrow and X which indicate the incorrect orientation. While the RFID system does not require
line of site to collect data, initial experiments indicate that the system behaves more reliably
when the tag faces the antenna. This effect can be attributed to the fact that the bins onto which
the tags are mounted contain metal parts and exhibit the RF shielding effect described in Figure
5-9 above. During the implementation of the auto RFID system and training of the machinists,
the author determined that it was best practice to mark off the desired location and orientation for
bins with tags using labels (similar to the Toyota ProductionSystem Kaizen practice) [12]. The
66
manufacturing systems engineer should refer to Appendix section A-3 to review the entire
process plan which discusses common failure modes experienced during implementation in a
highly metallic environment.
Figure 5-10: Photos taken in Waters' machining center of excellence CNC turning department.
67
Chapter 6 Results and discussion
This chapter demonstrates the approach taken to analyze the power monitoring and auto
RFID data captured during two weeks of the expermient by Chandar [1] and Puszko [2]. Figure
6-1 below is a map of the Waters CNC Turning department which indicates the location of each
specific power monitor and RFID antenna. Figure 6-2 below is a map of the Waters CNC
Milling department which indicates the location of each specific power monitor and RFID
antenna. The raw data captured using the power monitoring and auto RFID described in chapter
5 must be attributed to a specific machine in order to analyze the data using the method
described in chapter 3. These maps are used to assign labels to the data which correspond to the
relevant machine.
Antenna locations 1 and 4 on the CNC Turning map correspond to two large antennas
which are used to monitor the inprocess locations for M1302-2 and M1309 respectively.
Antennas 1 and 2 on the right side of the CNC Milling map correspond to two small antennas
which are used to monitor the inprocess locations for M1210 and M1211 respectively. Antenna 3
on the right hand side of the CNC Milling map corresponds to a small antenna which is used to
monitor the inventory location at M1211. Antenna 4 on the right hand side of the CNC Milling
map corresponds to a large antenna which is used to monitor the inventory location at M1210.
The remaining antennas on the CNC Milling map are used to monitor the CNC Milling Utility
Operator inventory location.
68
........
I'll.
...111..
.
I.........
N
Sk
Poe
Moitr
r
JT
JL
LUW
Figure 6-1: Map of the Waters CNC Turning department indicating data collection locations.
FRI
kA
-[-[1]2 1
#%
wo
vimU
Figure 6-2: Map of the Waters CNC Milling department indicating data collection locations.
69
Demonstration of average cycle time data
6.1
This section presents the captured power monitoring and RFID data from M1309 in CNC
Turning spanning from 21:00 on 7/16/2014 to 20:00 on 7/18/2014. The analytical method
presented in chapter 3 can be used to process the power monitoring and RFID data in order to
determine the average cycle time for the part type. In addition, the data can be used to determine
the utilization of M1309 during the period spanning from 21:00 on 7/16/2014 to 20:00 on
7/18/2014. Figure 6-3 below is a plot of power monitoring data from M1309 in CNC Turning.
Note that unlike the idealized power monitoring graphs in chapter 3, the plot of actual power
monitoring data is not smooth.
Plot of CNC Turning M1309 Power Monitoring Data
160
1 10
120
100
80
60
20
0
-T
7/16/14
-T
T
-
-,S~
7/17/14
- -
M
ZI
Date and Time
7/18/14
Figure 6-3: Plot of CNC Turning M1309 power monitoring data (7/16-7/18).
Figure 6-4 below is a plot of RFID data from antenna 4 which covers the inprocess
location at M1309 in CNC Turning. These data have been manipulated to only show the specific
part type SKU. The raw RFID data confirms that a total of 10 unique tags of part type
WAT081193 were present during this time period. Six of the tags appear during the first major
time period (n = 6) while four of the tags appear during the second major time period (n = 4).
70
n=6
7/16/14
n=4
W
W
W
A
T
0
8
1
1
9
3
A
T
0
8
1
1
9
3
A
T
0
8
1
1
9
3
7/17/14
Date and Time
7/18/14
Figure 6-4: Plot of CNC Turning M1309 RFID data (7/16-7/18).
Figure 6-5 below is the graphical combination of the power monitoring and RFID data
from M1309 in CNC Turning. If the engineer attempts to use the RFID data alone to determine
the cycle time during period one, he or she concludes that the elapsed time is approximately 17
hours. However, when the-RFID data is combined with the power monitoring data, the engineer
concludes that the total time elapsed during period one is approximately 13.5 hours. Only by
combining the power monitoring and RFID data is the engineer confident in the amount of time
spent processing the parts. Note that there is a period of time (from 18:00 to 19:00 on 7/17/2014)
when a WAT081193 tag is observed but no power is consumed by the machine. This indicates to
the engineer that no process was performed on the parts during this period. However, if the
engineer were to only view the RFID data, he or she might wrongly conclude that a machinist
spent approximately one hour processing the parts. It is for this reason that the engineer must
capture both power monitoring and RFID data in order to accurately determine the average part
cycle time.
71
Plot of CNC Turning M 1309 Power Monitoring and RFID Data
n =6
S
tWi
n 2 =4
W
W
A
120
100
0
8
10
9
3
0
7/17/14
7/16/14
Date and Time
7/18/14
Figure 6-5: Plot of CNC Turning M1309 power monitoring and RFID data (7/16-7/18).
Recall from section 3.4.2 equation 9 which is used to calculate the average cycle time for
part type i. Using the t and n values from Figure 6-5 above, we can compute the average cycle
time for part type WAT081193 over the period spanning from 21:00 on 16 July 2014 to 20:00 on
18 July 2014. In this case, N
=
2.
tWATO81193,1 + tWATo1193,2
n
N
n2
Average cycle time for PwATo81193 =
N
13.5
hours
Average cycle time for PWATO8 1193
6bins2
N
9.75
ot
(21)
hours
4bins
=
2.34 hours per bin
(22)
Each bin used to produce WAT081193 holds 30 parts. The cycle time values found in
SAP and Filemaker are recorded on a per part basis. Table 6-1 below contains the average cycle
time values for WAT081193 as reported by SAP, Filemaker, and the experimental results. Since
only two lots were observed for this experiment and used to determine the average lot cycle time,
the engineer should not be convinced that the experimental data are statistically significant nor
representative of the population. However, the analytical methodology described above should
be used as more data are captured. As the volume of experimental data increases, the engineer
should be convinced that the experimental results are statistically significant.
72
Table 6-1: Comparison of SAP, Filemaker, and Experimental part cycle time data.
Turning
Part No.
wat081193
Process Time (Hours)
SAP
0.083
Filemaker
0.105
Experimental
0.078
Using the raw power monitoring data plotted in Figure 6-3 the engineer can calculate the
utilization of the machine. In chapter 4 the author suggests that each machine should be
calibrated in order to determine the appropriate power threshold values used to distinguish
between uptime and down time. Table 6-2 below contains the results of the utilization
calculations using equation 11 while varying the power threshold.
Table 6-2: M1309 machine utilization rates using various power thresholds (7/16-7/18).
(Watts)
Utilization (%)
Power Threshold
6.2
10
1
0.1
38.11%
32.00%
29.43%
Demonstration of state dependent setup time data
This section presents the captured power monitoring and RFID data from M1309 in CNC
Turning spanning from 21:00 on 7/21/2014 to 05:30 on 7/22/2014. The analytical method
presented in chapter 3 can be used to process the power monitoring and RFID data in order to
determine the average state dependent setup time. In addition, the data can be used to determine
the utilization of M1309 during the period spanning from 21:00 on 7/21/2014 to 05:30 on
7/22/2014. Figure 6-6 below is a plot of power monitoring data from M1309 in CNC Turning.
Note that unlike the idealized power monitoring graphs in chapter 3, the plot of actual power
monitoring data is not smooth.
73
Plot of CNC Turning M1309 Power Monitoring Data
120
100
80
60
0
410
III[
0
1 -
'T
X 1r, C
C7
r I
lr
Ilium
C
r I
I
Z -1
r I
11
1 r,
ri
rl
nj ra
i r I
r
I r I r i r
r.
i
i
tr- I r-, -'
i r 4 r I r i
r-
t -T V
r I r
F4 71 rs
-T
W
r 1 r i r I
(-_A
Date and Time
7/21/14
ri
-r
r
-r
e:
sw:
r a
vT
11
-T -T
w,
-T -r
7/22/14
Figure 6-6: Plot of CNC Turning M1309 power monitoring data (7/21-7/22)
Figure 6-7 below is a plot of RFID data from antenna 4 which covers the inprocess
location at M1309 in CNC Turning. These data have been manipulated to only show the specific
part type SKU. The raw RFID data confirms that a total of two unique (n = 2) tags of part type
405013109 and one unique tag (n = 1) of part type 405008452 were present during this time
period. Two of the tags appear during the first major time period while one of the tags appears
during the second major time period.
n=l
2
4
0
5
0
0
4
0
5
0
1
3
1
0
9
7/21/14
8
4
5
2
Date and
74
..
........
inic
7/22/14
Figure 6-7: Plot of CNC Turning M1309 RFID data (7/21-7/22).
Figure 6-8 below is the graphical combination of the power monitoring and RFID data
from M1309 in CNC Turning. Note that there is a period of time (from 03:30 to 04:00 on
7/22/2014) when no power is consumed by the machine. This indicates to the engineer that no
process was performed on the parts during this period. When the RFID data is combined with
the power monitoring data, the engineer concludes that the total time elapsed during the setup
period is approximately 1 hour. Only by combining the power monitoring and RFID data is the
engineer confident in the amount of time spent setting up the machine.
to
Setup from 405013109
405008452 lasted 1 hour
Plot of CNC Turning M 1309 Power Monitoring and RFID Data
I
XKI
A-(
10I
n= 2
n=, 1
4
4
0
5
0
1
3
0
5
0
0
8
4
5
2
A
1
0
9
-T ;7- A
7
;7
' 14 ;rl W,
7 , r
Q
4 A-4 CA A:A r A A,4 C-1
Z
--
= r
A'A 1 1 C) CA e'k C) Ct rt
7/21/14
C4 rk 4-.!
-
-
-
-
-
Ir r A 7 z
I I':
-
-
-
-
-
Date and Time
C' r-
1
A71
= ro. !
rA CA
-r
7t
X
w
Lt 1
r
'T
r-
-r -
X
-T
'T
-T -T
-7
-, ' -r
-T
r
-T
7/22/14
Figure 6-8: Plot of CNC Turning M1309 power monitoring and RFID data (7/21-7/22).
Recall from section 3.4.2 equation 10 which is used to calculate the average state
dependent setup time from part type a to b. Using the t and n values from Figure 6-8 above, we
can compute the average state dependent setup time from part type 405013109 to part type
405008452 over the period spanning from 21:00 on 21 July 2014 to 05:30 on 22 July 2014. In
this case, N = 1.
75
N
Average state dependent setup time from a to b = sa- =
I a,bj
(23)
Table 6-3 below contains the average setup time values for 405008452 as reported by
SAP, Filemaker, and the experimental results. Since only one setup was observed during this
period of the experiment and was used to determine the "average" lot cycle time, the engineer
should not be convinced that the experimental data are statistically significant nor representative
of the population. The experiment by Puszko [2] to reduce the number of machine setups limits
the ability to capture a data set that statitically significant over a short period of time. In other
words, less setups means less data on the time duration of a setup. However, the analytical
methodology described above should be used as more data are captured. As the volume of
experimental data increases, the engineer should be convinced that the experimental results are
statistically significant. Note that the SAP and Filemaker setup times, values represent state
independent setup times whereas the experimental results represent state dependent setup times.
Table 6-3: Comparison of SAP, Filemaker, and Experimental setup time data.
Setup Time (Hours)
Turning
405008452
Filemaker
SAP
Part Material No.
42
4
1
4.00
2.92
Experimental
from 405013109
1.00
Using the raw power monitoring data plotted in Figure 6-6 the engineer can calculate the
utilization of the machine. In chapter 4 the author suggests that each machine should be
calibrated in order to determine the appropriate power threshold values used to distinguish
between uptime and down time. Table 6-4 below contains the results of the utilization
calculations using equation 11 while varying the power threshold.
Table 6-4: M1309 machine utilization rates using various power thresholds (7/21-7/22).
(Watts)
Threshold
7Power
0.1
16.60%
1
16.22%
Utilizain %
16.60%
16.22%
76
10
_5.84%
1.5.84%
6.3
Data analysis challenges
The data analyzed and discussed in sections 6.1 and 6.2 demonstrates that the analytical
method proposed in chapter 4 can be used to determine the average part cycle time and the
average state dependent setup time. However, not all of the data captured over the course of the
two week experiment was useful. As discussed in chapter 5, the successful implementation of the
passive data collection system requires thoughtful antenna placement and operator compliance
considerations. The data captured during this experiment indicates that the antenna locations in
CNC Milling were not ideal and that some of the machinists were not trained properly to comply
with the data collection process plan (see Appendix section A-3). The combination of these two
issues resulted in large quantities of unusable data.
Figure 6-2 is a map of the Waters CNC Milling department which indicates the location
of each specific RFID antenna. The antenna locations chosen for this experiment were selected
because they required no significant factory layout changes. In general, the engineer must
determine that the RFID antenna used to monitor the inprocess location can only track parts
within its RF field.
By inspection the engineer can see that antennas 2,3 and 1,4 have
overlapping RF fields. As a result, the data captured by the RFID readers indicates that all trays
of parts were simultaneously both inprocess and inventory. While the data could have been
processed by making assumptions based upon the relative read rates of each tag, the results
would not be conclusive. Therefore, the data captured in CNC Milling were not analyzed.
Additionally, some of the machinists were not trained properly to comply with the data
collection process plan. The process plan presented to Waters' machinists requires that only one
bin can be present in an inprocess RFID field at a time (see Appendix section A-3.8). If multiple
bins are observed at an inprocess location during the same period, the engineer cannot confdently
determine which SKU was being made at that time. Table 6-5 below contains an example of
RFID data captured in CNC Milling during this experiment. These data indicate that bins
8119309, 8119314, 8119315, and 8119313 were all in process at the same time. Additionally, the
machinists in CNC Milling continued to place metal objects (tool carts, overhead crane, etc.)
between the antennas and tags. Recall from section 5.3.3 that RF signals can be shielded or
obscured when metal is present. While the data could have been processed by making
assumptions based upon the relative read rates of each tag, the results would not be conclusive.
77
Therefore, the data captured when the inprocess antennas simultaneously detected multiple tags
in CNC Milling and CNC turning were not analyzed.
Table 6-5: Example CNC Milling RFID data indicating that machinists did not follow the data
collection process plan.
Antenna
Tag ID
EPOCH time
Time
Date
1
1
1
2
4
4
4
1
8119309
8119314
8119315
40501536720
8119316
8119317
28900330813
8119313
1.40609E+15
1.40609E+15
1.40609E+15
1.40609E+15
1.40609E+15
1.40609E+15
1.40609E+15
1.40609E+15
1:27:15
1:27:19
1:27:23
1:27:23
1:27:26
1:27:30
1:27:30
1:27:31
23-Jul-14
23-Jul-14
23-Jul-14
23-Jul-14
23-Jul-14
23-Jul-14
23-Jul-14
23-Jul-14
78
Chapter 7 Conclusion
The work done by Chandar [1] to optimize lot sizes and Puszko [2] to reduce time spent
performing machine setups requires the use of accurate, high efficacy average part cycle time
and average state dependent setup time data. Additionally, management at Waters requires
accurate, high efficacy machine utilization data in order to make capacity planning and capital
equipment purchasing decisions. Waters' present data collection system is designed to capture
lot cycle time, state independent setup time, and machine spindle uptime. Therefore, the present
data collection system in place at Waters Corporation is insufficient for the needs of management
and for the purposes of the team project.
The purpose of this thesis is to demonstrate the value of implementing a novel, accurate
automated data collection system designed to capture the data necessary to optimize lot sizes and
supermarkets for high-volume parts in a manufacturing environment, and to describe a practical
method for doing so. This thesis demonstrates that the graphical approach and analytical method
presented in chapter 3 can be used to determine the manufacturing systems statistics required by
Chandar [1] and Puszko [2]. The proposed novel method to automatically capture and process
manufacturing data detailed in chapter 4 was described assuming the use of power monitoring
and auto RFID.
Individually, the power monitoring and auto RFID systems implemented for this project
provide value to Waters. For instance, the power monitoring system can now be used to calculate
the utilization of each machine and standardize the reporting language across all machine types.
Furthermore, the auto RFID system can now be used to track average lot queueing time and
monitor inventory. This thesis demonstrates a proof of concept, enumerates the financial
benefits, and provides a practical guide for manufacturing systems engineers to use during the
implementation of a combined passive power monitoring and auto RFID data collection system.
During the course of preliminary investigations Waters management enumerated several
core functional requirements for the project specified in chapter 2. This thesis describes the
rationale as to why the power monitoring and auto RFID systems were to be implemented in
Waters machining center of excellence. The implementation covers the use of an automated
power monitoring system designed to determine machine state and the use of an auto RFID
79
system designed to track part locations. This thesis also discusses the specific commercially
available products selected to test the data collection concept presented in chapter 3.
The data collection system tested at Waters successfully demonstrates the combined
capabilities of a passive power monitoring and auto RFID data collection system to capture
average cycle time and average state dependent set up time for some of the 10 parts selected by
Chandar [1] and Puszko [2]. Since the Waters machining center is a highly metallic environment,
many logistical issues were faced during the implementation phase. These logistical issues
include an overlapping RFID antenna layout, operator training, and operator compliance.
Ultimately, the data captured from the two week data collection experiment is insufficient to
confidently report all of the manufacturing systems statistics needed by Chandar [1] and Puszko
[2]; however, the data captured and results discussed in chapter 6 indicate that the power
monitoring and auto RFID system is capable of capturing the necessary data. Running the
experiment for a longer period of time with minor improvements to the antenna layout and the
operator training should enable Waters to confidently capture all of the data needed to continue
the work of Chandar [1] and Puszko [2].
Figure 7-1 below shows the cost to scale up the combined data collection system across
all departments in Waters machining center of excellence. The present data collection system has
an opportunity cost of approximately $85,775 per year as calculated in section 2.2.1. Based upon
the implementation cost of the experimental setup used for this project, we project that the cost
to implement power monitoring and auto RFID at full-scale will be $60,000 in raw materials.
This amount does not consider the software development costs associated with network
integration at Waters. Based upon these costs, the combined passive data collection system is
expected to pay off 9 months after implementation. The combined data collection system is
expected to provide Waters at least $365,000 in value over a 5-year period.
80
200
-
180
160 -
Present system cost
Power + RFID cost
140
0120
4-
C
100100
=,
80
m
o
60
40
Breakeven in
9 months
20
0
Figure 7-1: Cost comparison between present data collection system and proposed data collection
system versus time.
In conclusion, the data collection method described in this thesis is useful to a
manufacturing organization that would benefit from using these manufacturing systems statistics
to improve production planning and factory throughput while simultaneously eliminating the
need for costly, manual data collection.
81
Chapter 8 Future work
Individually, the power monitoring and auto RFID systems implemented for this project
provide value to Waters. However, the author believes that there are many other benefits of the
combined passive data collection system implemented during this project. This chapter discusses
alternative uses and additional potential financial benefits of the combined power monitoring and
auto REID data collection system to Waters. Lastly, the author proposes a research agenda
discussing several additional uses of the machine utilization, average cycle time, and average
state dependent setup time data that is captured using an accurate automated data collection
system.
8.1
Next steps for Waters
The passive data collection system implemented during this project included antennas
that cover three separate inventory locations. The average queue time results were not calculated
because the information was not needed by Chandar [1] and Puszko [2]. Since the data collection
system is designed to handle multiple REID tags at the same time, integrating transfer lots is a
simple task. The flexible Velcro@ RFID mount design enables Waters to track each individual
bin within each lot. The manufacturing systems engineer needs to program separate tags for each
bin within the lot. If the manufacturing systems engineer implements a transfer lot system
tracked by the RFID system, the author recommends monitoring the in process inventory
locations. Capturing the data that indicates how long a tray of parts spends in process enables the
manufacturing systems engineer to determine the average queueing time for any tray with an
RFID tag. Understanding the queue time of the manufacturing system enables the manufacturing
systems engineer to set maximum buffer thresholds.
Preliminary investigations revealed that Waters' accounting department performs a cost
roll once per 5 years in order to set standard cycle times and setup times for each SKU. The
present cost roll procedure consumes hundreds of man hours. In recent months, Waters' Milford
facility has experienced increasing demand resulting in excessive overtime machinist hours at a
pay rate of time and a half. The combined passive data collection system enables the accounting
department to collect data for analysis on a more frequent basis without incurring the labor cost
82
......
.......
.
...
..
...
...
required to manually capture data. The finance department can use this real-time information to
inform smarter head count decisions to avoid excessive overtime costs.
The author further recommends that the passive data collection system be used to relate
scrap rates to a particular setup. In this scenario, it would be wise to track each tool and each
fixture with an RFID tag. This would then enable a data analyst to retroactively determine
whether high scrap rate was caused by a human error during setup or in process. Using unique
RFID tags to track a setup (i.e. tool or fixture) provides significantly higher accuracy average
state dependent setup times. Once the manufacturing systems engineer has determined the
relative setup scrap risk for each state dependent setup, the lot sizes for each part type must be
adjusted.
Additionally, the author recommends that Waters implement a networked global RFID
system at the Milford facility. A networked global RFID system enables the manufacturing
systems engineer to determine the position and velocity of each RFID tag in real time anywhere
within the factory. The system should be connected to Waters' network in order to monitor the
system in real time. This system would include several redundant large antennas in order to track
part location at all times. Knowing the velocity of an RFID tag tells the manufacturing systems
engineer how fast a bin, fixture, tool, or machinist is able to traverse a known distance.
Furthermore, once the auto RFID system is connected to the Waters network, e-Kanbans can be
implemented. This data is useful to the manufacturing systems engineer who must design the
layout of the factory.
Furthermore, the author proposes that Waters integrate a real-time notification system
remotely accessible to the department supervisors. The notification system would be comprised
of a dashboard interface that would display utilization data for each machine. The time series
plot of the utilization data should be presented and the user should be able to adjust the time
period used to compute the machine utilization. As an alternative view, raw current data can be
displayed to the user. Additionally, the dashboard interface can be used to display macro-level
factory statistics such as factory output, work in progress inventory, and total inventory. The user
should have drag and drop control over the production plan. Using the respective values from the
state dependent average setup time matrix, the interface can present the user with the setup time
83
benefit/cost of any changes made to the production plan using the drag and drop interface. Once
approved, key stakeholders will receive a copy of the updated process plan.
The last recommendation is to mount a networked camera to each RFID antenna such
that the viewing angle captures the inventory location or machine. The user of the dashboard
interface should have the ability to select a location from the factory map and stream the live
video feed from that camera. Additionally, the user should have the ability to view video data
from the past. The goal of this system is to capture video data that can be used by a data scientist
to use to train a computer vision program to check the power monitoring and auto RFID system.
8.2
Research agenda
Recall from chapter 2 the generalized form of the average part cycle time vector and the
average state dependent setup time matrix. As discussed in chapter 6, the values calculated using
the methodology described in chapter 3 are used to populate both the cycle time vector and state
dependent setup time matrix. Since the passive data collection system implemented at Waters
was used to monitor for two weeks the decreased machine setup experiment by Puszko [2] and
the increased lot sizes experiment by Chandar [1], the data reported in chapter 6 does not include
statistically significant sample statistics for each measurement. The author suggests that the
manufacturing systems engineer implement the improved passive data collection system as
described in section 8.1. Once the manufacturing systems engineer is confident in the data
captured, he or she should run an experiment to collect data for at least three replicates of each
part cycle time and state dependent setup. The manufacturing systems engineer can then study
the sample statistics of the data set.
Figure 8-1 below represents the sample statistics of the generalized average lot cycle time
vector. Figure 8-2 below represents the sample statistics of the generalized average state
dependent setup matrix. The images in both of these figures represent a generic Gaussian
distribution. By studying the underlying sample statistics, particularly the sample variance, the
manufacturing systems engineer can determine if the process is in control.
84
Figure 8-1: Sample statistics of the generalized average lot cycle time vector.
AA
000
Figure 8-2: Sample statistics of the generalized average state dependent setup matrix.
The data captured using the methodology described in this thesis enables the
manufacturing systems engineer to study the variability of each process, providing a data driven
roadmap for continuous systems improvement. For example, if part type a has a cycle time
variance higher than all other parts, then the manufacturing systems engineer can further explore
the data captured related to part type a. Additionally, if setup type a, b has a setup time variance
higher than all other setups, then the manufacturing systems engineer can further explore the data
captured related to setup type a,b. The author believes that the manufacturing systems engineer
will be able to determine either that the process is truly random or that there is a deterministic
85
cause for the high variance (i.e. a specific operator performs a setup faster than all other
operators).
Appendix
A-1
Dent ELITEpro XC Anatomy 171
ELITEPRO XC ANATOMY
Termid Roc* for Cirrent
Trarsformer (CT) Cormections
Mounting Tab
PhaseChek'" LEDs
to ensure proper CT
Votage Lead
Connections
phasin
Mount Tab
Mourntig mignets
(not picturecd
Standard Ethernet port
Fair Anog/
Ipat Chamels
Poier hAOt
LoggingOW/Conm
indicator LED
86
............
Optional Wi Wterface
may be ordered with
external 3 dbi antena
A-2
Cargo Track product specification 1131
9
FulintId
~Tk3
2sLeedt 3n
RF air protocol
EPC Class 1 Gen 2; IS018000-6C
Operating frequency
UHF 902-928 MHz (US); 866-868 MHz (EU)
IC type
Alien Higgs-3
Memory configuration
96-EPC bits; 512-bit user memory
Functionality
Read / write (user programmed)
100.000 cycles at 77*F (25*C)
Up to 50 years'
Memory - expected read / write cycles
Data retention
Read rate
400 tags per second for 96-EPC bit number
1 year
Warranty (limited)
on-meta2 ERPsT
Read range on-metal (2W ERPI 2
R~'eadf rang~
Read range oft-metal M2ER P) 2
Polarization
Up to 39 ft (12 ml
Up to 20 ft 16 m)
Linear
Material.
Industry Grade Polymer
Mounting system
Rivet hole, o 0.14 in (3.5 mm), adhesive loptional)
Color
Cool gray
Optional faceplate marking (large volume orders) Faceplate can be laser etched with 1-D or 2-D
barcode, human readable information or company logo
E nv ironmen
t and
industry Complian
RoHS
EU Directive 2002/95/EC
The chtp dala retention is based on chip operating under general emvirA ctdal read range may vary based upion use case and antenna power.
87
mert conditons.
><E2FF
AN
rj
Ill I
%
rWg
/~
\\
/
Operational temperature
Cold
Dry heat
Thermal shock
Application temperature
Cold
Dry heat
Humidity
Operational humidity
Storage humidity
Shock fdrop)
Compression strength
IP classification
prciduct Dm~n~~r~ ~
Dimensions (mm)
tolerance
Dimensions (in)
tolerance
Rivet hole diameter
Weight
1
-40*F (-400 C)
+185*F (+85*C
-40*F to 185OF (-400 C to +85*C; cycled
-40 0 F I-400 CI
+185*F (+854C
5%-95% non-condensing
5%-95% non-condensing
3 ft (1 m) to concrete/granite up to 200 cycles
29 psi (200 kPa)
IP68
W~gri
100 x 26 x 8.9
+/- 0.5
3.94 x 1.02 x 0.35
+/- 0.02
0.137 in +-0.008 13.5 mm +/-0.2)
0.61 oz 17.2 g)
88
A-3
Data collection process plan
A-3.1
Staging area
When an order is placed and ready to be set up, operator must come to the staging area adjacent
to NC turning (across from the restroom).
A-3.2 Staging area close up
When an order is placed and ready to be set up, operator must come to the staging area adjacent
to NC turning. Select the tag that matches the part number and the correct number of tags to
match the lot size. Affix each tag to the appropriate bin (24 or 30 parts per bin available).
89
90
A-3.3
CNC Turning - M1302-2 correct orientation
Tag must face the antenna. Do not put metal between tag and antenna. Must have clear line of sight. Bin
must stay in taped area on job cart. Once the bin is complete, immediately transfer the bin to the Turning
Utility Operator station. When setting up a new job, ensure that the bin for that part type is on the job cart
for the entire duration of the setup.
91
A-3.4 CNC Turning - M1302-2 incorrect orientation
Tag must face the antenna. Do not put metal between tag and antenna. Must have clear line of sight.
92
Only one bin at a time on the job cart. Do not stack multiple bins.
93
A-3.5 CNC Turning - M1309 correct orientation
Tag must face the antenna. Do not put metal between tag and antenna. Must have clear line of sight. Bin
must stay in taped area on job cart. Once the bin is complete, immediately transfer the bin to the Turning
Utility Operator station. When setting up a new job, ensure that the bin for that part type is on the job cart
for the entire duration of the setup.
94
.. ....
..
....
...
A-3.6
CNC Turning - M1309 incorrect orientation
Tag must face the antenna. Do not put metal between tag and antenna. Must have clear line of
sight.
95
Only one bin at a time on the job cart. Do not stack multiple bins.
96
...
....
....
A-3.7 CNC Milling- M1210 correct orientation
When setting up a new job, ensure that the bin for that part type is on the job cart for the entire duration of
the setup. Tag must face the antenna. Do not put metal between tag and antenna. Must have clear line of
sight. Be sure the tool arm is not in the line of sight of the antenna. Bin must stay in taped area on job
cart. Once the bin is complete, immediately transfer the bin to the Milling Utility Operator station with
the bin in the correct orientation.
A-3.8
CNC Milling- M1210 incorrect orientation
Only one bin at a time on the job cart. Do not stack multiple bins.
97
Tag must face the antenna. Do not put metal between tag and antenna. Must have clear line of
sight. Only the current job on the job cart. One bin at a time on the job cart.
Do not put metal between tag and antenna. Must have clear line of sight. Be sure the tool arm is
not in the line of sight of the antenna. Bin must stay in taped area on job cart.
98
.......
..........
..
..
.............
............
..............................
..............
Tag must be oriented so that it faces the antenna. Must have clear line of sight. Be sure the tool
arm is not in the line of sight of the antenna. Bin must stay in taped area on job cart.
99
A-3.9
CNC Milling- M1210 incoming rack
Trays must be on the middle to shelves with the tags facing outward toward the antenna. Do not put metal
between tag and antenna. Must have clear line of sight. Be sure the tool arm is not in the line of sight of
the antenna. Incoming rack must stay in taped area.
100
......
. . ...
....
11,11,11,11,11,11,11,11,11
..........
Trays must be on the middle to shelves with the tags facing outward toward the antenna. Must have clear
line of sight. Do not put metal (carts, fixtures, overhead gantry etc.) between tag and antenna. Incoming
rack must stay in taped area.
101
Trays must be on the middle to shelves with the tags facing outward toward the antenna. Must
have clear line of sight. Do not put metal (carts, fixtures, overhead gantry etc.) between tag and
antenna. Incoming rack must stay in taped area.
102
Trays must be on the middle to shelves with the tags facing outward toward the antenna. Must
have clear line of sight. Do not put metal (carts, fixtures, overhead gantry etc.) between tag and
antenna.
103
Trays must be on the middle to shelves with the tags facing outward toward the antenna. Must
have clear line of sight. Stacking is okay in incoming.
104
.
.........
...
Trays must be on the middle to shelves with the tags facing outward toward the antenna. Must
have clear line of sight. Do not put metal (carts, fixtures, overhead gantry etc.) between tag and
antenna.
105
A-3.10 CNC Milling - M1211 incoming rack
Trays must be on the middle to shelves with the tags facing outward toward the antenna. Must
have clear line of sight. Do not put metal (carts, fixtures, overhead gantry etc.) between tag and
antenna. Incoming rack must be oriented this way so that the bins face the antenna.
Incoming rack may not be oriented this way. Must be turned so that the bins face the antenna.
.... ... ....
.............
................
..............
..
....
.
106
Trays may not be on the top or bottom shelves. Tags must face outward toward the antenna.
Must have clear line of sight. Do not put metal (carts, fixtures, overhead gantry etc.) between tag
and antenna.
107
A-3.11 CNC Milling - M 1211 correct orientation
When setting up a new job, ensure that the bin for that part type is on the job cart for the entire duration of
the setup. Tag must face the antenna. Do not put metal between tag and antenna. Must have clear line of
sight. Be sure the tool arm is not in the line of sight of the antenna. Bin must stay in taped area on job
cart. Once the bin is complete, immediately transfer the bin to the Milling Utility Operator station with
the bin in the correct orientation.
108
A-3.12 CNC Milling - M1211 incorrect orientation
Only one bin at a time on the job cart. Do not stack multiple bins. Tag must face the antenna. Do not put
metal between tag and antenna. Must have clear line of sight. Only the current job on the job cart. One bin
at a time on the job cart. Must have clear line of sight. Be sure the tool arm is not in the line of sight of the
antenna. Bin must stay in taped area on job cart.
109
A-3.13 CNC Milling - Utility Operator correct orientation
Tag must face the antenna. Bins may be stacked. Do not put metal between tag and antenna. Must have
clear line of sight. Only place bins with tags on the specified rack. Once a bin has been checked into SAP,
remove the tag. Put the tag into the blue metal box. Close the blue metal box. Do not open the blue metal
box again until it is in the bin staging area. Take the bin to the next stage (cleaning/supermarket).
Tag must face the antenna. Bins may be stacked. Do not put metal between tag and antenna. Must have
clear line of sight. Only place bins with tags on the specified rack. Once a bin has been checked into SAP,
remove the tag. Put the tag into the blue metal box. Close the blue metal box. Do not open the blue metal
box again until it is in the bin staging area. Take the bin to the next stage (cleaning/supermarket).
110
.............
A-3.13 CNC Milling - Utility Operator incorrect orientation
Tag must face the antenna. Bins may be stacked. Do not put metal between tag and antenna. Must have
clear line of sight. Only place bins with tags on the specified rack.
Tag must face the antenna. Bins may be stacked. Do not put metal between tag and antenna. Must have
clear line of sight. Only place bins with tags on the specified rack.
111
References
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Limited-Capacity Manufacturing System," M.Eng. Thesis, Massachusetts Institute of
Technology, Cambridge, MA.
[2] Puszko, G., 2014, "Efficient Scheduling to Reduce Setup Times and Increase Utilization in a
Multiple-Part Manufacturing System," M.Eng. Thesis, Massachusetts Institute of Technology,
Cambridge, MA.
[3] Pinedo, M. L., 2005, Planningand Scheduling in Manufacturingand Services, 2nd Ed.,
Springer, New York.
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AtlasRFIDstore." AtlasRFIDstore.com.N.p., n.d. Web. 09 Aug. 2014. http://www.atlasrfidstore
.com/Impinj Speedway Revolution R420 UHF RFID Reader p/ip-rev-r420.htm
[9] "MITx." Maxwell's Equations. MITx, n.d. Web. 09 Aug. 2014. https://6002x.mitx.mit.edu/
wiki/view/MaxwellsEquations/
[10] "MITx." JoulesLaw. MITx, n.d. Web. 09 Aug. 2014. https://6002x.mitx.mit.edu/wiki/view/
JoulesLaw/
[11] "Omni-ID Power 400 Active RFID Tag I Omni-ID Power_400 I AtlasRFIDstore."
AtlasRFIDstore.com.N.p., n.d. Web. 09 Aug. 2014. http://www.atiasrfidstore.com/
Omni ID Power 400 Active RFID Tag p/omni-id power 400.htm
[12] Monden, Yasuhiro. Toyota ProductionSystem: An IntegratedApproach to Just-in-time.
Norcross, GA: Industrial Engineering and Management, 1993. Print.
[13] Cargo Trak (n.d.): Xerafy Datasheet. 03 July 2014. Web. http://www.xerafy.com/
userfiles/uploads/datasheets/Cargo%20Trak%20Datasheet.pdf
112