MANAGEMENT
SYSTEMS FOR
MANUFACTURING
Information Flow in Manufacturing
 The ‘flow of materials’ is a basic indispensable function in manufacturing.
 This flow is accompanied by the ‘flow of costs’ and the raw materials are converted
into products with increased value.
 The driving force of this function is the ‘flow of information’.
 The flow of materials which accompanies the flow of costs proceeds according to the
instructions from the flow of information based on market needs.
Information-flow Process
The information-flow process in the manufacturing system is usually called
manufacturing (or production) management', it proceeds with the following steps
(I) strategic production planning;
(II) operational production management;
(1) aggregate production planning;
(2) production process planning;
(3) production scheduling;
(4) production implementation;
(5) production control
II-4: production implementation (the production function) is an actual activity of
production/fabrication in a workshop; it is the ‘flow of materials’ through production
operations and processes. II-2: production process planning (the design function) is
concerned with intrinsic (or pure) production technology as well; it is called the
‘flow of technological information’.
Other activities—
I: strategic production planning,
II-1: aggregate production planning,
II-3: production scheduling, and
II-5: production control (the [strategic]
management function)—constitute a series of management [systems]; i.e. the ‘flow
of managerial information,’ as mentioned.
• In particular phase I treats the ‘flow of administrative, or strategic management
information’.
• The structure and operating procedures of the above information flow in an online,
real-time basis with the use of computer facilities and information technology
are called manufacturing information systems.
• This part is concerned with this type of information flow.
Decision Problems in Managerial Information Flow
Decision Problems in Managerial Information Flow Issues in Strategic Production Planning
Strategic issues treat matters concerning the relationship between the system and its
environment, usually in the long term (5 to 10 years).
The main problems of strategic production planning are:
• new product planning:
• long-term profit planning:
• capital investment for new plant construction:
• international manufacturing:
Basics of Multiple-objective Optimisation
Formulating Multiple-objective Optimisation
It is quite usual that multiple objectives are aimed in practice, e.g. at production
planning. This type of decision is made by multiple-objective (or multicriterion)
optimisation. This is to maximise (or minimise) a vector:
(#i(x), g2(x), ..., gK(x))
Difficulty of Multiple-objective Optimisation
The difficulties of multiple-objective optimisation are:
• noncommensurability among objectives— difficult to measure objective values
with a common dimension;
• noncompatibility among objectives— objectives are inconsistent and are in a
trade-off relationship.
Hence it is very rare to be able to obtain a supremal solution
Pareto Optimum
Usually multiple-objective optimisation generates the Pareto optimum
Existence of a supremal solution— this
solution maximises every objective.
Decision Problems in Aggregate Production Planning
Aggregate production planning determines the kinds of product items and the
quantities to be produced in the specified time periods. The important problems
involved in this field are:
• short-term production planning;
• long-term production planning;
• optimal product mix;
• lot-size analysis;
• material requirements planning (MRP);
• production smoothing;
• production forecasting.
Decision Problems in Production Control Production control reduces or eliminates the
deviations of actual production performances from the production standards (plans
and schedules). Important problems to be solved are:
• process control:
• inventory control:
• quality control:
• cost control:
Aggregate Production Planning
Since available resources for production, such as raw materials, machines, labour forces,
funds, etc. are limited, it is desirable to allocate effectively and utilise those production
resources to determine optimal kinds and quantities of products to manufacture.
This is [aggregate] production planning in a specified time period (usually a comparatively
long range, such as month or year units).
Short- and Long-term Production Planning
If the time range is short, usually less than a year, this is short-term (or range) (or static)
production planning; the time factor is not considered in this case
Short-term Production Planning
 Linear-programming Model for Short-term Production Planning
 Mathematical Model for Production Planning by Linear Programming
Suppose we want to produce N kinds of products (or parts) with M kinds of
production resources.
Let us assume that a i; units of resource i(= l, 2, ..., M) are
required to produce a unit of product y(=l, 2, ..., N), from which c, units of profit
are gained.
If only b, units are available for resource i, determine an optimal product mix and optimal
production quantities.
Linear Program [ming]
maximise z = 4x-, + 9x2
subject to 4x, + 10x2 « 40 . . (a)
5x, + 7x 2 « 35 . • (b)
16x, + 5x 2 =£ 80 . . (c)
Xi, x2 s 0.
Graphical Solution for Linear Programs
If there are only two decision variables in the LP problem, as in the above
example,
it is convenient to obtain an optimal solution by the graphical method.
Four Categories for LP Solutions
classified into the following four categories:
(1) a unique solution, which is located on an extreme point of the feasible region
(2) an infinite number of optimal solutions which are located on a boundary of the
feasible region;
(3) no feasible solution, hence, no optimal solution;
(4) unbounded feasible solutions, and the optimal solution is infinite.
Introduction to the Simplex Method
Basics of the Simplex Method
• In cases where a linear program contains more than three decision variables, the
graphical approach to solving linear programs is impossible; hence, they should be
solved analytically. A most famous and representative analytical solution procedure
is the simplex method
 The simplex method is an iterative procedure to obtain an optimal solution for
linear programming problems in a finite number of steps.
 It starts from an initial basic feasible solution and repeats the pivoting process
moving iteratively to a better basic feasible solution by replacing a basic variable
with a non-basic variable one by one.
 The algorithm below is for solving a well-structured linear program; in this
 solution procedure the simplex table
Techniques of Multiple-objective Optimisation
Basic Solution Methods for Multiple-objective Optimisation
(1) Parametric (or combined) method
(2) Lexicographic (or priority) method
(3) Satisficing method
(4) Global (or compromise) evaluation method.
(5) Fuzzy-programming method
(6) Goal-programming (or goal-attainment) method
Product Mix Analysis
Optimal Product Mix
The product mix determines the proper combination of the kinds of items to be
produced with the existing production capacity. If there is insufficient capacity to
produce the entire amount of products demanded, such a demand cannot be
sufficiently fulfilled. In such a case, the optimal product mix—optimal kinds of
products and their production quantities— should be properly selected. Some of the
policies employed to solve this problem are to:
• maximise the total profit obtained by production under the capacity constraint;
• maximise the total amount of products produced even at the expense of the profit
gained; etc.
 Lot-size Analysis
 Intermittent production, which is often called lot (or batch) production.
 It is a type of production taken when the demand for a commodity is small
compared with the production capacity, namely, the production rate.
 The product is then manufactured periodically in a quantity which will meet the
demand for some time until the next production run is resumed. In the interval
between two production runs the production facilities may be employed for other
work in a similar fashion.
 This quantity, to be manufactured periodically, is called a lot (or batch) size.
 The basic problems in this sort of intermittent production are to determine the
optimal (or economic) lot (or batch) sizes for the products and to decide the
production cycles or the order of production in which those economic lot sizes
are run.
Multi-product Cyclic Lot Production
In the case that consistent and continuing demands occur for certain multiple-
product items, the equipment must be assigned to the multi-product cyclic lot
production.
If each product is manufactured once each cycle, the problem is to determine the
production cycle and the economic lot sizes for all the products of M items to be
produced.
A subscript j is used in the previous nomenclature to indicate the association with
product j.
The total variable cost made up of the setup cost and
the inventory-holding cost is minimal at the optimum lot
size, Q *.
Material Requirements Planning (MRP) and Machine Loading
What is MRP?
1. Material requirements planning (MRP), and manufacturing resource planning
(MRPH) are widely used systematic procedures using computer based software for
production planning based upon the parts explosion, which was described in Section.
2. They are usually applied to a large job-shop production in which multiple products are
manufactured in lots through several processing stages.
3.First, MRP establishes a master production schedule (MPS), which is an aggregate plan
showing required amounts vs. planning periods for multiple end items to be produced.
4.Then, with the use of a bill of materials and an inventory file, production information for
dispatching multiple jobs is generated on the hierarchical multiple-stage manufacturing
system by taking into account commonness and possible substitution of many parts needed
to assemble the multiple products.
5.Thus, MRP schedules and controls a total flow of materials from raw materials to
finished products on a time basis (the unit of time is called a time bucket) usually,
this is one week).
CRP
The possibilities of assembling products and machining parts are also examined in
relation to the capacity of production facilities. This function is called capacity
requirements planning {CRP), as is also indicated in Figure
The General Procedure of MRP
MRP is made by the following considerations.
(1) Calculating the requirements for products/parts. The first step of MRP is to
calculate the gross and net requirements for products/parts to be produced in
the specified periods (time bucket; usually weekly basis3) as established in
MPS. The gross requirement for parts can be obtained by multiplying the
quantities of products indicated in MPS by the required number of the part
shown in the bill of materials. By subtracting the inventory (including work-inprocess)
from the gross requirement, the net requirement is easily obtained.
(2) Making up the lot. In the case that a certain product or part is required to be
produced in sequential periods, an appropriate lot size is decided. There are
several ways to make up the lot:
(a) Economic-order-quantity (EOQ) method. A method to minimise the sum
of ordering cost and the inventory -holding cost. This quantity, EOQ, can
be obtained through a similar analysis of production lot size, as follows:
Q* = V(2cs£>/ch)
where D is the annual demand in units, cs is the cost of placing an order,
and ch is the annual carrying cost per unit inventory.
(b) Periodic-order-quantity method. Determines the time interval of two
succeeding orders by dividing the EOQ calculated using (13.74) by the
average demand in the planning horizon.
(c) Economic-part-period (or least-total-cost) method. Determines a lot as a
demand in the period such that the cumulative ordering cost equals the
cumulative inventory cost. Supplementing the adjusting routine (called
‘look-ahead/look-back’) to this method constitutes the part-periodbalancing
method, in which the order determined by the economic-partperiod
method is implemented in the previous or the next period in order
to cope with sudden change of demands.
(d) Wagner-Whitin method. Determines the optimal making-up periods for a
lot so as to minimise the sum of ordering and carrying costs by dynamic
programming. Incidentally the least-unit-cost method minimises the unit
product cost, and the Silver-Meal method minimises the cost required in
a single period.
(e) Lot-for-lot method. A method to simply order the amount of the demand.
(3) Subtracting lead times. By subtracting lead times from the period, in which the
product/part is required, the start time for producing every product/part is
established. For a piece part (unit) constituting a complex part (sub-assembly),
first the start time for that complex part is determined, and then the start time
for the piece part is calculated.
Production Scheduling
Meaning of Production Scheduling
Production scheduling is a function to determine an actual (optimal or
feasible) implementation plan as to the time schedule for all jobs to be
executed; that is, when, with what machine, and who does what operation?
Production scheduling for those cases, where jobs to be processed on
different facilities are independent of each other, as in machining lines, is
called operations scheduling.
Gantt Chart
This is a powerful management tool invented by H.L. Gantt in the early 20th
century for planning and controlling jobs and operations
Project Scheduling
In scheduling, long-range decisions sometimes have to be made, as for example
in scheduling the construction of a new factory, a large ship, a building, a highway,
etc.
In this case, scheduling covers a relatively long term; hence, very short periods such
as seconds or minutes are not pertinent to this scheduling problem.
However, since many and varied kinds of work elements are contained in this
type of scheduling, a reasonable schedule must be made for a smooth and effective
implementation of production. Such a scheduling decision is called project (or
network) scheduling.
Typical solution techniques for solving this type of scheduling problem are
the program evaluation and review technique (PERT), critical path method (CPM),
and the more recent and more flexible technique known as the graphical evaluation
and review technique (GERT).
Operations Scheduling
Scope of Operations Scheduling
Meaning of Operations Scheduling
Operations scheduling is the allocation of jobs to be processed on the corresponding
machines, in a given time span, for a workshop consisting of several machines or
production facilities including operative workers.
A job is a product or part to be completed. For that a piece of raw material is converted into
a finished part through a single or multiple stages, on each of which an operation is run,
such as turning, drilling, grinding, setup, etc. on a suitable machine tool or by a skilled
worker.
Hence, a job is a task made up of multiple operations or work elements arranged in
technological order.
Types of Operations Scheduling
(1) Job sequencing—determining the order of processing jobs waiting to go on a
single machine.
(2) Flow-shop scheduling— scheduling for a flow shop, where the sequence of machines
according to multiple-stage manufacturing is completely identical for all jobs to be produced.
This type of flow pattern is typical for mass production.
(3) Job-shop scheduling— scheduling for a job shop, where the sequence of machines differs for
each job. This shop is typical for the case of varied production of most jobbing types and some
batch types.
Preliminary Analysis of Operations Scheduling
Preconditions for Operations Scheduling
For simplicity of analysis a static state is in most cases considered, where a set of M
independent jobs is available for processing at time zero (e.g. 8:00 a.m. on Monday).
If those jobs randomly enter the shop floor, this situation is dynamic.
Single-machine Scheduling— Job Sequencing
Job Sequencing
Single-machine scheduling is a most basic problem in operations scheduling.
This situation occurs when many jobs are waiting for processing prior to a machine tool,
when many programs are waiting for processing in front of a computer, when an entire
plant can be regarded as a single machine such as a process (chemical) industry, etc.
Flow-shop Scheduling
Basic Propositions for Flow-shop Scheduling
In solving flow-shop scheduling problems, wherein all jobs are processed through an
identical sequence of multiple machines arranged in series, two properties given
below are dominant.
Here a regular measure of performance is an amount to be minimised that is expressed as a
function of job completion times and increases only if at least one of the completion times
increases. Scheduling criteria introduced so far are mostly regular measures.
Two-stage Flow-shop Scheduling—Johnson's Method
Minimising the total flow time, Fmax, for a two-stage (machine) flow shop is the most
basic and classic problem in the field of flow-shop scheduling. S.M. Johnson
developed a very useful and simple algorithm for solving this scheduling problem as
follows. In this problem we have tn and ti2 as the processing times for the first and
second operations of job J, (i = 1, 2, ..., M).
Johnson’s algorithm for the minimum makespan two-stage flow-shop scheduling:
Step 1 : Find the minimum processing time among a set of jobs that are not yet
sequenced. (In case of a tie, select arbitrarily.)
Step 2: If it requires machine 1 (or 2), place the associated job in the first (or last)
position in the sequence. When all positions in the sequence are filled,
stop. Otherwise, proceed.
Step 3: Remove the assigned job from consideration and return to Step 1.
Dispatching Rules
• When more than one job is in a queue in front of a machine, the order of processing
such jobs must be determined to specify what the machine should do next at the
completion of any job.
• Such scheduling decisions in a dynamic job-shop environmentare usually determined by
means of scheduling disciplines called dispatching certain appropriate priority rule for a
dynamic job shop.
• Typical dispatching rules that are frequently used in practice are:2
• • Shortest-process-time (SPT) rule. Jobs are processed in the increasing order of
• imminent operation times.
• • Least-work-remaining rule. Jobs are processed in the increasing order of total
• processing times remaining to be done.
• • EDD rule. Jobs are processed in the increasing order of due dates.
• • First-come, first-served ( F C F S ) rule. Jobs are processed in the order of arrival at
• the machine.
• Minimum-slack-time rule. Jobs are processed in the order of non-decreasing slack
times, where slack time signifies the remaining time obtained by subtracting the
remaining processing times from the time between the present time and the due
date.
•
Maximum-number-of-operations rule. Jobs are processed in the order of
decreasing number of remaining operations.
• Minimum-number-of-operations rule. Jobs are processed in the order of
increasing number of remaining operations.
• Penalty rule. Jobs are processed in the order of decreasing delay cost.
• COVERT (cost-over-time) rule. Jobs are processed in the order of decreasing
ratio of delay cost over processing time.
Project Scheduling—PERT and CPM
Techniques of Project Scheduling
A project is usually thought of as a one-off effort, and project scheduling is distinguished
from operations scheduling in job shops by the non-repetitive nature of the work, such as the
construction of a building, a ship, research and development (R & D) programs, and the like.
The most popular techniques for this type of scheduling for large-scale projects are:
• PERT (program evaluation and review technique). Feasibility study of the project
completion date is a major concern (PERT/time); in some cases, activity costs are taken
into‘account (PERT/cost).
• CPM (critical path method). The optimal schedule is established by trading off required
times and costs for performing activities.
• GERT (graphical evaluation and review technique). Network problems of a stochastic nature
are solved by a procedure combining flow-graph theory, the semi-Markov process and PERT
principles.
Structure of Arrow Diagram
An arrow diagram is made up of:
• arrows (or arcs) representing the activities or jobs of the project under
consideration;
• nodes (or events) indicating the intersection (start or completion) of activities,
and presenting the structure and the precedence relationships among activities.
Basic Rules for Constructing Arrow Diagrams
In construction of the network, information about all activities (times and costs
required) included in the project and their precedence relationships is needed, and
the following basic rules should be satisfied:
• Each activity is represented by one and only one arrow (see Figure 14.10).
• The length of the arrow has no meaning with respect to duration or cost of an
activity relating to the arrow.
• The network should have a unique starting node (origin or source).
• The network should have a unique completion node (terminal or sink).
• The nodes should be numbered so that the number increases in the direction of
arrows (topological ordering or event numbering).
• No activity should be represented by more than one arrow in the network.
• No more than one arrow should be drawn between two successive nodes. (A
dummy activity represented by a broken line is used to avoid violation of this
rule. This activity needs neither time nor cost.)
• Activities originating at a node can only begin after all activities terminating at
the node have been completed.
Inventory Management
‘Inventory’ plays the role of buffer— s t o c k — in the
material f l o w from the acquisition of raw materials to the finishing
of products.
Inventory management/control is made so as to:
• reduce inventory costs;
• stabilise the production level;
• increase the service level by preventing product shortages to
customers
Inventory in Logistics
Inventory is one of the basic functions concerning an integrated material flow. Other basic
functions are:
• procurement;
• production;
• distribution;
• sales.
Stocks piled between procurement and production, in the production processes (stages)
and between production and sales are called the [ r a w - ] m a t e r i a l i n v e n t o r y ,
the [ w o r k - ] i n - process inventory and the product inventory ,
Objectives and Policies of Inventory Analysis
In an inventory analysis, an attempt is usually made to minimise
the total costs
incurred such as:
• acquisition or production cost;
• inventory-carrying cost;
• penalty associated with shortages, etc.
Basic decision factors for attaining these aims of inventory analysis
are
• reorder date (or point) P, and
• reorder quantity Q.
Inventory Models
The states of the above two factors produce various inventory
models:
• the fixed-order quantity system, frequently referred to as the
Wilson model: the most basic, classical model wherein the economic
order quantity Q* which minimises the total variable costs of
managing inventory is optimally determined and this quantity is
ordered whenever the inventory on hand drops to a particular
level, referred to as the reorder point. Hence P varies.
• the replenishment system: P is fixed and Q varies according to the
inventory level so as to fill the replenishment level.
• the (S, s) system: both P and Q vary.
Inventory in Practice
Practical inventory policies include the following:
 Two-bin system. The items are stored in two equal-sized bins. The demands
are filled by one of the bins; when it becomes empty, the other bin is
employed and parts are procured to fill the empty bin, e.g. inexpensive
fasteners stocked in bulk in a two-bin system and on free issue to production.
 • ABC inventory classification (or analysis). It is common that 5-20% of
inventory items incur 50-60% of the inventory expense; these items are called
‘A-items’, and stock-leads are controlled accurately. ‘B-items’ typically account
for 25% of the remaining inventory expense and represent 30-50% of the entire
items. The remaining ‘C-items’ are of low cost, and control of their inventory is
relaxed (S
Inventory Systems
Economic Order Quantity for Fixed-order Quantity Inventory Systems
The e c o n o m ic o r d e r q u a n tity (E O Q ), which plays a fundamental role in
the f i x e d - o r d e r q u a n tity [ in v e n to r y ] s y s te m , minimises the cost of
managing inventory. In determining this quantity the model assumes that the cost
is made up solely of two components:
• ordering cost;
• carrying cost.
Hence the objective function is formulated:
U = c0D/Q + chQ/2
where co is the cost of placing an order, ch is the annual carrying cost per unit of
inventory, and D is annual sales or demand in units (uniform distribution). ch is
often expressed:
ch= c pr
where cp is the unit purchase or production cost of an item and r is the carrying
cost expressed as an annual percentage of this unit cost— annual inventorycarrying charge.
Then the EOQ is obtained by differentiating (15.1) and setting it equal to zero:
Q* = square root of 2 C0 D/ch.
As in the case of the economic lot size mentioned in Chapter 13, it is usual that
the inventory-cost curve is shallow near this EOQ; hence it is relatively insensitive
to deviations from the EOQ.
In this model, the number of reorders a year is:
n = D/Q* = Square root ChD)/2C0
and the minimum annual cost is:
U* = Square root of 2C0 ch D.
Reorder Point
The reorder point P is the point at which to reorder the above quantity Q*. It
is made up of the average expected sales during the lead time— the time lag
between placing and fulfilment of a reorder— and the safety stock preventing
a temporary out-of stock condition which would lead to late deliveries and
probably result in lost sales
The fixed-order quantity inventory model.
where d is the average daily sales in units, L is the lead time in days, and Q0 is
safetystock in units, and is given by:
where ad is the standard deviation for daily sales and d a is the reliability level
for satisfying demands, e.g. d0 95 = 1.65 for satisfying 95% of demands.
The Replenishment Inventory System
In the replenishment system there is no fixed reorder quantity; instead,
inventory is viewed at periodic intervals, and if there have been any sales
since the last review, an order is placed. The order quantity is equal to the
amount by which a fixed replenishment level exceeds the actual inventory
level at the time of the review
The replenishment inventory model. Reorder is made at periodic intervals
R so as to fill the inventory up to the replenishment level S. L is lead time.
Service Level
Sometimes, in practice, all demands may not be met, since the amount of safety
stock held depends on a certain measure of the maximum expected demand and
the risk and associated cost of failure of the service. To prevent the shortage and
express the measure of service of shipping the products to the customers, we
often use the service level, which is defined as follows:
Probabilistic Inventory Models
Probabilistic Aspect of Inventory
In practice, the future demands are not deterministic, hence they are to be of a
probabilistic (or stochastic) nature. It is assumed in this section that the future
demand of an item, x, is continuous with density function /(x).
Then a probabilistic inventory model determines the optimum value of inventory
at the start of a period— ‘initial inventory’ Q, so as to minimise the total cost.
Just-in-time (JIT) Production
JIT and Kanban
Just-in-time (JIT) refers to the production and supply of the required number of
parts at the right time, just as required, in order to minimize work-in-process
inventory. This production system is the ‘pull[-through] system’ rather than the
usual ‘push system’. By inputting the production schedule only at the final stage,
the demanded items are withdrawn from the previous stages with the circulation
of kanbans (instruction cards), on which the items and their quantities needed
are indicated
Preliminaries to JIT Production
In order to implement JIT production effectively, the following conditions should
first be imposed:
(1) Standardise the individual operations.
(2) Provide U-shape layouts such that each operator can handle more than one
machine
Effectiveness of U-shape layout)
(3) Multiple-job work by each operator. Job allotment to each individual
operator changes frequently (e.g. every week); every operator is required to
master multiple jobs. Again there are implications for job enrichment and
satisfaction.
(4) Reducing setup times. A typical objective requires that setup is completed
within ten minutes (called ‘single’ setup). This may reduce batch sizes and
work-in-progress inventory.
(5) Production smoothing. By this procedure stabilisation of a process for
multipleproduct, small-batch production is achieved. In this mode the cycle
time— the required time per unit of production— is decided by dividing the
available time by the production quantity, as formulated in equation (8.20a).
(6) ‘Jidoka’ (self-actuation). When unusual events happen in a production line,
the worker in charge stops the line and removes the cause of trouble.
(Production smoothing for multiple-product production) Suppose that a car-
maker produces 10 000 of model A, 4000 of model B, and 6000 of model C in a
month. There are 20 work days, the work conditions being 2 shifts and 8 hours a
day. In this case, the three models are to be produced at the rate of 500 for A,
200 for B, and 300 for C a day. Then the average production time is calculated as
follows:
JIT Production Process
As mentioned previously and as shown in Figure 16.4, the master production plan
which has been decided through aggregate production planning is given only to the
final stage of the whole production process. In this system, workers perform the
required work on the material provided by the preceding workstation at the necessary
time; it is hence called the pull[-through] system. For the purpose of providing
production information, two kinds of kanban (instruction card) are used. These
include ‘withdrawal’ and ‘production ordering’ information.
The withdrawal kanban indicates the items (parts) and their quantities to be brought from
the previous stage to the current workplace. Once this kanban is issued, the items and
their quantities indicated on the kanban are withdrawn and brought to the current
workplace together with the kanban.
Then the production kanban is taken from the box which has stored the item parts
withdrawn for the next stage. According to the instruction indicated by this production
kanban production starts and continues until the quantities of the with
The pull-through type JIT production system
Issues of JIT Production
It is commonly believed that JIT production is very efficient; however, several issues
or demerits are pointed out
Productive Maintenance
Failure and Productive Maintenance
Types of Failure
With age and use, physical productive facilities are susceptible to reduction
or complete stoppage of their capability. This phenomenon is deterioration or
failure.
Failures can be classified under two headings, according to their nature.
(1) Wear-out failure arises as a result of a gradual change in the condition of
the facility as a consequence of aging or wearing, and is often accompanied
by loss of quality of the part being produced.
(2) Catastrophic (or chance) failure, a sudden cessation of functioning of the
equipment due to abrupt change in the wear-out failure may occur. This is
inevitably accompanied by an inability to continue manufacturing with the
facility.
Productive Maintenance and Total Productive Maintenance (TPM)
Types of Productive Maintenance
Productive maintenance is usually classified under the following three headings:
(1) Breakdown (or emergency) maintenance—often called repair. This is a
maintenance operation performed after the facility has broken down or after it has
deteriorated to a degree which renders proper productive operation impossible.
Effective breakdown maintenance is aimed at reducing the repair time.
(2) Preventive maintenance—often abbreviated PM. This is a maintenance
operation performed to prevent and eliminate the complete breakdown of the
facility by taking appropriate actions according to predetermined maintenance
schedules. Effective preventive maintenance reduces the number and often the
severity of failures.
(3) Corrective maintenance. This is a maintenance operation performed to raise the
capability of the facility by investigating causes of failures and improving it so