Module 1 MPS

8/9/2019 Module 1 MPS

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ME3101 Management of Production Systems

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MANAGEMENT OFPRODUCTION SYSTEMS

(ME 3101)

Course Note

Prepared by: Dr. Vinay V. Panicker

Assistant Professor,

Department of Mechanical Engineering

National Institute of Technology CalicutCalicut673 601, Kerala, India


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MODULE 1

1. INTRODUCTION

The systems aspects of manufacturing are more important than ever today. The word manufacturing

was originally derived from two Latin words manus (hand) and factus(make), so that thecombination means make by hand.

In this way manufacturing was accomplished when the word first appeared in English around

1567. Commercial goods of those times were made by hand. The methods were handicraft,

accomplished in small shops and the goods were relatively simple. As many years passed, the

products become more complex along with processes. Thus factories were developed with many

workers at a single site; the work was organized using machines

Modern manufacturing enterprises that manage these production systems must cope with the

economic realities of the modern world. These realities include the following:

Globalization

International outsourcing

Local outsourcing

Contract manufacturing

Trend toward the service sector

Quality expectations

Operational efficiency

1.1. System: It consists of elements or components. The elements or components are interlinked

together to achieve the objective for which it exists. Eg: human body, educational

institutions, business organizations.

Figure 1.1 Transformation process

Components of a system: The input, processing, output and control of a system are called

the components of a system.

Control: There are two types of control, namely Proactive Control and Reactive Control.

There are three types of feedback mechanisms such as feed forward control, feedback control

and concurrent control

TransformationProcess

Inputs Men Material Machine Money Information

Outputs Desirable Undesirable

Feedback


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2. What is Production management?

In any manufacturing system, the job of a Production Manager is to manage the process of converting

inputs into the desired outputs.

It is concerned with the production of goods and services, and involves the responsibility ofensuring that business operations are efficient and effective.

It is also the management of resources, the distribution of goods and services to customers.

Therefore, Production Management can be defined as the management of the conversion process,

which converts land, labor, capital, and management inputs into desired outputs of goods and

services. It is also concerned with the design and the operation of systems for manufacture, transport,

supply or service.

3. Difference between Operations and Production

In the transformation process, the inputs change the form into an output, by adding value to the entity.

The output may be a product or service.

If it is a product centric that is known as production,

If it is a service centric then that is known as operation.

4. Production System

A production system is a collection of people, equipment, and procedures organized to perform the

manufacturing operations of a company (or other organization)

4.1.Components of a production system:

There are two components for a production system such as:

1. Facilities the factory and equipment in the facility and the way the facility is organized

(plant layout)

2. Manufacturing support systems the set of procedures used by a company to manage

production and to solve technical and logistics problems in ordering materials, moving work

through the factory, and ensuring that products meet quality standards

Figure 1.2 Diagrammatic representation for a production system


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Facilities include the factory, production machines and tooling, material handling equipment,

inspection equipment, and computer systems that control the manufacturing operations. For the

facilities, plant layout is a significant factor for the production system to be efficient. The plant layout

is the way in which the equipment is physically arranged in the factory

Manufacturing systems include the logical groupings of equipment and workers in the factory. Acombination of a group of workers and machines are termed as Production line. There can be

instances where there is only one worker and a machine. This arrangement is called as Stand-alone

workstation and worker. Based on the human participation in the production processes, the

manufacturing system can be classified as the following three systems:

Manual work systems- a worker performing one or more tasks without the aid of powered

tools, but sometimes using hand tools. For example, filing work carried out in the central

workshop

Figure 1.3a Diagrammatic representation a manual work system

Worker-machine systems- a worker operating powered equipment.For example, turning done

on a work piece using a Lathe.

Figure 1.3b Diagrammatic representation a worker-machine system

Automated systems - a process performed by a machine without direct participation of a

humanFor example, turning done on a work piece using a CNC machine.


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Figure 1.3c Diagrammatic representation an automated system

Manufacturing support systems: To operate the production facilities efficiently, a company must

organize itself to design the processes and equipment, plan and control the orders and satisfy product

quality requirements. The support systems have no direct contact with the product, but they plan and

control its progress throughout the factory. The manufacturing support system involves a cycle ofinformation-processing activities that consists of four functions. The four functions are depicted in

Figure 1.4.

Figure 1.4 Information processing cycle

i. Business functions- sales and marketing, order entry, cost accounting, customer billing

This function is the principal means of communication with the customer

This represents the beginning and the end of the information-processing cycle

It is at this function, the customer comes in contact with the company and places an order

The production (or customer) order will be (1) order to manufacture an item to customers

specifications (2) customer order to buy one or more of the manufactures product and (3)an internal company order based on a forecast of future demand.


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ii. Product design- research and development, design engineering, prototype shop

The role of the product design team depends on the production order. As mentioned above,

the production order may change.

iii. Manufacturing planning- process planning, production planning, MRP, capacity planning

Process planning is the sequence of individual processing and assembly operations needed

to produce the part.

Production planning considers the logistics issues in the production process

The authorization to produce the product must be translated into the Master Production

Schedule (MPS)

MPS is a list of products to be made, the dates on which they are to be delivered, and the

quantities of each are included

Based on the MPS, individual components and the sub-assemblies that make up each

product must be planned.

MPS must not list more quantities of products than the factory capacity for a period.

Capacity planning plans the manpower and machine resources of the firm.

iv. Manufacturing control- shop floor control, inventory control, quality control

Managing and controlling physical operations in the factory to implement plans.

Shop floor control monitors the progress of the product as it is being processed, assembled,

moved and inspected in the factory

o Materials being processed in the factory are called as Work-in-process (WIP)

inventory.

o Both shop floor control and inventory control overlap each other.

Inventory control tries to strike a balance between the risk of too little inventory

(stock-out situation) and the carrying cost of too much inventory.

o Right quantity to order and when to re-order a given item

Quality control ensures the quality of product and its components meet the standards

specified by the product designer.

o Raw materials and component parts from outside sources are inspected when

they are received and final inspection and testing is done to ensure functional

quality and appearance.

Exercise: Refer the Textbook Automation, Production systems and Computer Integrated

Manufacturing by Mikell P. Groover Chapter 2:Exhibit on History of Manufacturing


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4.2.Aim of production: The aim of a production system is to provide goods and services for

mankind

In right quantities

At the appropriate place

At the desired time

With the required quantity

At a reasonable cost

4.3. Challenges in manufacturing : The challenges in manufacturing includes

Changing market conditionsshift from sellers market to buyers market

Rate of change is faster

Global competition

Need to be pro-active

Increased customer focusthe customers are less loyal.

4.4. Characteristics features of production system

Production is an organized activity.

The system transforms the various inputs into useful outputs.

Production system does not operate in isolation from the other organizational

systems.

There exists a feedback about the activities which is essential to control and

improve system performance.

4.5. Classification of production systems

The production system can be classified on the basis of the following:

Type of productionJob shop production, Batch production, Mass production

Low Variety High

High Uniformity Low

High Volume per output Low

Figure 1.5 (a) Type of production system

Size of the plantLarge size plant (eg. Oil refinery), Medium size plant, Small size plant(eg. Printing press)

Types of

productionsystem

Continuous

production

Flow

production

Mass

production

Intermittent

production

Batch

production

Job

production

Project

production


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There are three possible situations

o A batch is manufactured only once (make-to-order)

o Batch is repeated at irregular time intervals (make-to-order)

o Batch is repeated at regular time intervals (make-to-stock)

Final product is usually standard. The basic design is same.

Such production of standardized items on a continuous basis is called repetitive

production.

Customer may be external or internal. For example, in an automobile plant, the engine

assembly plant will be an internal customer for gear assembly plant)

Machines and resources must be of general purpose or semi-automated.

Skilled workforce is needed to work on product variety.

Less supervision is need in comparison with job-shop

Less flexible than job-shop

Machines are grouped as per their functional capabilities.

4.8. Mass production

The demand rate is more than the rate of production.

Similar product is manufactured and hence, standard method and time standard is to be

analysed.

Most of the machines used in mass production are special purpose. The equipment is

dedicated to the manufacture of a single product type such as light bulbs, medicines etc.

The system is capital intensive and a long term planning needed before the investment.

Semi-skilled labour is only needed as the product design is similar mostly.

This system is a rigid production system.


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Figure 1.5 (b) Process flexibility Vs. Product variety

5. Decisions in Production Management

Production management involves the following major decisions:

1.Strategic Decisions: are taken at top level management. Examples for strategicdecisions include

Warehouse location

Distribution systems

Building a new plant

Growth strategy for expansion - Mergers and Acquisitions

New product planning

Compensation planning

Quality assurance planning

R&D planning

Adoption of New technology

Dropping a product from the product mix

Corporate Social Responsibility (CSR)2.Tactical decisions: are taken at the middle level of management. Few examples of such

decisions are

Pricing a product

Product improvementvalue analysis

Preventive maintenance policy

Budget analysis

Short term forecasting


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Make or buy analysis

Plant layout

Buying equipments

Project scheduling3.Operational decisions: are taken at the first-line level of management. Few examples of

such decisions are Design of sampling planinspection of raw materials and products

Price discount at salesman level

Production Scheduling

Maintenance manpower scheduling

Machine loading

Order entry

Inventory control

6. Forecasting

The first step of planning is forecasting

Estimating the future demand for products, services and resources necessary to

produce these outputs.

Forecasts are estimates of the timing and the magnitude of occurrence of future

events.

6.1.Why forecasting?

These are the reasons for forecasting

Dynamic and complex environments forecasting is not required if the

organisation has complete control over the market forces and knows exactly what

the sale of its products is going to be in the future. This situation seldom exists.

Short term fluctuations in production- In production, there will be a short term

fluctuation in demand. If information is known, organizations can avoid knee-jerk

reactions to the unfolding reality. The organizations can plan that minimize the

costs of adjusting production system for short term fluctuations.

Better materials management Organizations benefit from better materials

management and ensure better resources availability.

Workforce scheduling the workforce must be scaled up or down to meet the

demand fluctuations by re-assignment, overtime, layoffs or hiring. Moreover

better planning of overtime and idle time can also lead to cost minimization.

Strategic decisions this includes planning for product line decisions, new

products, augmenting capacity, building factories and expanding the current level

of activities.


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6.2.Forecasting time horizon

The three categories of forecasting on the basis of time horizon are: short term, medium

term and long term horizons.

Criterion Short-term Medium term Long termTypical decision 1-3 months 6-18 months 5-10 years

Nature of decision Operational Tactical Strategic

Nature of data Quantitative Quantitative or Qualitative Qualitative

Degree of uncertainty Low Moderate High

Key consideration Random

effects

Seasonal and cyclical

effects

Trends or cycles

6.3Elements of a good forecast: A properly prepared forecast should fulfill thefollowing requirements:

The forecast should be timely. Usually, a certain amount of time is needed to

respond to the information contained in a forecast.

For example, capacity cannot be expanded overnight, nor can inventory levels

be changed immediately. Hence, the forecasting horizon must cover the time

necessary to implement possible changes.

The forecast should be accurate, and the degree of accuracy should be stated.

This will enable users to plan for possible errors and will provide a basis for

comparing alternative forecasts.

The forecast should be reliable; it should work consistently.

The forecast should be expressed in meaningful units. Financial planners

need to know how many INR will be needed, production planners need to

know how many units of raw materials will be needed, and schedulers need to

know what machines and man-power will be required. The choice of units

depends on the user requirement.

The forecast should be in writing. Although this will not guarantee that all

concerned are using the same information, it will at least increase the

likelihood of it. In addition, a written forecast will help in evaluating the

forecast once actual results are in.

The forecasting technique should be simple to understand and use. Users

often lack confidence in forecasts based on sophisticated techniques; they do

not understand either the circumstances in which the techniques are


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appropriate or the limitations of the techniques. Misuse of techniques is an

obvious consequence. Not surprisingly, fairly simple forecasting techniques

enjoy widespread popularity because users are more comfortable working with

them.

The forecast should be cost-effective: The benefits should outweigh the costs.

6.4Types of forecasting methods: The forecasting methods can be classified as

Figure 1.6 Forecasting methods

7. Qualitative forecasting methods

7.1.Educated guess: uses his or her best judgement based on experience and intuition to

estimate a sales forecast.

Used for short-term forecast

Cost of forecast-inaccuracy is low

Owner of a small convenience store uses to decide the number of units to be

stocked for the next to meet the demand.

7.2.Executive committee consensus (Panel consensus): knowledgeable executives

from various departments within the organisation form a committee with the

responsibility of developing a sales forecast.

Tend to be a compromise forecasts, not reflecting the extremes.

Forecastingmethods

Qualitative

methods

Quantitative

methods

Time seriesmodels

Casual models

(associative)


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It has the advantage of bringing together the considerable knowledge and

talents of various managers. However, there is the risk that the view of one

person will prevail, and the possibility that diffusing responsibility for the

forecast over the entire group may result in less pressure to produce a good

forecast.

7.3.Delphi method: used to achieve a consensus within a committee. This method is

developed by RAND Corporation, 1950.

Executives anonymously answer a series of questions on successive rounds

Each response is fed back to all participants on each round and process is

repeated.

Four-Six rounds may be required before consensus.

Result in forecast that most participants have ultimately agreed to in spite of

their initial disagreement.

As a forecasting tool, the Delphi method is useful for technological

forecasting, that is, for assessing changes in technology and their impact on

an organization.

Often the goal is to predict when a certain event will occur. For instance, the

goal of a Delphi forecast might be to predict when video telephones might be

installed in at least 50 percent of residential homes.

For the most part, these are long-term, single-time forecasts, which usually

have very little hard information to go by or data that are costly to obtain, so

the problem does not lend itself to analytical techniques. Rather, judgments of

experts or others who possess sufficient knowledge to make predictions are

used.

7.4.Survey of sales forces future regional sales are obtained from individual members

of the sales force.

Companies have a good communication system and have sales people who

sell directly to customers.

There are, however, several drawbacks to using sales-force opinions. One is

that staff members may be unable to distinguish between what customers

would like to do and what they actually will do.

Another is that these people are sometimes overly influenced by recent

experiences. Thus, after several periods of low sales, their estimates may tend

to become pessimistic. After several periods of good sales, they may tend to

be too optimistic.


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In addition, if forecasts are used to establish sales quotas, there will be a

conflict of interest because it is to the salespersons advantage to provide low

sales estimates.

7.5.Survey of customers: directly from customers.

What quantity of firms products they intend to purchase in each future time

period.

The obvious advantage of consumer surveys is that they can tap information

that might not be available elsewhere.

On the other hand, a considerable amount of knowledge and skill is required

to construct a survey, administer it, and correctly interpret the results for valid

information. Surveys can be expensive and time-consuming. In addition, even

under the best conditions, surveys of the general public must contend with thepossibility of irrational behaviour patterns.

7.6.Historical analogy: this method ties the estimateof sales of a product to a similar

product.

Useful in forecasting sales of new product

7.7.Market research: In market surveys, mail questionnaires, telephone interviews or

field interviews form the basis for testing hypothesis about real markets.

For new products or existing products to be introduced into new market

segment

8. Quantitative forecasting models

Are mathematical models based on historical data

Assume that past data are relevant to the future.

Can be time series and causal relationships.

8.1.Time series analysis based on the idea that the history of occurrences over time

can be used to predict the future.

Time series methods are statistical techniques that make use of historical data

accumulated over a period of time.

Time series methods assume that what has occurred in the past will continue to

occur in the future.

As the name time series suggests, these methods relate the forecast to only one

factortime.


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These methods assume that identifiable historical patterns or trends for demand

over time will repeat themselves.

They include (i) linear regression (ii) simple moving average (iii) weighted

moving average (iv) exponential smoothing, (v) Box Jenkins model

Linear regression

Linear regression is a method of forecasting in which a mathematical relationship

is developed between demand and some other factor that causes demand

behaviour.

However, when demand displays an obvious trend over time, a least squares

regression line, or linear trend line, that relates demand to time, can be used to

forecast demand.

If the data are a time series, the independent variable is the time period.

A linear trend line relates a dependent variable, which for our purposes is demand

(or sales), to the independent variable, time, in the form of a linear equation:

bXaY

Where, YDependent variable

XIndependent variable

a- Intercept in y axis

b- Slope of the trend

22

2

)( xxn

xyxyxa

22)( xxn

yxxynb

8.1..1. Coefficient of correlation (r): The coefficient of correlation, r, explains

the relative importance of the relationship between X and Y.

The sign of r shows the direction of the relationship.

The absolute value of r shows the strength of the relationship.

The sign of r is always the same as the sign of b.

r can take on any value between1 and +1.


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Meanings of several values of r:

-1 a perfect negative relationship

(as x goes up, y goes down by one unit, and vice versa)

+1 a perfect positive relationship

(as x goes up, y goes up by one unit, and vice versa)

0 no relationship exists between x and y

+0.3 a weak positive relationship

-0.9 a strong negative relationship

Simple moving average: A moving average forecast uses a number of the most

recent actual data values in generating a forecast. The moving average forecast

can be computed using the following equation:

n

AAAF ntttt

...21 (1.1)

Where,Ft= Forecast for the coming period, t

n = Number of periods to be averaged

1tA = Actual occurrence in the past period

2tA , 3tA ,, ntA = Actual occurrences two periods ago, three periods ago, and

so on up to n periods ago.

The disadvantage of the moving average method is that it does not react to

variations that occur for a reason, such as cycles and seasonal effects.

Factors that cause changes are generally ignored. It is basically a mechanical

method, which reflects historical data in a consistent way.

The advantage of moving average method is that it is easy to use, quick, and

relatively inexpensive. In general, this method can provide a good forecast for

the short run, but it should not be used for a long term.

It is called moving because as new demand data becomes available, the

oldest data is not used

By increasing the Averaging Period (AP), the forecast is less responsive to

fluctuations in demand (low impulse response and high noise dampening)


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By decreasing the AP, the forecast is more responsive to fluctuations in

demand (high impulse response and low noise dampening)

Weighted moving average: A weighted average is similar to a moving average,

except that it assigns more weight to the most recent values in a time series.

For instance, the most recent value might be assigned a weight of .40, the next

most recent value a weight of .30, the next after that a weight of .20, and the next

after that a weight of .10.

Note that the weights must sum to 1.00, and that the heaviest weights are

assigned to the most recent values.

n

AWAWAWF ntnttt

...2211 (1.2)

Where W1, W2,, Wn are the weight to be given to the actual occurrence for

periods t-1, t-2, , t-nrespectively.

The advantage of a weighted average over a simple moving average is that the

weighted average is more reflective of the most recent occurrences.

Choosing weights: However, the choice of weights is somewhat arbitrary and

generally involves the use of trial and error to find a suitable weighting scheme.

Experience also helps in deciding the weights.

As a general rule, the most recent past is the most important indicator of

what to expect in the future.

If the most recent periods are weighted too heavily, the forecast might

overreact to a random fluctuation in demand.

If they are weighted too lightly, the forecast might underreact to actual

changes in demand behaviour.

Exponential smoothing: Exponential smoothing is a sophisticated weighted

averaging method that is still relatively easy to use and understand. Each new

forecast is based on the previous forecast plus a percentage of the difference

between that forecast and the actual value of the series at that point.

That is:

forecast)Previous-demand(ActualforecastPrevious=forecastNext

Where, (Actual demand -Previous forecast) represents the forecast error and is

a percentage of the error. More concisely,


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)( 111 tttt FAFF (1.3)

Where, tF exponentially smoothened forecast for period t

1tF exponentially smoothened forecast for period t-1

1tA actual demand during the period t-1

smoothening coefficient

Recursively substituting1tF in (2.3)

)()()1()1(

)()]()1)[(1(

)]()[1(

122

2

122

11

ttt

ttt

ttt

AAF

AAF

AFF

)())(1(...)()1()1( 1212

11

tttt

t AAAFF (1.4)

This method is called exponential smoothing because each increment in the past

is decreased by (1-)

Choosing the appropriate value of : Constant is between 0 and 1

If real demand is stable, we would like a small alpha to lessen the effects

of short term or random changes.

If real demand is rapidly increasing or decreasing we would like a large

alpha to try to keep up with the change.

Figure 1.7 Exponential forecast verses actual demand for units of a product

showing the forecast lag

As shown in Figure 1.7, the forecast lags during an increase or decrease, but

overshoots when a change in direction occurs.

0

50

100

150

200

250

0 5 10 15

Actual demand

alpha 0.5

alpha 0.3

alpha 0.1


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Note that the higher the value of alpha, the more closely the forecast

follows the actual.

Trend effects in exponential smoothing

As we move toward medium-range forecasts, trend becomes more important.

Incorporating a trend component into exponentially smoothed forecasts is

called double exponential smoothing.

The estimate for the average and the estimate for the trend are both smoothed.

An upward or downward trend in data collected over a sequence of time

periods causes the exponential forecast to always lag behind (be above or

below the actual occurrence)

Exponentially smoothed forecasts can be corrected somewhat by adding in a

trend adjustment.

In addition to smoothing constant , the trend equation also uses a smoothing

constant .

Trend smoothing constant reduces the impact of the error that occurs

between the actual and the forecast.

The equation to compute the forecast including trend is

)(

)(

11

111

tttt

tttt

ttt

FITFTT

FITAFITF

TFFIT

(1.5)

Where, tF is exponentially smoothed forecast for period t

tT is exponentially smoothed trend for period t

tFIT is forecast including trend for period t

1tFIT is forecast including trend for period t-1

1tA is actual demand for period t-1

exponential smoothing constant

trend smoothing constant


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8.2.Causal relationship tries to understand and the system underlying and surrounding

the item being forecast. Sales may be affected by advertising, quality and

competitors.

8.3.Common types of trends - Analysis of time-series data require the analyst to

identify the underlying behavior of the series.

This can often be accomplished by merely plotting the data and visually

examining the plot.

One or more patterns might appear: trends, seasonal variations, cycles, or

variations around an average.

In addition, there will be random and perhaps irregular variations. These

behaviors can be described as follows:

i. Trend refers to a long-term upward or downward movement in the data.

Population shifts, changing incomes, and cultural changes often account for suchmovements.

ii. Seasonality refers to a data pattern that repeats itself after a short period of time.

Fairly regular variations generally related to factors such as the calendar or time of

day. Restaurants, supermarkets, and theaters experience weekly and even daily

seasonal variations.

Figure 1.8 Trend, cyclical and seasonal data plots, with random and irregular

variations


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iii. Cycles are data pattern that covers several years before it repeats again. These

wavelike variations occur for more than one years duration. These are often

related to a variety of economic, political, and even agricultural conditions.

iv. Irregular variations are due to unusual circumstances such as severe weather

conditions, strikes, or a major change in a product or service. They do not reflecttypical behavior, and their inclusion in the series can distort the overall picture.

Whenever possible, these should be identified and removed from the data.

v. Random variations or noise is a pattern resulting from random variation or

unexplained causes. These are residual variations that remain after all other

behaviors have been accounted for.

9. Forecast errors

A forecast is never completely accurate; forecasts will always deviate from the actual

demand.

This difference between the forecast and the actual is the forecast error. These errors

are called residuals.

Errors can be classified as bias or random.

Bias error occurs when a consistent mistake is made. Bias error include the

failure to include the right variables, the use of the wrong relationships

among variables; employment of wrong trend line.

Random error can be defined as those that cannot be explained by the

forecast model.

Although forecast error is inevitable, the objective of forecasting is that it be as slight

as possible. A large degree of error may indicate that either the forecasting technique

is the wrong one or it needs to be adjusted by changing its parameters.

9.1.Standard error is a measure of how historical data points have been dispersed about

the trend line

2

2

n

xybyaySyx (1.6)

9.2.Mean Squared Error (MSE) is ( yxS )2

9.3.Mean Absolute Deviation (MAD) is one of the most popular and simplest to use

measures of forecast error. MAD is an average of the absolute difference between the

forecast and actual demand, as computed by the following formula:


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n

demandforecastdemandActual

MAD

n

i

ii

1 (1.7)

If the MAD is small, the actual data closely follow forecasts of the dependentvariable and the forecasting model is providing actual forecasts.

9.4.Mean Absolute Percentage Error (MAPE): The mean absolute percent error

(MAPE) measures the absolute error as a percentage of demand rather than per

period. As a result, it eliminates the problem of interpreting the measure of accuracy

relative to the magnitude of the demand and forecast values, as MAD does. The

mean absolute percent error is computed according to the following formula:

1001

n

demandActual

demandforecastdemandActual

MAPE

n

i i

ii

(1.8)

9.5.Tracking Signal (TS): A tracking signal indicates if the forecast is consistently

biased high or low. It is computed by dividing the Running Sum of Forecast Error

(RSFE or cumulative error) by MAD, according to the formula:

n

FD

FD

MAD

RSFETS

n

i

ii

n

i

ii

1

1

)(

)(

(1.9)

Acceptable limits of TS depend on the size of the demand being forecast (high-

volume or high revenue) item monitored frequently.

Amount of personnel time available (narrow limits require more time to

investigate)

The tracking signal is recomputed each period, with updated, running values ofcumulative error and MAD. The movement of the tracking signal is compared to

control limits; as long as the tracking signal is within these limits, the forecast is

in control.

9.6.Evaluation of Short-range forecasting models: are evaluated on the basis of three

characteristics:

i. Impulse response

ii. Noise-dampening ability

iii. Accuracy


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If forecasts have little period-to-period fluctuation, they are said to be noise

dampening.

Forecasts that respond quickly to changes in data are said to have a high impulse

response.

A forecast system that responds quickly to data changes necessarily picks up a

great deal of random fluctuation (noise).

Hence, there is a trade-off between high impulse response and high noise

dampening.

Accuracy is the typical criterion for judging the performance of a forecasting

approach

Accuracy is how well the forecasted values match the actual values

10.Manufacturing Planning and Control

Manufacturing Planning and Control (MPC) system is concerned with planning and

controlling all aspects of manufacturing, including managing materials, scheduling

machines and people, and coordinating suppliers and key customers.

Provides information to effectively manage the flow of material, effectively utilize

people and equipment, and coordinate supply chain.

10.1 MPC System:

The essential task of the MPC system is to manage efficiently the flow of material, to

manage the utilization of people and equipment, and to respond to customer

requirements by utilizing the capacity of the suppliers, that of the internal facilities, and

(in some cases) that of the customers to meet customer demand.

Important additional activities involve the acquisition of information from customers on

product needs and providing customers with information on delivery dates and product

status.

MPC supports to make intelligent decisions.

An important point to note is that the MPC system provides the information upon which

managers make effective decisions. The MPC system does not make decisions nor

manage the operationsmanagers perform those activities. The MPC system provides

the support for them to do so wisely.

10.2 Typical support activities of MPC

The support activities of the MPC system can be broken roughly into three time

horizons: long term, medium term, and short term.


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In the long term, the system is responsible for providing information to make

decisions on the appropriate amount of capacity (including equipment, buildings,

suppliers, and so forth) to meet the market demands of the future.

Moreover, long-term planning is necessary for the firm to provide the appropriate mix

of human resource capabilities, technology, and geographical locations to meet thefirms future needs.

In the case of supply chain planning, the long term has to include the same kind of

capacity planning for the key suppliers. For companies that outsource their

manufacturing to outside companies, the planning of supplier capacity can be more

critical than internal capacity planning.

In the intermediate term, the fundamental issue addressed by the MPC system is

matching supply and demand in terms of both volume and product mix.

This means planning for the right quantities of material to arrive at the right time and

place to support product production and distribution. It also means maintaining

appropriate levels of raw material, work in process, and finished goods inventories in

the correct locations to meet market needs.

Another aspect is providing customers with information on expected delivery times

and communicating to suppliers the correct quantities and delivery times for the

material they supply.

Planning of capacity may require determining employment levels, overtime

possibilities, subcontracting needs, and support requirements.

In the short term, detailed scheduling of resources is required to meet production

requirements. This involves time, people, material, equipment, and facilities.

As the day-to-day activities continue, the MPC system must track the use of resources

and execution results to report on material consumption, labour utilization, equipment

utilization, completion of customer orders, and other important measures of

manufacturing performance.

Moreover, as customers change their minds, things go wrong, and other changes

occur, the MPC system must provide the information to managers, customers, and

suppliers on what happened, provide problem-solving support, and report on the

resolution of the problems.

Throughout this process, communication with customers on production status and

changes in expectations must be maintained

10.3 MPC System Framework: MPC System framework is depicted in figure 2.3. The

figure is divided into three parts or phases.

The top third, or front end, is the set of activities and systems for overall direction

setting. This phase establishes the overall company direction for manufacturing

planning and control.

Demand management encompasses forecasting customer/end-product demand,

order entry, order promising, accommodating interplant and intercompanydemand, and spare parts requirements. In essence, demand management


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coordinates all activities of the business that place demands on manufacturing

capacity.

Sales and operations planning balance the sales/marketing plans with available

production resources. Increasingly, this activity is receiving more management

attention as the need for coordination is recognized in progressive firms.

The master production schedule (MPS) is the disaggregated version of the

sales and operations plan. The MPS must support the sales and operations

plan.

Figure 1.9 MPC Framework

Resource planning determines the capacity necessary to produce the required

products now and in the future. Resource planning provides the basis for

matching manufacturing plans and capacity.

The middle third, or engine, encompasses the set of MPC systems for detailed

material and capacity planning.

The master production schedule feeds directly into the detailed material planning

module. Firms with a limited product range can specify rates of production for

developing these plans.


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However, for firms producing a wide variety of products with many parts per product,

detailed material planning can involve calculating requirements for thousands of parts

and components, using a formal logic called material requirements planning (MRP).

MRP determines (explodes) the period-by-period (time phased) plans for all

component parts and raw materials required to produce all the products in theMPS.

The bottom third, or back end, depicts MPC execution systems. For example,

firms producing a large variety of products using thousands of parts often

group all equipment of a similar type into a single work centre. Their shop

floor system establishes priorities for all shop orders at each work centre so the

orders can be properly scheduled.

Other firms will group mixtures of equipment that produce a similar set of

parts into work centres called production cells.

The supplier systems provide detailed information to the company suppliers.

10.3.1 Functions of MPC/Production Planning and Control (PPC)

Figure 1.10 PPC Process

10.4. Dependent demand Vs. Independent demand

Demand for final products is called as Independent demand.

Demand for the items that are subassemblies or component parts which are to beused in production of finished goods is called as Dependent demand.

The Figure 2.5 diagrammatically explains the definition for dependent demandand independent demand.


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Figure 1.11 Dependent demand Vs. Independent demand

The difference between dependent demand and independent demand is tabulated in

Table 2.2.

Table 1.2 Difference between dependent demand and independent demand

Attribute Dependent demand Independent demand

Nature of demand No uncertainty

Parent-child relationship

cause dependency

Considerable uncertainty

Independent

Service level 100% a necessity 100% not a feasible

solution

Demand occurrence Often lumpy Often continuous

Estimation of demand By production planning By forecasting

How much to order Known with certainty Estimate based on pastconsumption

When to order Can be estimated Cannot be answered

directly

10.5. Material Requirements Planning (MRP) is used to determine the quantity and

timing for the acquisition of dependent items needed to satisfy master schedule

requirements.

Managerial objective is to provide the right part at the right time.

It is a production planning process that starts from the demand for finished

products and plans step by step the production of subassemblies and parts.

Computer-based information system (i.e. glorified database) for ordering and

scheduling of dependent demandinventories

Basic inputs for MRP are listed below:

o Product structure or Bill of materials


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o Master production schedule for the final assembly

o Economic order quantity

o Beginning inventory (Inventory status)

MRP is characterised by the use of time-phased requirement records.

10.5.1 Explosion process

The desired quantity of each component part is calculated by using the Master

Production Schedule (MPS) in a process known as Bill of Material Explosion.

Consider an example of a basic telephone instrument.

Figure 1.12 Bill of Material Explosion for Telephone instrument

A telephone instrument consists of a base unit and a hand set. The hand set has

a speaker and a microphone and is covered by a pair of cover plates firmly

held together using a pair of screws. There is a wire that connects the handset

to the base unit. This is the parent-child relationship as shown in Figure 2.6.

Let us assume that we have in stock an inventory of 30 telephones, 27

handsets and 16 cover plates. Suppose that we want to plan for the production

of 100 telephones during the next week. How much inventory do we need in

each of these components?

One method is merely subtracting the on-hand inventory from the requirement

at each level to arrive at this number. (Method I in Table 2.4a).

Another method is to proceed level by level in the computation. In this

method, after computing the requirement at one level, we use the parent child

relationship to compute the requirement at the next level (Method II in Table

2.4b)

Telephone

Base unit Hand set

Cover

plates (2)Speaker

Micro-

phoneScrew (2)

Wire


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Table 1.3a Method I

Basic Telephone

Required 100

On-hand inventory 30

Planned quantity 70Hand set

Required 100


Planned quantity 73

Cover plate

Required 200


Planned quantity 184

Table 1.3b Method II

Basic Telephone

Required 100


Planned quantity 70

Hand set

Required 70


Planned quantity 43

Cover plate

Required 86

On-hand inventory 16Planned quantity 70

10.5.2 Bill of Materials (BOM)

Shows exactly what goes into what instead of being just a part list.Two ways the bill of material is represented

10.5.2.1 Product structure diagram or product tree

Figure 1.13 Product structure for product A

A

B(2) C(3)

D(1) E(4) F(2) G(5) H(4)

Level 0

Level 1

Level 2


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- Modular BOM describe the product structure for basic sub-assemblies of

parts that are common to different end items.

Modularization is used for end items that contain

many options.

- Intended BOM: is a form of multi-level BOM

It exhibits the final product as level 0 and all its components as level 1.

The level numbers increase as you proceed down the tree structure.

If an item is used in more than one parent within a given product

structure, it appears more than once, under every sub-assembly in

which it is used.

Problem 1: Product A is assembled from one B assembly, three C assemblies, two D

assemblies, and four H components. Each B assembly is made from two F subassemblies.

Each C is made from one G component and one H component. Each D assembly is made

from two I assemblies and three G components. Each F subassembly is made from three H

components. Each I subassembly is made from one H component.

a. Construct a product structure tree for product A.

b. Prepare and indented bill of materials for Product A.

10.5.3 Basic MRP Record: There is a universal representation of the status and

plans for any part number, which is the MRP time-phased record.

A single record provides the correct information on each part in the system.

All the single records associated with the parts needed for a complex productis linked together for proper managing.

Indented parts list

A

B(2)

D(1)

E(4)

C(3)F(2)

G(5)

H(4)

Single level parts list

A

B(2)

C(3)

B

D(1)E(4)

C

F(2)

G(5)

H(4)


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Key elements for linking the records are the bill of material, explosion processand lead time.

Table 1.4 Basic MRP Record

Period1 2 3 4 5

Gross requirements 10 40 10

Scheduled receipts 50 50

Projected available balance 4 54 44 44 4 44

Planned order release 50

Lead Time (LT) = 1 period

Lot size = 50 units

Figure 1.15 Product structure for a ladder-back chair

10.5.3.1 Terms in MRP record: There are certain terms used in a basic MRP record as

shown in Table2.5. The terms are defined as follows:

Gross requirements- are the total demand derived from all parent-production

plans. They also include demand not otherwise accounted for, such as demand


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Projected on-hand inventory (or projected available balance) is an estimate

of the amount of inventory available at end of each week after gross requirements

have been satisfied.

- The beginning inventory shown as 50 indicates the on-hand inventory available at

the time the record was computed.(Projected on-hand inventory balance at end of week t) =

(Inventory on-hand at end of week t-1) +

(Scheduled or planned receipt in week t)

(Gross requirement in week t).

Planned receipts planning for receipt of new orders will keep the projected on-

hand balance from dropping below zero.

Planned order release indicates when an order for a specified quantity of an

item is to be issued. We must place the planned order release quantity in the

proper time-bucket (at the beginning of each period)

Planned order release date = planned receipt date lead time

- Whenever projected available balance shows a quantity insufficient to satisfy

gross requirements, additional material must be planned for.

Period can vary in length from a day to a quarter or even longer. The periods

are also called a time bucket.

- Widely used time bucket is one week.

- The number of period in the record is the planning horizon

- This is done by creating a planned order release in time to keep the projected

available balance from becoming negative.

10.6. Safety Stock and safety lead time

Safety stock is a buffer of stock above and beyond that needed to satisfy the gross

requirements.

Theoretically, MRP systems should not require safety stock

Variability may necessitate the strategic use of safety stock

A bottleneck process or one with varying scrap rates may cause shortages

in downstream operations

Shortages may occur if orders are late or fabrication or assembly times are

longer than expected When lead times are variable, the concept of safety time is often used

Safety lead time is a procedure whereby shop orders are released and scheduled to

arrive one or more periods before necessary to satisfy the gross requirements.

When safety stock is used, the projected available balance does not fall below the

safety stock level instead of reaching zero.


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Table 1.6a MRP Record with safety stock

Period

1 2 3 4 5

Gross requirements 10 40 10Scheduled receipts 50 50




Lot size = 50 units

Safety stock = 10

Table 1.6b MRP Record with safety lead time

Period1 2 3 4 5

Gross requirements 10 40 10

Scheduled receipts 50 50




Lot size = 50 units

Safety lead time = 2 period

10.7. Lot sizing rules A lot sizing rule determines the size of order quantities for each component in a

product.

A lot sizing rule must be assigned to each item before planned receipts and

planned order releases can be computed.

While deciding on an appropriate lot size, the planner must keep in mind, the

influence of two sets of costsset up cost and inventory holding cost.

It is important because they determine the number of set-ups required and the

inventory holding costs for each item.

Large lot sizes will require fewer set-ups, but may result in carrying a hugeinventory for a longer time.

Smaller lot sizes require several set-ups, thereby increasing the set-up costs.

The objective of the planner is to minimize the total cost.

Different methods which are available to determine effective lot size in MRP are

as follows:

1. Lot-for-Lot method (L4L)

2. Economic Order Quantity (EOQ) method

3. Fixed Order Quantity method (FOQ)

4. Period Order Quantity (POQ) method5. Periods of supply


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6. Least Unit Cost (LUC) method

7. Part Period Balancing (PPB) method

8. Minimum Cost per Period (MCP) method

10.7.1

Lot-to-lot batch sizing: is the simplest for lot sizing technique. The rule is to supplyin each period what is demanded in that period. Lot for lot batch sizing results in

minimal inventory holding costs.

However, lot-for-lot means setting up to manufacture (or purchase) once for each

period in which the item is required, no matter how many are required.

When setup (or order) costs are low and inventory carrying costs are high, lot-for-

lot frequently results in the lowest inventory costs.

Because lot-for-lot is simple, it can be calculated quickly. This is important if the

firm has many lot sizing decisions to make each time it runs or regenerates its MRP

schedules.

Table 1.7a MRP Record given

Period 0 1 2 3 4 5 6 7 8 9 10 11 12

Gross

requirement

- 30 50 40 - 15 20 20 - 10 - 15 50

Scheduled

receipt

Projectedavailable

balance

30

Planned

release

LT=1 week

Lot-of-lot

Table 1.7b MRP Record solution

Period 0 1 2 3 4 5 6 7 8 9 10 11 12

Gross

requirement

- 30 50 40 - 15 20 20 - 10 - 15 50

Scheduled

receipt

50 40 0 15 20 20 0 10 0 15 50

Projected

available

balance

30 0 0 0 0 0 0 0 0 0 0 0 0

Planned

release

50 40 0 15 20 20 0 10 0 15 50

LT=1 week

Lot-of-lot


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10.7.2Economic Order Quantity method In this method, the economic order

quantity based on the data given are computed.

Problem 2: Consider an item that has projected requirement as shown below. The

beginning inventory is 30 units. The carrying cost per unit per week is Rs 2.50. The costper set-up is Rs. 250 and lead time is 1 week.

Initial inventory on hand = 30 units

Average demand per week (d) = 20.83 units/week

Carrying cost per unit per week (Cc) = Rs. 2.50

Set up cost per set up (Co) = Rs.250

unitsC

dC

c

o 655464502

832025022

.

.

.EOQ

EOQ is the least-cost position if all the assumptions of constancy of cost and certainty

of demand and delivery are satisfied. Inventory is much higher than it was when the firm used the lot-for-lot technique.


Period 0 1 2 3 4 5 6 7 8 9 10 11 12

Gross

requirement

- 30 50 40 - 15 20 20 - 10 - 15 50

Scheduled receipt

Projected

available balance

30

Planned release

LT=1 week

Lot size =

EOQ=65 units


Period 0 1 2 3 4 5 6 7 8 9 10 11 12

Gross

requirement

- 30 50 40 - 15 20 20 - 10 - 15 50

Scheduled receipt 65 65 65 65Projected

available balance

30 0 15 40 40 25 5 50 50 40 40 25 40

Planned release 65 65 65 65

LT=1 week

Lot size =

EOQ=65 units 65

units

Total number of orders in the planning horizon = 4 weeks


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Ordering cost = 4 x 250 = 1000

Carrying cost = 370 x 2.50 = 925

Total cost = 1925.00

10.7.3Period Order Quantity (POQ) method: In this method, the economic order size is

computed as in EOQ method. The optimal order period is computed using the

following formula:

Initial inventory on hand = 30 units

Average demand per week (d) = 20.83 units/week

Carrying cost per unit per period (Cc) = Rs. 2.50

Set up cost per set up (Co) = Rs.250

unitsC

DC

c

o 6554.6450.2

83.2025022EOQ

Optimal order period (P*) = weeksEOQD

N31143

651084

52 .

//

WhereNis the number of periods per year (52 weeks); D is the annual demand


Period 0 1 2 3 4 5 6 7 8 9 10 11 12

Gross

requirement

- 30 50 40 - 15 20 20 - 10 - 15 50

Scheduled

receipt

Projected

available

balance

30

Planned

release

LT=1 week

Lot size =65 units

In this method, whenever a planned order release is made, its order quantity is equal to

the sum of the non-zero projected requirements of optimal order periods in sequence

starting from the first period in that periods combination.

For example, the projected requirement for week 1 (=30 units) is met from the inventory.

The requirement for weeks 2, 3, 4 and 5 is added together (50+40+0+15=105) and

accordingly a planned order release for 105 units is made at the beginning of period 1.(Since the lead time is 1 week)


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Period 0 1 2 3 4 5 6 7 8 9 10 11 12

Grossrequirement

- 30 50 40 - 15 20 20 - 10 - 15 50

Scheduled

receipt

105 50 65

Projected

available

balance

30 0 55 15 15 0 30 10 10 0 0 50 0

Planned

release

105 50 65

LT=1 weekLot size =

65 units

Documents

Module 1 MPS