Basics of Supply Chain Managment (Lesson 2)

8/6/2019 Basics of Supply Chain Managment (Lesson 2)

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Basics of Supply Chain Management

Unit1

Unit 1Supply Chain

Management Basics

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Copyright Leading Edge Training Institute Limited i i


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Preface............................................................................................................3

Course Description................................................................................................................. 3

Lesson 2 Forecasting Introduction ...............................................................4

Introduction and Objectives.................................................................................................. 4Factors that Influence Demand............................................................................................. 4Patterns of Demand................................................................................................................ 5What to Forecast .................................................................................................................... 7

Forecasting Principles............................................................................................................ 7Data Collection ....................................................................................................................... 8

Forecasting Techniques ......................................................................................................... 9Moving Averages.................................................................................................................. 11Exponential Smoothing........................................................................................................ 11

Seasonality............................................................................................................................. 12Forecast Accuracy................................................................................................................ 14

Gathering Forecast Information......................................................................................... 17Summary............................................................................................................................... 18Further Reading ................................................................................................................... 18

Review ................................................................................................................................... 19Whats Next? ........................................................................................................................ 21

Appendix.......................................................................................................22

Answers to Review Questions.............................................................................................. 23

Glossary........................................................................................................25


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Preface

Course Description

This document contains the second lesson in the Basics of Supply Chain Management unit,which is one of five units designed to prepare students to take the APICS CPIM examination.

The Basics of Supply Chain Management unit provides the foundation upon which the other fourunits build. It is necessary to complete this unit, or gain equivalent knowledge, beforeprogressing to the other units. The five units, which together cover the CPIM syllabus, are:


Master Planning of Resources

Detailed Scheduling and Planning

Execution and Control of Operations

Strategic Management of Resources

Please refer to the preface of Lesson 1 for further details about the support available to youduring this course of study.

This publication has been prepared by E-SCP under the guidance of Yvonne Delaney MBA,

CFPIM, CPIM. It has not been reviewed nor endorsed by APICS nor the APICS Curricula and

Certification Council for use as study material for the APICS CPIM certification examination.


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Lesson 2 Forecasting Introduction

Introduction and ObjectivesBefore planning production, it is necessary to estimate what conditions will exist in the nearfuture. Most firms cannot wait until orders are received before they start planning production:

they must anticipate future demand. This lesson looks at the factors influencing demand and theprinciples and techniques of forecasting demand.

On completion of this lesson you will be able to:

Identify factors that influence demand

Recognize basic demand patterns

Describe basic forecasting principles

Explain the principles of data collection

Compare and contrast basic forecasting techniques

Define seasonality and the seasonal index

Identify possible sources of and types of forecast error

Factors that Influence Demand

Many factors influence demand. Often, it is not possible to identify all of them, or the effects

they have. Some of the major demand influences include

Business and economic conditionsCompetition

Market trends

Company plans for products, pricing and promotion.

Other factors that affect demand in some situations include government or health regulations,

climate conditions, seasonality, and population demographics. For example, a reasonablywealthy country that is experiencing a baby boom may have increased demand for nursery-

related and pre-school education products. In this case, the birth rate is a factor influencingdemand.

George Santayana

Example

ABC Beverages has recorded the demand history for its premium freshly squeezed orange juicein the first quarter of 2003 (see Figure 1 below), which shows an abnormal spike in demand for

February.

Normal demand for the product remains steady at around 50000 litres per month. However,actual demand spikes in February. This is mainly due to the success of a 6 week promotional

Those who ignore the past are condemned torepeat it.


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period starting in February during which the company ran a Buy 2 get 3rd free campaign. Thisis responsible for an increase of 30,000 litres in February and 15,000 in March.

Orange Juice Demand Data

-20000

0

20000

40000

60000

80000

100000

Jan Feb Mar

F03

Litres

Special Promotion

Seasonal Variation

Trend Factor

Normal Demand

Figure 1 Freshly Squeezed Orange Juice Demand Data

The chart above also shows the affects of seasonal variation on demand for orange juice whichhas a negative effect in January and February, as demand usually drops in those two months. In

March, seasonal demand usually increases.

Sources of Demand

Its important to identify and monitor all sources of demand. These vary from industry to

industry. It is easy to overlook lesser sources of demand when concentrating on the maincustomer. Other sources of demand include:

Spare parts, for example, exhaust pipes in the car industry

Promotions : for example, buy one get one free promotion for baby wipes

Intracompany demand: for example, a beverage concentrate manufacturing facility inEngland is unable to meet demand for several months. A plant in the same group, basedin Mexico is able to produce what is required and ship over the product.

Patterns of Demand

The best way to identify patterns of demand is to plot demand in a graph against a time scale. Itwill then be easy to visually identify demand shapes or consistent patterns of demand. Althoughactual demand varies, there are several underlying demand factors that often have a measurableeffect on demand, depending on the type of product. These are:

Trends

Seasonality

Random Variation

Cycle

The chart below shows a historical demand pattern. It shows quite large variations in demand.There are also clear patterns of demand.


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Dependent and Independent Demand

Dependent demand occurs when the demand for the product is derived from the demand for

another product. For example, the sale of ice-cream cones and wafers is dependent on the sale ofice-cream. The sale of mobile phone chargers is dependent on the sale of certain types of mobile

phone. It is not usually necessary to forecast demand for dependent items as this can becalculated from the forecast of the product they are dependent on.

Independent items are usually end items of finished goods. However, this category also includes

service parts and inter-company transfers where items are supplied to other plants in the samecompany. All independent demand items must be forecast.

1. All of the following have a measurable effect on demand except:

A. Trends

B. Seasonality

C. Random variationReview Q

D. Gut feel

What to Forecast

At each level of business planning the forecast requirements differ because the informationneeded to plan the business differs. For example, a detailed forecast of the amount of raw

material required daily for the next 3 months will be of little use when formulating a strategicplan of where the business needs to go in the next 5 years. The following table links each level of

business planning with the most appropriate time frame and forecast.

Forecast Time Frame

Strategic Business Plan Market direction Between 2 and 10 years

Production Plan Product groups Between 1 and 3 years

Master Production Schedule End items and options Months

Forecasting Principles

There are four basic principles of forecasting which help to ensure more effective use offorecasts. These four principles are explained in the following paragraphs.

Forecasts are usually wrong.Errors are inevitable and are to be expected. Even a forecast that is correct on average may beinaccurate over each period.


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Data Integrity

There are numerous ways in which error can be introduced into company systems as a result of

delayed or inaccurate data entry. More recent developments in data storage and transmission,

such as bar coding and electronic data interchange (EDI) have helped improve data integrity.Bill of Material Error: A substitution may occur on any given BOM. If the change is not

updated, the recorded amount of both the original component and the substituted component heldin inventory will be incorrect.

Work Order Error: When a Work Order (WO) is released, the Bill of Material (BOM) for thatwork order is locked at the time of WO release. Subsequent changes to the BOM must also beupdated in the WO to maintain accurate records.

Time Delays: Delays in updating data may affect the ability to cycle count correctly. Inconsequence, incorrect stock record adjustments may be performed. For example, a delay in

scrapping material, the system may suggest material is available that has already been consumed

in manufacturing.

Data Entry Error: These occur particularly with manual data entry. For example, entering

receipt of 1010 units instead of 1100 will introduce errors into the system that will impactinventory accuracy and planning.

Data Collection Principles

There are three important guidelines to consider when collecting data for forecasts:

Record the data in the same format required by the forecast. If the purpose is to forecast

demand on production, data based on demand, not shipments will be required. Shipments show

how production responded to incoming orders but this is not a true indicator of demand asproduction may have under or over produced. The forecast period should be the same as theschedule period and the items in the forecast should be the same as those controlled bymanufacturing.

Record the circumstances related to the data. Record details of external events such as salespromotions, weather conditions or public holidays if they have a noticeable effect on the

demand.

Record the demand separately for different customer groups. Each customer group will haveits own characteristics. For example, a busy city retailer may make several orders for a product

in one week while a smaller outlet may only require one order a fortnight.

Forecasting Techniques

There are many different ways to forecast. However, they fall into one of two categories:

Qualitative forecasting

Quantitative forecasting

Qualitative Forecasting

Qualitative forecasting relies on the experience and judgement of the people involved in the

forecasting process. Future estimates are based on subjective assessments, intuition, and

informed opinion, as, for example, in the Delphi method, which relies on the opinion of a panelof experts. These techniques are used to forecast business trends and potential demand for new


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products. They may be used extensively in medium and long range forecasting but are lessappropriate for detailed production and inventory forecasting.

Qualitative forecasting is useful where there is no reliable historical trend to work from, such as

in very dynamic and changeable markets or when introducing a new product.

Quantitative Forecasting

In contrast, quantitative forecasting is based on mathematical formulae using historical data.Quantitative techniques are strongly influenced by the historical demand trends and are therefore

most useful where extensive demand history is available and the demand is relatively stable.Both intrinsic and extrinsic factors may be assessed when using quantitative forecasting. Thesefactors are described below.

Extrinsic Techniques

Extrinsic techniques, sometimes called causal techniques, are concerned

with external influencers of demand. Examples of such influencers wouldinclude the weather, the disposable income of the target market, andchanges in the demographic profile of the target market. For example,

demand for a magazine aimed at professional women in their earlytwenties will be more likely to increase in the near future if the number of

women graduating is increasing and if employment is also on theincrease.

Intrinsic Techniques

Intrinsic techniques are based on internal factors that are mostlyrecorded and are usually readily available in the demand history.

Forecasting that is reliant on intrinsic factors assumes that whathappened in the past will happen in the future. There are manymethods of extrapolating past data into the near future. These are

all useful for forecasting, particularly in an environment wherethere is little random fluctuation in demand.

Quantitative Forecasting Techniques

At its simplest, quantitative forecasting involves one or two assumptions or rules, for example:

Demand this month will be the same as last month. This is only useful in a few cases where

there is little ongoing change in demand.

Demand this month will be the same as the same month last year. This is useful if demand is

relatively stable year to year but exhibits seasonal variation.

The difficulty with forecasting based on either of these assumptions is the strong influence ofrandom demand. For example, during the aftermath of 9/11 a great deal of uncertainty and fear

led to a drop in air travel. Demand figures for November of that year would not have been anaccurate predictor of airline ticket sales in the following year. Methods that average out history

to discover underlying trends help to reduce the effects of random variation. Some methods thatdo this include moving averages, exponential smoothing, and seasonality.


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Moving Averages

It is often effective simply to forecast based on average demand in the preceding period. For

example, a soft drinks company may forecast demand for April equal to the average demand for

January February and March. Moving averages emphasise the underlying trend and smooth outthe noise of random demand fluctuation.

The graphic to the right shows an example of movingaverages. The average demand for January, February

and March was 25. This is entered as the estimateddemand for April.

The actual demand for April turns out to be 29, higherthan the projected demand. The forecast for May is setas the average of the demand for February, March, and

April. Each months forecast is based on the average of

the three preceding months.The mathematical formula for moving averages is quite simple:

(Sum of the demand figures)

Moving Average = -----------------------------------(The number of demand figures)

For example:

(22 + 25 + 27)

Moving Average for April = ------------------ = 253

1. Demand figures for January to June has been given below. Enter a forecast

for July based on a moving average of the previous three months.

Jan Feb Mar Apr May Jun JulReview Q

34 41 46 44 49 51

Exponential Smoothing

Exponential smoothing makes the calculation of a moving average simpler and reduces the

amount of data needed. It can be used as a routine method of updating item forecasts and workswell for stable items, particularly those with no trend or seasonality. It is an acceptable methodfor short range forecasting and can detect trends but will lag them. The technique involves using

an average figure and the previous months actual demand and applying a weight factor, orsmoothing constant to each figure before calculating the forecast demand.

The formula for exponential smoothing is:

New forecast = old forecast + weighting factor(actual demand old demand)

The weighting factor is often called alpha and is represented by the symbol ?

28282927

27292725

25272522

JunMayAprMarFebJan

28282927

27292725

25272522

JunMayAprMarFebJan


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The following table calculates the new forecast for a series of periods using exponentialsmoothing with a weighting factor of 0.2.

Period Old Forecast(OF)

Actual Demand (AD) Weighting Factor:0.2 (AD-OF)

New Forecast

1 4000 4400 80 4080

2 4080 3400 -136 3944

3 3944 2200 -348 3596

4 3596 5400 360 3956

5 3956 4200 48 4004

Table 1 Exponential Smoothing Example

2. Using the data from Table 1 above, calculate the new forecast for period 5,assuming the weighting factor has changed to 0.4 before the end of period 4.

Period Old Forecast Actual Demand New ForecastReview Q

5 3956 4200

Seasonality

Seasonal demand patterns are evident in many consumer products. In summer months, the sale

of sunglasses, suncream, cold drinks, and garden furniture tends to increase. During coldermonths, the demand for oil and electricity increases as the need for heat and light increases.Seasonality also refers to more frequently recurring demand patterns. Supermarket and restaurantsales are often highest at weekends and coming up to certain holidays. Canteens and cafes

experience peak demand for during the early morning and midday for breakfast and lunch.

Seasonal Index

Forecasts are made for the average demand. If seasonality exists as a factor in demand, it can becalculated using the seasonal index. This is necessary in order to cut out the effects of seasonalvariation so that you can compare sales in a high season with those in a low season.

Seasonal Demand

0

200

400

600

800

1000

1200

14001600

1800

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Demand

Average


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The extent of seasonal variation in demand is indicated by the seasonal index, an estimate of theamount by which demand during the season will fall outside average demand.

Throughout the year, demand for sunglasses might average around 1000 permonth. However, the average demand in the month of June may be much higher,

at 1650. Average demand for the month of October may fall to 475. Thefollowing formula calculates the seasonal index:

Period average demandSeasonal index = ----------------------------------------------

Average demand for all periods

Using this formula, the seasonal index for June and October are calculated as follows:

1650 475Index for June = ----------- = 1.65 Index for October = ------------ = 0.475

1000 1000

The period in question can be any length from daily to quarterly depending on the type ofseasonal demand. The average demand for all periods is taken by totalling the demand for each

period and dividing by the number of periods. The average demand for all periods is also calleddeseasonalized demand.

3. From the following demand data, calculate the seasonal index for eachperiod against the average demand over the 6 months .

Jan Feb Mar Apr May JunReview Q

600 720 850 1100 1360 1650

Month

Demand

Seasonal Index

When the seasonal pattern is relatively stable, the seasonal index can be applied to an averagedemand in order to calculate a seasonal forecast using the following formula:

Seasonal demand = (seasonal index) x (deseasonalized demand)

For example, given that the seasonal index for June is 1.65, if we have predicted total demandfor next year to be 13200, thats an average demand of 1100 for each period. We can thencalculate seasonal demand for June of next year as follows:

June demand = ( 1.65 ) x ( 1100) = 1815


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4. Using the seasonal indices you calculated in the last exercise, determinethe seasonal demand for next year, given that the deseasonalized demand is

1100.

Jan Feb Mar Apr May JunReview Q Month

Seasonal Demand

Forecast Accuracy

It is commonly accepted that the forecast will never be exactly right. Even if the overall averagedemand for a product group is accurately predicted over the year, the breakdown of demand for

each product in the group may be quite far out and the actual demand each month may varysignificantly from the average demand.

This poses a problem when actual demand exceeds forecast demand as it may affect customer

service. Most companies hold safety stock to ensure against stockouts when demand is higherthan forecast.

The forecast can be wrong in two ways: either through random error or forecast bias.

Random Error

When a forecast had random errors the actual demand will vary above and below the average

demand for the year but the total variation from the average will be close to zero. Random

variation such as this can be measured using mean absolute deviation (MAD) which is coveredin a later lesson. Once the random variation is known it is possible to:

Judge the reasonableness of the error.

Make plans to accommodate for expected error.

Set appropriate safety stock levels.

Forecast Bias

When a forecast has a persistent tendency to err in a particular direction it is said to be biased. In

the chart below, the forecast shows a positive bias; it is nearly always higher than the actualdemand. This can be due either to bias on the part of the forecaster or bias built into the business

process. It is more likely that the bias is due to the forecaster if the error is in one direction for allitems. However, if the error is in one direction for a specific set of items over a period of time itmay be due to the business process.


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400

600

800

1000

1200

1400

1600

Jan Feb Mar Apr May Jun

Actual Demand

Forecast Demand

Fixing Forecast Bias

Often, subjective bias on the part of the forecaster is introduced in order to safeguard against

certain issues. For example, the forecast may be increased to match performance objectiveswithin the forecasters functional area. It may be adjusted to create a higher safety stock inresponse to problems in production. Usually, the bias tends to increase inventories, which leads

to a high risk of inventory obsolescence and carries associated costs of storing, managing, andinsuring such inventory.

When subjective bias of this kind has been identified, the simplest remedy maysimply be to reduce all the forecast figures by a percentage. The exact

percentage may be determined by examining historical forecast accuracy.

In some cases, forecast bias may be built into the process for specific products.For example, if the business process has ignored increased growth trends in a

particular product group, the forecast will tend to be consistently low for thatproduct group.

Correcting process bias can be complex and time-consuming. Each item must be examined to

identify the cause of the bias and the process must then be adjusted to correct this bias.

Tracking Forecast Accuracy

An accurate forecast of demand is important to ensure efficientallocation of resources within an organization. Inaccuracies in thedemand forecast will cause problems at all levels of the organization

and may impact customer service. It is particularly important thatdetailed short-term forecasts used for tactical and operational planning

are accurate as errors here will increase inventory and potentially losesales and customers.

One way to measure forecast accuracy is to examine its converse concept: forecast error. To

calculate the forecast error, examine the forecast and actual demand figures for each SKU andcalculate the amount by which the forecast figure was in error.

In the table below, the forecast error for each SKU and the total forecast error were calculated bysubtracting the forecast figure from the forecast figure and recording the absolute value.


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FrescaJuice Forecast Accuracy

F03 Actual Forecast Ltrs Actual LtrsAbsolute

Error

Jul-03 Jul-03 Jul-03

Orange Juice 2,000 1,920 80

GrapefruitJuice 800 750 50

BreakfastJuice 550 700 150

Lemon Juice 200 150 50

CranberryJuice 600 640 40

Apple Juice 900 1,300 400

Total 5,050 5,460 410

Table 2 Absolute Error

When you divide the absolute error figure by the actual demand and multiply by 100, you see the

forecast error as a percentage of the total demand. Table 3 below displays the absolute error as apercentage of the actual demand for each SKU.

FrescaJuice Forecast Accuracy

F03 Actual

Forecast

Ltrs Actual Ltrs

Absolute

Error % ErrorJul-03 Jul-03 Jul-03 Jul-03

Orange Juice 2,000 1,920 80 4GrapefruitJuice 800 750 50 7

BreakfastJuice 550 700 150 21

Lemon Juice 200 150 50 33CranberryJuice 600 640 40 6

Apple Juice 900 1,300 400 31

Total 5,050 5,460 410 8

Table 3 Forecast error as a percentage of actual demand

% Forecast Error =Absolute(Actual - Forecast)

Actual Demand100 x


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Gathering Forecast Information

The forecast, as an estimate of future demand can be determined in many ways: using historical

data and mathematical formulae, using subjective opinion and informal sources, or any

combination of these approaches.

The forecast may use data from inside the company such as past sales or orders received in eachperiod. This information can be projected into the future taking into account growth factors oreconomic trends, to achieve a forecast estimate. Many companies gather external information to

assist in the forecasting process, such as market surveys and market research.

The three main areas of research are market intelligence, market changes, and market demand.

Such research involves consulting with the market to identify what it believes it wants. Methodsinclude street polls, supermarket stands to gauge reaction to a product and focus groups.

Market Intelligence

This approach involves comparing intelligence of the market, gathered wherever possible, withthe statistical forecast to identify if any changes must be made. This may be an individual or

cross-functional team responsibility. Knowing what people want to buy is essential to thebusiness of forecasting.

Market Changes

Market changes may be temporary, for example as the result of promotions by an organization orits competitors, or more permanent, for example, changes in government regulations that impacton product demand as in the UK where beef on the bone was banned as a result of BSE fears.

Market DemandMarket demand is the total volume that will be bought by a defined customer group, in a

specified location, during a particular period of time under specific environmental conditions andmarketing effort. A shift in market demand can often be detected by market surveys andresearch. A typical example is the clothing industry where basic demand changes with each

season.


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Summary

Lesson 2 covered the factors influencing demand and the principles and techniques offorecasting demand.

You should be able to:

Identify factors that influence demand

Recognize basic demand patterns

Describe basic forecasting principles

Explain the principles of data collection

Compare and contrast basic forecasting techniques

Define seasonality and the seasonal index

Identify possible sources of and types of forecast error

Further Reading

Introduction to Materials Management, JR Tony Arnold, CFPIM,CIRM and Stephen Chapman CFPIM

APICS Dictionary10th edition, 2002


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Review

The following questions are designed to test your recall of the material covered in

lesson 2. The answers are available in the appendix of this workbook.

6. The following are major influences on a firms demand for product and services except:

A. Master Production Schedule

B. General business and economic trends

C. The firms promotional activities

D. Market trends

7. All of the following are fundamentals of forecasting except:

A. Forecasts are generally inaccurate

B. Forecasts for sub-assemblies are more accurate

C. Forecasts are more accurate in the near term

D. Forecasts should include an estimate of error

8. When a company has to rely on external indicators when forecasting, the forecasting

technique for calculating the data is called:

A. Qualitative forecasting

B. Extrinsic forecasting

C. Intrinsic forecasting

D. Causal forecasting

9. Which forecasting technique uses the following formula:

New forecast = old forecast + ?(old forecast actual demand)?

A. Weighted moving average

B. Seasonal index

C. Exponential smoothing

D. Focus forecasting

10. In the month of June a product sells 300 units. The product in question has an annualdemand of 2400. What is the seasonal index for this product for June?

A. 1.0

B. 1.5

C. 1.75

D. 2.0


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11. Which is the best description of forecast bias?

A. A forecast has a persistent tendency to err in a particular direction

B. The standard deviation is consistently positive

C. The mean absolute deviation (MAD) = the forecast error

D. The sum of the errors is less than the MAD

12. Tracking forecast accuracy is useful for

A. Monitoring the quality of the forecast

B. Determining the variation in the production plan

C. Measuring whether the schedule is being met

D. Measuring the material plan


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Appendix


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Answers to Review Questions

Lesson 2 Review

1. D Gut Feel

A gut feeling is an internal hunch or judgment made about demand. It does not

have any effect on demand.

2. Moving Average for July

Jan Feb Mar Apr May Jun Jul

34 41 46 44 49 51 48

This was calculated by dividing the sum of demand for April, May and June by 3

3. New forecast for period 5, assuming the weighting factor has changed to 0.4

before the end of period 4.

Period Old Forecast Actual Demand New Forecast

5 3956 4200 4054

This was calculated by multiplying the difference between forecast and actual demand by theweighting factor of 0.4 and adding this to the old forecast figure.

4. Seasonal index for each period against the average demand over the 6

months.


600 720 850 1100 1360 1650

0.6 0.72 0.85 1.1 1.36 1.65

Month

Demand

Seasonal Index

The seasonal index for each month is calculated by dividing the average demand for the monthby the average demand over the entire season.

5. Seasonal demand for next year based on deseasonalized demand of 1100.


660 792 935 1210 1496 1815

Month

Seasonal Demand

This is calculated by multiplying the deseasonalized demand by the seasonal index for eachmonth.


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6. A

The Master Production Schedule (MPS) is driven by market demand (as set down in the forecast

and production plan). It does not influence market demand.7. B

Forecasts are most accurate at the aggregate level and tend to be less accurate for sub-assemblies. For this reason, it is important to forecast at the product group level rather than the

sub-assembly level.

8. B

Extrinsic forecasting relies on external factors. An extrinsic forecast is based on external factors

that will influence demand. For example, the number of new houses built will impact on thedemand for flooring. Extrinsic forecasts are useful for large aggregations such as total company

sales.9. C

Exponential smoothing uses a smoothing constant or weighting factor, often called alpha (? ).

The alpha factor smoothes variation between latest actual demand and forecast demand.

10. C

To calculate the seasonal index, you divide the period average demand by the average demandfor all periods in the season. In this example, the average demand for all periods in the season is200, so the seasonal index for June is 300 / 200 or 1.5.

11. A

Forecast bias is evident when actual demand varies consistently higher or lower than the

forecast. When bias occurs in the forecast the forecast is incorrect and must be adjusted.

12. A

A tracking signal is used to measure the quality of the forecast and determine whether to adjust

the forecast. There are many methods of tracking forecast accuracy, including forecast error as apercentage of demand.


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Glossary

Term Definition

bill of material

(BOM)

A listing of all the subassemblies, intermediates, parts, and raw materials

needed for a parent assembly, showing the required quantity of each. It isused with the MPS to determine items that must be ordered. Also calledformula or recipe.

Delphi method A qualitative forecasting technique where the opinions of experts arecombined in a series of iterations. The results of each iteration are used to

develop the next, so that convergence of the experts' opinion is achieved.

dependentdemand

Demand that is directly related to or derived from the bill of materialstructure for another item or end product. Dependent demand should be

calculated rather than forecast. Some items may have both dependent andindependent demand at the same time.

exponentialsmoothing

A weighted moving average forecasting technique in which past records aregeometrically discounted according to their age with the heaviest weightassigned to most recent data. A smoothing constant is applied to avoid using

excessive historical data.

extrinsic forecast A forecast based on a correlated leading indicator, for example, estimating

furniture sales based on house builds. Extrinsic forecasts are more useful forlarge aggregations like total company sales.

independent

demand

Demand for an item that is unrelated to the demand for other items.

Examples include finished goods and service part requirements.

intrinsic forecast A forecast based on internal factors, such as an average of past sales.

lead time Lead time is the span of time required to perform a process.

master

productionschedule (MPS)

The anticipated build schedule for those items assigned to the master

scheduler. The master scheduler maintains this schedule and it drivesmaterial requirements planning. It specifies configurations, quantities and

dates for production.

moving average An arithmetic average of a certain number of the most recent records. Aseach new record is added, the oldest record is dropped. The number of

periods used for the average reflects responsiveness versus stability.

random

variation

A fluctuation in data that is caused by random or uncertain events.


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seasonality A repetitive pattern of demand from year to year or month to month (or othertime period) showing much higher demand in some periods than in others.

trend General upward or downward movement of a variable over time, forexample in product demand.

work order an order to the machine shop for tool manufacture or equipment maintenance

or an authorization to start work on an activity or product.

Basics of Supply Chain Managment (Lesson 2)

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