BSCMSE02m

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Session 2

Factors influencing demand

Basic demand patterns

Basic principles of forecasting

Principles of data collection

Basic forecasting techniques

Seasonality Sources and types of forecast error

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Capacity Management

Techniques

Capacity

Requirements

Planning (CRP)

Priority Management

Techniques

ResourcePlanning

(RP)

Sales andOperations

(S&OP)

Rough-Cut

Capacity

Planning (RCCP)

Master

Production

Schedule (MPS)

Material

Requirements

Planning (MRP)

Production

Activity Control

(PAC) Operation

Sequencing

Input/Output

Control

Planning Hierarchy

At each level, thereare three questions:

What are the

priorities? What capacity is

available?

How can differences

be resolved?

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Session 2

Demand plan

Planning horizon

1…18+ months

Unit

Quantity or $

Statistical

analysis

Sales input

Business & Strategy

Product & brand

Management

Input

Marketing input

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Sales & operations planning

Pyramid forecasting

Roll upForce down

X1 X2 Z1 Z.. Zn

Individual item X unit

price

Product family wise line

Wise

Family forecast X average

price

Total

Business

level

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Month -3 -2 -1

forecast 120 120 120

actual 109 137 133

difference -11 +17 +13

cumulative +6 +19

current 1 2 3 4

original 120 120 120 120 120

Revised 130 130 130 130 130

Marketing

19/360 5 % increase

In demand

MAKE to Stock

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Month -3 -2 -1

planned 125 125 125

actual 121 118 119

difference -4 -7 -6

cumulative -11 -17

current 1 2 3 4

original 125 120 120 120 120

Revised 135 135 135 135 135

manufacturing

Seems to be not

practical

17/375 dropped

By 5 %

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Month -3 -2 -1

planned 106 111 116

Actual 101

difference

cumulative

current 1 2 3 4

original 121 121 121 121 121

Revised

inventories

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Objective:

Better customer service (Customer Lead

time)

Lower Inventories ( Cost)

Stabilize production ( Commitments)

Team Work ( Management control on

business)

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1. Sales forecasts are reviewed & revised

2. Current inventories & backlog

3. Demonstrated capacities are documented

4. Production rates are revised within the constraints of

both materials & capacity availability.

5. Projected inventories and/or backlog positions are

calculated & compared to target financial projections

are developed and reviewed .

6. Contingency plan developed within the reasonable

framework.

7. Alternatives presented discussed reviewed & approval.

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Hiring cost 2000 per employeeLayoff cost 5000 Per employee

Inventory carrying cost 2 % on month ending inventory

Beginning & ending inventory 115,000 SKU

Current work force 1437

Plan 1 Chase

Plan 2 Level

Plan 3Mixed

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Manufacturing Strategies

Make-to-

Stock

Design

Inventory Manufacture Assemble Ship

Delivery Lead Time

Manufacture Inventory Assemble Ship

Manufacture Assemble Inventory Ship

Purchase Manufacture Assemble Ship Engineer-to-Order

Make-to-

Order

Assemble-

to-Order

Delivery Lead Time

Delivery Lead Time

Delivery Lead Time

Reprinted with permission, J.R. Tony Arnold, Introduction to Materials Management, third edition, Prentice-Hall, 1998

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Demand Management

Input Customer orders

Customer Schedules

Customer commitments

Customer Quotas Sales forecast

Promotions

Distribution center replenishments

Samples

Intra plant, division ,company requirements

Principle: The Master Schedule must have visibility into all known

demands from both internal & external customers

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Performance

Measurement

Financial

Review

Supply

Review Demand

Review

Product

Review

S & OPMeeting

Strategies

Resources

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The MPS

SIX key questions1. Has demand really changed?

2. What is the impact on “production plan”?

3. Is capacity available?

4. Is material available?

5. What are the costs and associated risks?

6. What is impact in market place?

Master

schedule

Past

Due

1 2 3 4 5 6 7 8

Firm Zone Trading zonePlanning

zone

FrozenSlushy Liquid

Only firm

Orders considered

Total demand

considered

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The MPS

Master

schedule

Past

Due

1 2 3 4 5 6 7 8

Flexibility

Cost

+

-

FirmTrading Planing

Time

Demand time fence Planning

Time fence

Product total cycle timeTotal

Manufacturing

Lead time

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The MPS

Master

schedule

Past

Due

1 2 3 4 5 6 7 8

Firm zone Trading Zone Planning Zone

Approved By Approved By Approved By

President

Vice Presidents

Manufacturing Dir.

Plant In charge

Master Scheduler

Materials Manager

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Factors Influencing Demand

Planning

Demand

1

Communicating

demand

2

Priortizing

demand

4

Influencing

demand

3

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Sources of Demand

All sources of demand must beidentified:

Customers

Spare parts

Promotions

Intracompany

Other

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Characteristics of Demand

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Demand Patterns

Stable versus dynamic

Stable demand retains same generalshape over time

Dynamic demand tends to be erratic

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Demand Patterns

Dependent versus independent

Only independent demand needs to be

forecast

Dependent demand should never be

forecast

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What Should Be Forecast?

Business plan Market direction 2 to 10 years

Sales and operations Product lines andfamilies

1 to 3 years

Master production

schedule

End item and

option

Months

Forecast Time Frame

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Principles of Forecasting

Forecasts Are rarely 100% accurate over time

Should include an estimate of error

Are more accurate for product lines and

families

Are more accurate for nearer periods of

time

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Data Preparation and Collection

Record data in terms needed for theforecast

Record circumstances relating to the data

Record demand separately for differentcustomer groups

Q

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Qualitative Techniques

Are based on intuition and informedopinion

Tend to be subjective

Are used for business planning andforecasting for new products

Are used for medium-term to long-term

forecasting

Q i i T h i

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Quantitative Techniques

Based on historical data usually availablein the company

Assume future will repeat past

E t i i T h i

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Extrinsic Techniques

Based on external indicators Useful in forecasting total company

demand or demand for families of products

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Intrinsic Quantitative Techniques

Month Sales January 92

February 83

March 66

April 74

May 75 June 84

July 84

August 81

September 75

October 63 November 91

December 84

January ?

M i A

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3

90)+84+(91

3

84)+91+(63 =forecastJanuary

Moving Averages

Forecast sales as an average of past months

An average of the past 3 months:

If January sales are 90, forecast for February

M i A F ti

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Moving Average Forecasting

It can be used to filter out random variation. Longer periods smooth out random

variation.

If a trend exists, it is hard to detect.

Manual calculations can be cumbersome

when dealing with more periods.

P bl 2 1

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Problem 2.1

P bl 2 1

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Problem 2.1

Month Demand

Three-Month Total

Next Month Forecast

1 102

2 91 3 95

4 105

5 94

6 101

P bl 2 1 S l ti

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Problem 2.1 Solution

2-18a

E ti l S thi

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Exponential Smoothing

Provides a routine method of updating item forecasts

Works well for stable items Is satisfactory for short-range forecasts

Detects trends, but lags them

2-19

SeasonalityMeasures the amount of seasonal variation of demand for a

product

Relates the average demand in a particular period to the

average demand for all periods

periodsallfor sales Average

salesaveragePeriod =indexSeasonal

D l i S l S l I d

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Quarter Average Quarterly Sales/100 Seasonal Index

1 128/100 = 1.282 102/100 = 1.02

3 75/100 = 0.75

4 95/100 = 0.95

Total = 4.00

Sales History

Year Quarter Total

1 2 3 4

1 122 108 81 90 401

2 130 100 73 96 399

3 132 98 71 99 400

Average128 102 75 95 400

Developing Seasonal Sales Indexes

units1004

400quartersallfor sales Average ==

2-21

S l S l

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Seasonal Sales

Average Salesfor All Periods

2-22

Tracking the Forecast

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Tracking the Forecast

Forecasts are rarely 100% correct over time.Why track the forecast?

To plan around the error in the future

To measure actual demand versusforecasts

To improve our forecasting methods

2-23

Forecasts Can Be Inacc rate in T o Wa s

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Bias: Cumulative sales may not be the same as forecast.

Bias exists since the cumulative variation is not zero.

Month Forecast Actual Variation

1 100 90 –10

2 100 125 +253 100 120 +20

4 100 125 +25

5 100 120 +20

6 100 110 +10

Total 600 690 +90

Forecasts Can Be Inaccurate in Two Ways

2-24

F t C B I t i T W ( t )

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Month Forecast Actual Variation

1 1,000 1,050 +50

2 1,000 940 –60

3 1,000 980 –20

4 1,000 1,040 +40

5 1,000 1,030 +30

6 1,000 960 –40

Total 6,000 6,000 0

Random variation: Sales will vary plus and minus about the average.

There is no bias, but there is random variation each month.

Forecasts Can Be Innaccurate in Two Ways (cont.)

2-25

Session 2: Objectives

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Session 2: Objectives

Understand the factors influencing demand Recognize basic demand patterns

Describe the basic principles of forecasting

Understand the principles of data collection

Compare basic forecasting techniques

Understand the concept of seasonality

Understand the sources and types of

forecast error

2-26

Problem 2 3 (Sol tion)

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Problem 2.3 (Solution)

Month Demand Next Month Forecast

1 102

2 91

3 95 96

4 105 97

5 94 98

6 101 100

7 108 101

8 91 100

9 101 100

10 99 97

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BSCMSE02m