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Basics of Supply Chain Management Session 2 Demand Management

Basics of Supply Chain Management 1.

2.

3.

4.

5.

Demand Management

Master Planning

Material Requirements Planning

Capacity Management and Production Activity Control

Aggregate Inventory Management

Item Inventory Management

Purchasing and Physical Distribution

Lean/JIT and Quality Systems

Theory of Constraints and Review Activity

6.

7.

Introduction to Supply Chain Management

8.

9.

10.

Learning Objectives Demand Management Processes • Describe the significance of marketing management and customer relationship management • Explain the role and objectives of demand planning (forecasting and customer order management)

Characteristics of Demand • Differentiate independent from dependent demand • Identify at least five sources of independent demand • Recognize at least four demand patterns

Learning Objectives (cont.) Basic Forecasting Concepts • Describe three planning levels that are supported by demand forecasts • Explain four major principles of forecasting and three principles of data collection and preparation • Differentiate quantitative from qualitative forecasting techniques

Estimate Demand • Calculate and explain the logic of an exponential smoothing forecast • Explain the logic behind the calculation of a seasonal forecast • Calculate and explain the use of the mean absolute deviation

Session 1 Demand Management Processes

Demand Management Processes Marketing Strategy & Product Management

Marketing Management

Demand Planning Forecasting & Other Demands (e.g. Internal)

Customer Relationship Management (CRM)

These topics are covered in the CSCP Program

Customer Interaction & Order Management

Marketing Mix: The Four Ps The four P’s are used to implement marketing strategy via product positioning, product differentiation, and market segmentation. Each attribute should contribute to the creation of Order Qualifiers & Order Winners consistent with strategy.

• Product

The design, features, cost, service, etc.., of the product need to be aligned with the market segment requirements and the pricing strategy.

• Price

Key decision is whether to compete with a commodity product or provide value that will bring premium pricing.

• Promotion Must decide what sales promotion and advertising approach is right for the product marketing strategy.

• Place Such decisions as sales channels used, distribution inventory policy, and network design are critical to providing the product where and when the customer wants it.

Order Qualifiers and Winners • Order qualifiers—Competitive characteristics that a firm’s products and services must exhibit in order for the firm to be a viable competitor in the marketplace • Order winners—Competitive characteristics that cause customers to prefer a firm’s products and services over those of its competitors

Customer Relationship Management Help customers achieve better business results through:

• Design assistance: helping in the design of new products or improvement of existing ones • Customer needs: assessing the customer’s business and creating (expanding) product offerings • Information and communications: collecting and analyzing customer data to support marketing, sales, and customer service

Order Management CRM plays a major role in operations efficiency and customer service through: Fast and accurate order entry and tracking Real-time, on-line order confirmation using Available-to-Promise functionality is best.

Meet promised delivery dates and quantities Measure and improve “Delivery Reliability”

Handle customer inquiries and service complaints, returns, and repair Firm should be easy to do business with.

Accurate and timely shipping documentation, invoicing, and recording of sales history “Perfect Order Fulfillment” is the goal.

Demand Planning Recognition of customer requirements through – Forecasts – Management of orders from • Internal customers • External customers

Sample Demand Plan - APO

External Customer Forecast Distribution Replenishment Internal Customer

Session 2 Characteristics of Demand

Independent vs. Dependent Demand • Only independent demand needs to be forecasted • Dependent demand should never be forecasted; it should be calculated

In this example, only the “arrows” would be forecasted. The components would be calculated using MRP.

Sources of Demand • Forecasts Estimate of future demand based on quantitative or qualitative methods or a combination of the two.

• Customer orders Orders from external customers, represents “actual” demand not estimated demand.

• Replenishment orders from DCs Based on both forecast placed at the DC level and customer orders placed at the DC.

• Interplant transfers Orders from other divisions or affiliates within the firm.

• Other Sample Orders, Orders for research & testing, replacement of damaged goods, etc..

Demand Patterns: Trend Trends can be “linear” or “exponential”

Increasing Decreasing

Demand

Level

Quarters

Demand Patterns: Seasonal Demand Third Quarter is always high

Demand

In this case, Seasonal & Trending Upward

First quarter is always low

Quarters

Cyclical Pattern The general economy goes through periods of expansion or growth followed by contraction or recession.

Growth or Expansion

Recession or Contraction

Cyclical Patterns occur across years where Seasonality occurs within a year.

Stable vs. Dynamic Demand • Stable demand retains same general shape over time and average demand may yield a usable forecast. • Dynamic demand tends to be erratic and more difficult to forecast. Dynamic

Stable vs. Dynamic Demand

Stable

Average demand

Session 2 Forecasting

Introduction • Purposes and uses of the forecast • Principles of forecasting • Principles of data collection and preparation

How Forecasting Supports Planning Planning Level

Forecast

Horizon (up to)

Business Planning

Sales volume ($); new market and supply chain initiatives

2 to 10 years

Physical units of production at the product family level

1 to 3 years

Physical units of production at the end item level

3 to 18 months

Sales and Operations Planning

Master Scheduling

The business should generate a “one number” forecast at the detailed level which can then be aggregated by product group or total business forecast.

Principles of Forecasting Forecasts Are rarely 100% accurate over time Should include an estimate of error Are more accurate for product groups and families Are more accurate for nearer periods of time

Data Collection and Preparation • Record data in terms needed for the forecast Record demand and in similar forecast periods as manufacturing.

• Record circumstances relating to the data Weather, price changes, competitors initiatives

• Record demand separately for different customer groups Business, government, A versus B and C customers

Data Collection and Preparation Example Month

1

2

A

3

4

5

6

7

8

6000

9

10

11

12

6000

B

500

500

500

500

500

500

500

500

500

500

500

500

Average Forecast (Produce)

1500

1500

1500

1500

1500

1500

1500

1500

1500

1500

1500

1500

PAB

1000

2000 -3000 -2000 -1500 -1000 -0-

Customer A’s annual demand: Customer B’s annual demand: Total: Average over 12 months:

1000 -4000 -3000 -2000 -1000

12,000 6,000 18,000 1,500 per month

Session 2 Forecasting Techniques

Forecasting Techniques Forecasting Techniques

Qualitative

Quantitative

Judgment

Mathematics

Includes inputs from Sales & Marketing

Intrinsic (Time Series) • Based on historical sales • Assumes the past demand pattern will continue

Extrinsic (Causal) • Using housing start forecasts to predict demand for construction chemicals

• Using weather forecasts to predict demand for agricultural chemicals

Qualitative Techniques • Are based on intuition and informed opinion Use such tools as surveys, expert opinions, marketing estimates of changes, etc…

• Tend to be subjective Contain more “bias” (tendency to over or under forecast) than quantitative methods.

• Are used for business planning and forecasting for new products Factor in qualitative information about the economy, competitors, trends, etc.. .

• Are used for medium-term to long-term forecasting Because quantitative forecasts are based on history and longer term changes must be input by marketing and sales.

Quantitative Techniques: Extrinsic • Based on correlation and causality For example, decrease prices to increase sales; higher unemployment leads to lower consumer spending.

• Rely on external indicators For example, Consumer Price Index, Housing Starts, Auto Build Rates, Unemployment Rates, Interest Rates, Stock Prices, etc….

• Useful in forecasting total company demand or demand for families of products Adjustments based on these type inputs is usually not applied at the article level.

• Two types of leading indicators – Economic e.g. Housing Starts, Defense Contracts, Consumer Spending, etc…

– Demographic e.g. Birth rates, ethnic mix, etc….

Quantitative Techniques: Intrinsic • Based on several assumptions – The past helps you understand the future Future buying will be similar to past buying.

– Time series are available Accurate demand data exist in the firm’s software system.

– The past pattern of demand predicts the future pattern of demand No major change expected in demand components (e.g. trend, seasonality, etc..)

• Examples – Moving Averages Best used with horizontal demand patterns with only random variation. No good with trends or seasonality.

– Exponential Smoothing Provides the ability to place more weight on recent data points which in times of change may be more representative of the demand pattern.

Moving Averages: Principles • Best used when demand is stable and there is little trend or seasonality, and demand variations are random We will discuss why in the examples that follow.

• When past demand shows random variation… – Do not second-guess what the effect of random variation will be – It is better to forecast based on average demand

Moving Average Forecast Example Assume it is the end of December; forecast demand for the next month, January Jan

Feb

92

Mar

83

Apr

66

May Jun

74

75

Jul

84

84

Aug Sep Oct

81

75

Nov Dec Jan

Mo.1

Mo.2

Mo.3

63

91

84

?

95 90 85 80

Avg.

75 70 65 60 1

2

3

4

5

6

7

8

9

10

11

12

What forecast would you choose for January and Why? 79

Moving Average Forecast Logic Moving average forecast = average demand of past periods Moving average forecast for month 4 288 Σ demand for months 1 - 3 = = = 96 units number of months 3 Month

Demand

1

102

2

91

3

95

Three-month total

Forecast

288 96

4

Key:  = sum

Month 4 forecast

Class Problem 2.1 Month

Demand

1

102

2

91

3

95

4

105

5

94

6

101

7

Three-month total

Forecast

Class Problem 2.1 Solution Three-month total

Month

Demand

1

102

2

91

3

95

288

4

105

291

96

5

94

294

97

6

101

300

98

7

Forecast

100

Class Problem 2.1 Solution (cont.) 106 Actual 104

Threemonth total

100

3

288

4

291

96

5

294

97

6 7

300

102

Forecast Demand

Month

Forecast

98 100

98 96 94 92

90 0

2

4

Period

6

8

Three-Month Moving-Average Forecast Month

Demand

1

89

2

89

3

94

272

4

91

274

91

5

95

280

91

6

104

290

93

7

106

305

97

8

110

320

102

9

Three-month total

Forecast

107

Six-Month Moving-Average Forecast Month

Demand

1

89

2

89

3

94

4

91

5

95

6

104

562

7

106

579

94

8

110

600

97

9

Six-month total

Forecast

100

Moving Averages: Lessons Learned • The moving average forecast will lag the development of a rising or falling trend • The farther back the moving average forecast reaches for data, the greater the lag • The three-month moving average forecast may have overreacted if the demand surge had abated • The moving average forecast works best when demand is stable with random variation; it will “filter out” random variation

110

105

100 Actual Sales 95

3-Mth MovAvg 6-Mth MovAvg

90

85

80 1

2

3

4

5

6

7

8

Exponential Smoothing Logic • Take the old forecast and the actual demand for the latest (most current) period • Assign a weighting factor or smoothing constant (α, alpha) to the latest period demand vs. the old forecast • Calculate the weighted average of the old forecast and the latest demand New forecast = (α) (latest demand) + (1 – α) ( old forecast) Note: Higher alpha values place more weight on recent demand data.

Smoothing Constant (α, Alpha) New forecast = (α) (latest demand) + (1 – α) (previous forecast)

• Low smoothing constant gives more weight to the old forecast: e.g., – α = .2 for latest demand (e.g. period X) – 1 – α = .8 for old forecast (also period X)

• Appropriate if demand is stable, not rising or falling • Run simulations with different α values to see which one best fits the historical demand pattern

Class Problem 2.2 New forecast = (α) (latest demand) + (1 – α) (previous forecast) A. Prepare an exponential smoothing forecast for June.

May data: actual demand = 220; forecast = 200. Calculate the forecast for June using a smoothing constant (α) of .20 B. Prepare an exponential smoothing forecast for July.

June data: actual demand = 240 Calculate the forecast for July also using a smoothing constant (α) of .20

Class Problem 2.2 Solution New forecast = (α) (latest demand) + (1 – α) (previous forecast) May Actual Demand = 220 Units

May’s Forecast = 200 Units

A. Prepare an exponential smoothing forecast for June.

= (.2) 220 + (.8) 200 = = 44 + 160 =

204

June Forecast

Actual June Demand = 240

B. Prepare an exponential smoothing forecast for July.

= (.2) 240 + (.8) 204 = = 48 + 163 =

211

Seasonal Demand

Demand (units)

Average demand for all periods

Seasonal demand

Time (quarters)

Seasonal Forecast Process

3

Develop a seasonal forecast for each period of the year being forecast

Develop a deseasonalized demand forecast spanning all periods

2

1

Calculate a seasonal index of demand for each period to establish seasonality

Seasonal Demand Indexes (Step 1) Demand History

Year 1 2 3 Average

Quarter

Total

1

2

3

4

122 130 132 128

108 100 98 102

81 73 71 75

90 96 99 95

Average demand for all quarters = 400 = 100 units 4

401 399 400 400 Average Period Demand/Average Demand for All Periods

Quarter

Average Quarterly Demand/100

Seasonal Index

1

128/100

=

1.28

2

102/100

=

1.02

3

75/100

=

0.75

4

95/100

=

0.95

Total

=

4.00

Deseasonalized Forecast (Step 2) • Make the forecast for the next year (The business expects to sell 420 in Year 4) • De-seasonalize the forecast — distribute it evenly across the four quarters De-seasonalized demand (average demand/period)

=

=

Annual forecast No. of periods

420 4

= 105 units

Seasonal Forecast (Step 3) Calculation

=

(seasonal index)  (deseasonalized forecast demand)

Expected first quarter demand

=

1.28 X 105 = 134 units

Expected second quarter demand

=

Expected quarter demand

1.02 X 105 = 107 units

Expected third quarter demand =

.75 X 105 =

Expected fourth quarter demand

=

.95 X 105 = 100 units

Total forecast demand

=

Alternate Method: (1.28/4.00) x 420 = 134

79 units

420 units

Session 2 Tracking the Forecast

Tracking the Forecast never

• Forecasts are rarely 100% correct over time. Random Variations alone ensures some error will occur.

• Why track the forecast? – To understand why demand differs from the forecast And take actions to eliminate error.

– To plan around error in the future Develop safety stock targets, make contingency plans in case of demand peaks, etc..

– To improve forecasting methods Identifying errors and investigating to find root causes will result in improved forecasting methods.

Bias vs. Random Variation Bias

Random Variation

Cumulative demand may not be the same as forecast

Demand will vary plus and minus about the average

Month

Forecast

Actual

Variation

Forecast

Actual

Variation

1

100

90

-10

100

105

+5

2

100

125

+25

100

94

-6

3

100

120

+20

100

98

-2

4

100

125

+25

100

104

+4

5

100

120

+20

100

103

+3

6

100

110

+10

100

96

-4

Cumulative Total

600

690

+90

600

600

0

Bias exists since cumulative variation is not zero.

There is no bias since cumulative variation is zero.

Forecast Error Data

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Total

Forecast

500

500

500

500

500

500

500

500

500

500

500

500

-

Actual

460

520

530

490

460

500

530

490

530

480

490

520

-

Absolute deviation

40

20

30

10

40

0

30

10

30

20

10

20

260

Mean Absolute Deviation (MAD) Use “Absolute” error as both over and under forecasting are problems.

Key:  = Sum; I I = Absolute Value MAD =

| - F|  | |A n

n

Σ Absolute errors MAD =

=

260

= 22 units

12

No. of periods



A-F A

MAPE = n

[%]

MAD Analysis: Normal Distribution

-3

-2

-1

-66

-44

-22

0

1

2

3

MAD

22

44

66

Units

If the data is normally distributed, 60% of the data points will fall within +or- 1 MAD or 22 Units. Ninety (90%) will fall within +or- 2 MADs.

Uses of Forecast Measurement • Identify changes and trends in demand So the forecasting method can be changed to match the new demand pattern.

• Identify and adjust for forecast error that results from random events For example, use averaging techniques to smooth out random variations in demand.

• Adjust the period forecast so that it is close to the true forecast average demand to minimize bias For example, remove data outliers that vary significantly from average demand.

• Making decisions on safety stock and service levels based on the degree of random variation (forecast error) For example, calculate statistical safety stocks using the standard deviation of error.

Supply Chain Management Implications Deal with demand uncertainty through process improvements

• Decrease reliance on long-term forecasts and increase ability to react quickly to demand Improved manufacturing flexibility and reduced lead times make it possible to react more quickly to changes in demand.

• Collaborate with customers and suppliers, especially in sharing demand information • Increase manufacturing flexibility internally and operations integration externally with customers and suppliers For example, sharing production schedules with suppliers instead of the supplier having to forecast demand.

Basics of Supply Chain Management Session 2