Supply Chain Management:

DEMAND PLANNING

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DEMAND Demand is a need for a particular product or

component. The demand could come from any number of sources. Core components of demand includes:

Trend Seasonality Random variation. Cycle.

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Trend: General upward or downward movement of a variable over

time.

Seasonality: A repetitive pattern of demand from year to year (or other

repeating time intervals.) Demand may fluctuate depending on time of year. Example; holidays weather, or other seasonal events.

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Random variation:A fluctuation in data that is caused by uncertain or

random occurrences.Many factors affect demand during specific time

periods and occur on a random basis.Cycle:

Over time, increases and decreases in the economy influence demand.

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Sources of demand variability Competition. Seasonality Life cycle trends External factors. Promotions. Disasters

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DEMAND PLANNINGThe process of planning all demand for

products and services to support the market place. The process involves updating the supporting plans and assumptions and reaching consensus on an updated demand plan.

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ImportanceIf production outstrips demand, you suffer

financial losses and perhaps go bankrupt.If order exceeds supply, your frustrated

customers may go to your competitors.

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ASPECTS OF DEMAND PLANNING

1. Supply chain dynamics.2. Forecasting3. Collaborative demand planning4. Role of marketing.

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1. SUPPLY CHAIN DYNAMICS

Sometimes customers go to stores to buy your products or enjoy services, other times they do not.

In the worse case scenario, demand fluctuations at the retail level tend to be magnified up the supply chain. This is called the “ripple effect” or “Bullwhip effect”.

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The Bullwhip Effect

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Causes of the Bullwhip Effect

1. Demand Forecast Errors2. Lead time3. Order batching (lumping)4. Price fluctuations and promotions.

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1. Demand forecast errors Forecast errors are “the difference between

actual demand and forecast demand.” It is mainly due to the incomplete

information. Adding up the safety stock at all levels of the

supply chain result the forecast errors.

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2. Lead Time A span of time required to perform a process (or

series of operations). The time between recognition of the need for an

order and the receiving of goods. The amount of lead time influences the magnitude of

the bullwhip effect. The longer the lead time, greater will be the

magnification.

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3. Order batching Batching or lumping small orders into bulk amounts

is used to take advantage of economies of scale. This will lead to very large order followed by a

period of no orders at all.

4. Price Fluctuations and promotions Discounts and favorable financing offered by the

manufacturers or distributors can cause a spike in buying that increases order for a time.

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Countermeasures to reduce Bullwhip effect

1. Avoid Multiple forecast2. Reducing Lead time3. Reduce the size of order4. Maintaining stable prices

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1. Avoid Multiple Forecast

Information sharing. Electronic Data Interchange (EDI) Vendor managed inventory (VMI)

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2. Reducing Lead time:

Cross docking EDI can also reduce lead times by speeding

up transmission of orders between supply chain partners.

3. Reducing size of the order: Ordering small batches improves demand

forecasting.

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3. Reducing the size of the order

Ordering small batches improves demand forecasting.

This also reduces lead time. Ways of ordering more frequently and in small

batches: Better forecasting Use of EDI More efficient transportation. Outsourcing.

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2. FORECASTING

Forecasting demand is a necessary part of business planning.

Forecasts are subject to uncertainty, and this uncertainty is one potential contributor to the bullwhip effect.

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Principles of Forecasting

1. Forecasts are (almost) always wrong.2. Forecast should include an estimate of error.3. Forecasts are more accurate for groups than

for single items.4. Near term forecasts are more accurate than

long term forecasts.

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Forecasting (continued) Independent Demand Dependant Demand.

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Qualitative Approaches to Forecasting Demand

1. Personal Insight.2. Sales force consensus estimate.3. Management estimate4. Market research.5. Delphi Method.

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Quantitative Approaches to Forecasting Demand

1. Naive approach.2. Moving average3. Weighted moving averages4. Exponential smoothing.

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1. Naive Approach

This forecast method assumes that demand in the next time period will be the same as demand in the last time period.

For e.g retailer sells 500 pair of shoes in February, naïve forecast for the March would be 500 pair of shoes.

This forecast can be considered baseline for use in evaluating more sophisticated approaches.

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Simple Moving Average It is more sophisticated than naïve approach. Averages actual demand data for a specified number

of previous time periods. It is moving average because it is recalculated for

each new period. Moving average is used when demand is fairly

constant from period to period. For e.g moving average for 3 month is calculated as (M1 + M2 + M3) / 3

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Simple Moving Average - ExampleWeek Demand

1 350

2 397

3 375

4 342

5 381

6 366

7 348

365 Forecast

3381366348 Forecast

Average Moving Month 3

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362.4 Forecast

5375342381366348 Forecast

Average Moving Month 5

8

8

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Weighted Moving Average More sophisticated than simple moving

average. It emphasize on recent periods and less on

earlier periods. Any combination of weights that sums to 1.00

may be used Any number of periods may be used

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Weighted Moving Average - ExampleWeek Demand

1 350

2 397

3 375

4 342

5 381

6 366

7 348359.4 Forecast

10 / (4)(348)](3)(366)(2)(381)[(1)(342) Forecast

Average Moving WeightedPeriod 4

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Using the data from the previous example, calculate a 4 week weighted moving averag.

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Exponential Smoothing It is a more sophisticated version of the weighted

moving average. It requires three basic terms:

The last period’s forecast. The last period’s actual demand. Smoothing constant (forecast error margin), represented

by Greek letter alpha (α). Formula:

New forecast = Last period’s forecast + α (Last period’s demand – Last period’s forecast’s)

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Exponential Smoothing - Example

44.91 Forecast

31)0.328)(47.-(1)(0.328)(40 Forecast

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Using the given data, calculate demand in week 12 using an exponential smoothing forecast with an alpha = 0.328

 Period Actual Demand

Forecasted Demand

7 48 52.69

8 45 51.15

9 47 49.13

10 45 48.43

11 40 47.31

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  Sales (Demand) Seasonal Avg Demand

Deseasonalized Avg Monthly Demand

Seasonal Index

Month 2005 2004 2003 2003-2005    Jan 32 27 34 31 14 2.214

Feb 26 31 33 30 14 2.143

Mar 12 11 10 11 14 0.786

Apr 5 4 3 4 14 0.286

May 4 2 0 2 14 0.143

Jun 3 1 2 2 14 0.143

Jul 2 1 0 1 14 0.071

Aug 5 3 4 4 14 0.286

Sep 10 11 9 10 14 0.714

Oct 15 13 14 14 14 1.000

Nov 25 29 27 27 14 1.929

Dec 32 30 34 32 14 2.286

Total Average Annual Demand 168    

Average Monthly Demand 14    

SEASONAL INDEX

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Mean Absolute Deviation (MAD) MAD is the average of the absolute deviation between actual

and forecasted values The forecast with the smallest MAD best fits the data

Periods ofNumber Demand Forecasted - Demand Actual

MAD

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MAD - Example

Period ActualDemand

ForecastedDemand Error Absolute

Error

7 48 52.69 -4.69 4.698 45 48.97 -3.97 3.979 47 45.82 1.18 1.1810 45 46.76 -1.76 1.7611 40 45.36 -5.36 5.36

Total 16.96

3.39 5

16.96 MAD

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Mean Squared Error (MSE) MSE is the average of all of the squared errors. It magnifies the error by each of the error. The forecast with the smallest MSE best fits the data

Periods ofNumber

Demand Forecasted - Demand Actual MSE

2

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Mean Squared Error - Example

Period ActualDemand

ForecastedDemand Error Squared

Error

7 48 52.69 -4.69 228 45 51.15 -6.15 37.829 47 49.13 -2.13 4.5410 45 48.43 -3.43 11.7611 40 47.31 -7.31 53.44

Total 129.56

25.91 5

129.56 MSE

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Collaboration Information Sharing Continuous Replenishment Vendor managed inventory CPFR

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1. Information sharing

This is often called Quick response program (QRP).

A system of linking final retail sales with production and shipping schedules back through the supply chain.

It requires that the retailer provide POS information to the supplier.

The supplier uses POS data for scheduling production and determining inventory levels.

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2. Continuous Replenishment

Also known as rapid replenishment. Prepare shipment intervals with the

collaboration with the customer. Goal is to reduce inventory level at the store,

so forecasting become more accurate.

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3. Vendor managed inventory

It is more sophisticated than QR, or continuous replenishment.

In VMI, supplier takes over inventory function. In VMI, supplier may do all or some of the following:

Determine how the inventory will be stored and displayed. Provide the bins or other storage units. Replenish the inventory on a schedule based on customer supplied

demand data. Maintain inventory records. Handle the delivery, receiving, stocking, and counting functions.

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Measuring VMI success

The partners can track the following measures of success:

Reduction or elimination of bullwhip effect. Reduced inventory costs in the supply network as a

whole. Greater percentage of on-time deliveries to retailer. Reduction or elimination of stock outs. Reduction of lead time for deliveries.

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COLLABORATIVE PLANNING, FORECASTING AND REPLENISHMENT (CPFR)

CPFR is a business practice that combines the intelligence of multiple trading partners in the planning and fulfillment of customer demand. Objective is to increase availability to the

customer while reducing inventory, transportation and logistics costs

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CPFR- Key Principles The consumer is the ultimate focus of all efforts Buyers” (retailers) and “sellers” (manufacturers) collaborate

at every level Joint forecasting and order planning reduces surprises in the

supply chain The timing and quantity of physical flows is synchronized

across all parties Promotions no longer serve as disturbances in the supply

chain