The document discusses demand planning in supply chain management. It covers key topics like demand forecasting techniques, sources of demand variability, and collaboration approaches. Demand forecasting involves analyzing trends, seasonality, random variation, and cycles. Qualitative and quantitative forecasting methods are described, including naive, moving averages, and exponential smoothing. Collaboration approaches like information sharing, continuous replenishment, vendor-managed inventory, and CPFR aim to reduce the bullwhip effect through better demand signal synchronization.

1. Supply Chain Management:
DEMAND PLANNING

2. 2
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.

3. 3
 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.

4. 4
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.

5. 5
Sources of demand variability
 Competition.
 Seasonality
 Life cycle trends
 External factors.
 Promotions.
 Disasters

6. 6
DEMAND PLANNING
The 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.

7. 7
Importance
If production outstrips demand, you suffer
financial losses and perhaps go bankrupt.
If order exceeds supply, your frustrated
customers may go to your competitors.

8. 8
ASPECTS OF DEMAND PLANNING
1. Supply chain dynamics.
2. Forecasting
3. Collaborative demand planning
4. Role of marketing.

9. 9
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”.

10. 10
The Bullwhip Effect

11. 11
Causes of the Bullwhip Effect
1. Demand Forecast Errors
2. Lead time
3. Order batching (lumping)
4. Price fluctuations and promotions.

12. 12
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.

13. 13
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.

14. 14
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.

15. 15
Countermeasures to reduce Bullwhip effect
1. Avoid Multiple forecast
2. Reducing Lead time
3. Reduce the size of order
4. Maintaining stable prices

16. 16
1. Avoid Multiple Forecast
 Information sharing.
 Electronic Data Interchange (EDI)
 Vendor managed inventory (VMI)

17. 17
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.

18. 18
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.

19. 19
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.

20. 20
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.

21. 21
Forecasting (continued)
 Independent Demand
 Dependant Demand.

22. 22
Qualitative Approaches to Forecasting Demand
1. Personal Insight.
2. Sales force consensus estimate.
3. Management estimate
4. Market research.
5. Delphi Method.

23. 23
Quantitative Approaches to Forecasting Demand
1. Naive approach.
2. Moving average
3. Weighted moving averages
4. Exponential smoothing.

24. 24
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.

25. 25
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

26. 26
Simple Moving Average - Example
Week Demand
1 350
2 397
3 375
4 342
5 381
6 366
7 348
365Forecast
3
381366348
Forecast
AverageMovingMonth3
8
8
=
++
=
362.4Forecast
5
375342381366348
Forecast
AverageMovingMonth5
8
8
=
++++
=

27. 27
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

28. 28
Weighted Moving Average - Example
Week Demand
1 350
2 397
3 375
4 342
5 381
6 366
7 348
359.4Forecast
10/(4)(348)](3)(366)(2)(381)[(1)(342)Forecast
AverageMovingWeightedPeriod4
8
8
=
+++=
Using the data from the previous
example, calculate a 4 week weighted
moving averag.

29. 29
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)

30. 30
Exponential Smoothing - Example
44.91Forecast
31)0.328)(47.-(1)(0.328)(40Forecast
12
12
=
+=
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

31. 31
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

32. 32
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
PeriodsofNumber
DemandForecasted-DemandActual
MAD
∑=

33. 33
MAD - Example
Period
Actual
Demand
Forecasted
Demand
Error
Absolute
Error
7 48 52.69 -4.69 4.69
8 45 48.97 -3.97 3.97
9 47 45.82 1.18 1.18
10 45 46.76 -1.76 1.76
11 40 45.36 -5.36 5.36
Total 16.96
3.39
5
16.96
MAD ==

34. 34
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
( )
PeriodsofNumber
DemandForecasted-DemandActual
MSE
2
∑=

35. 35
Mean Squared Error - Example
Period
Actual
Demand
Forecasted
Demand
Error
Squared
Error
7 48 52.69 -4.69 22
8 45 51.15 -6.15 37.82
9 47 49.13 -2.13 4.54
10 45 48.43 -3.43 11.76
11 40 47.31 -7.31 53.44
Total 129.56
25.91
5
129.56
MSE ==

36. 36
Collaboration
 Information Sharing
 Continuous Replenishment
 Vendor managed inventory
 CPFR

37. 37
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.

38. 38
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.

39. 39
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.

40. 40
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.

41. 41
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

42. 42
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