Chapter 5 demand management

LEARNING OBJECTIVES You should be able to: • Explain the role of demand forecasting in a supply chain • Identify the components of a forecast • Compare & contrast qualitative & quantitat

COLLABORATIVE PLANNING,

FORECASTING, & REPLENISHMENT

Chapter 5

LEARNING OBJECTIVES

You should be able to:

• Explain the role of demand forecasting in a supply chain

• Identify the components of a forecast

• Compare & contrast qualitative & quantitative forecasting techniques

• Assess the accuracy of forecasts

• Explain collaborative planning, forecasting, & replenishment

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Replenishment (CPFR)

important to manage demand, especially in pull manufacturing environments.

• Suppliers must find ways to better

match supply & demand to achieve optimal levels of cost, quality &

customer service to enable them to compete with other supply chains.

• Improved forecasts benefit all trading

partners in the supply chain &

mitigates/decrease supply-demand mismatch problems

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Demand Forecasting

 A forecast is an estimate of future demand & provides the basis for planning decisions

 The goal is to minimize forecast error

 The factors that influence demand must be considered when forecasting.

 Managing demand requires timely & accurate forecasts

 Good forecasting provides reduced inventories, costs, & stockouts, & improved production plans & customer service

Forecasting Techniques

 Qualitative forecasting is based on opinion & intuition.

 Quantitative forecasting uses mathematical models & historical data to make forecasts

 Time series models are the most frequently used among all the forecasting models.

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Forecasting Techniques

(Continued)

Qualitative Forecasting Methods

Generally used when data are limited,

unavailable, or not currently relevant Forecast

depends on skill & experience of forecaster(s) &

available information

Four qualitative models used are –

1 Jury of executive opinion

2 Delphi method

3 Sales force composite

4 Consumer survey

Forecasting Techniques

(Continued)

Quantitative Methods

that the future is an extension of the past

Historical data is used to predict future demand

more factors (independent variables) predict future demand

It is generally recommended to use a combination of

quantitative & qualitative techniques

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Forecasting Techniques

(Continued)

Components of Time Series

Data should be plotted to detect for the following

components –

 Trend variations : increasing or decreasing

 Cyclical variations : wavelike movements that are longer than a year (e.g., business cycle)

 Seasonal variations : show peaks & valleys that repeat over a consistent interval such as hours, days, weeks, months, seasons, or years

 Random variations : due to unexpected or unpredictable events

Forecasting Techniques

(Continued)

Time Series Forecasting Models

equal to the demand in the past period

Ft+1 = At

Where Ft+1 = forecast for period t+1

At = actual demand for period t

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Forecasting Techniques

(Continued)

Time Series Forecasting Models

data to generate a forecast Works well when demand

is stable over time

Where F t+1 = forecast for period t+1

A t = actual demand for period t

n = number of periods to calculate

moving average

Forecasting Techniques

(Continued)

Time Series Forecasting Models

based on an n-period weighted moving

average

Where F t+1 = forecast for period t+1

A i = actual demand for period i

n = number of periods to calculate

moving average

Forecasting

Time Series Forecasting Models

needed

F t+1 = F t + α (A t - F t ) or F t+1 = α A t + (1 – α ) F t

Where F t+1 = forecast for Period t + 1

F t = forecast for Period t

A t = actual demand for Period t

α = smoothing constant (0 ≤ α ≤1)

Forecast Accuracy

difference between actual quantity & the forecast –

Forecast error, e t = A t - F t

Where e t = forecast error for Period t

A t = actual demand for Period t

F t = forecast for Period t

Forecast Accuracy (Continued)

Several measures of forecasting accuracy follow –

 Mean absolute deviation (MAD)- a MAD of 0 indicates the forecast exactly predicted demand

 Mean absolute percentage error (MAPE)- provides a perspective of the true magnitude of the forecast error

 Mean squared error (MSE)- analogous to variance, large forecast errors are heavily penalized

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Forecast Accuracy (Continued)

Mean absolute deviation

(MAD)-MAD of 0 indicates the forecast exactly predicted demand.

Where e t = forecast error for period t

A t = actual demand for period t

n = number of periods of evaluation

Forecast Accuracy (Continued)

Mean absolute percentage error (MAPE) –

provides a perspective of the true magnitude of the forecast error.

Where e t = forecast error for period t

A t = actual demand for period t

n = number of periods of evaluation

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Forecast Accuracy (Continued)

Mean squared error (MSE) –

analogous to variance, large forecast errors are heavily penalized

Where e t = forecast error for period t

n = number of periods of evaluation

Forecast Accuracy (Continued)

Running Sum of Forecast Errors (RSFE) – indicates bias in the forecasts or the tendency of a forecast to

be consistently higher or lower than actual demand

Running Sum of Forecast Errors, RSFE = ∑

Forecast Accuracy (Continued)

Tracking signal –

determines if forecast is within acceptable control limits If the tracking signal falls outside the pre-set control limits, there is a bias problem with the forecasting method and an evaluation of the way forecasts are generated is warranted.

Tracking Signal =

MAD

RSFE

MAD RSFE

Useful Forecasting Websites

Collaborative Planning, Forecasting, &

Replenishment (CPFR)

CPFR is a concept that aims to enhance supply chain integration by supporting & assisting joint practices CPFR seeks cooperative management of inventory through joint visibility & replenishment of product throughout the supply chain Information shared btw suppliers & retailers aids in planning & satisfying customers demands through a supportive system of shared information This allows for continuous updating of inventory & upcoming requirements, essentially making the end-to-end supply chain process more efficient Efficiency is also created through the decreased expenditures for merchandising inventory, logistics and transportation across all trading partners.

( CSCMP)

Objectives of CPFR

• Improve demand forecast accuracy

• Deliver the right products at the right time to the right location

• Reduce inventory across the supply chain,

• Avoid stock-out

• Improve customer service

CPFR’s Benefits

introductions & store openings or closing to improve forecast accuracy

appear

purchasing pattern

ineffeciencies, improve customer service & increase revenues & profitability

•

Collaborative Planning, Forecasting, &

Replenishment (CPFR)

In short, we can see:

of multiple trading partners in the planning & fulfillment of customer demands

firms rather than sophisticated algorithms from only one firm.

requires a fundamental change in the way that buyers & sellers work together.

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VICS’s CPFR Model

Collaborative Planning, Forecasting, &

CPFR Model

Step 1: Collaboration Arrangement

Step 2: Joint Business Plan

Step 3: Sales Forecasting

Step 4: Order Planning/Forecasting

Step 5: Order Generation

Step 6: Order Fulfillment

Step 7: Exception Management

Step 8: Performance Assessment

Collaborative Planning, Forecasting, &

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Software Solutions

Forecasting Software

 Business Forecast Systems www.forecastpro.com

 SAS www.sas.com