Chapter 11: Strategic Leadership Chapter 6 Forecasting supply chain requirements

Chapter 11: Strategic Leadership

Chapter 6

Forecasting supply chain requirements


Contents• Introduction• Forecasting in general• Types of forecasting• Long-term and short-term forecasting• The forecasting process• Appropriate forecasting models• Validating forecasting models• Stationary data• Data with trend• Forecasting seasonality• Conclusion


Introduction

• Demand forecasting refers to the process of determining the amount of product and related information that consumers will require, either in the short or long term.

• The responsibility for preparing demand forecasting usually lies in the marketing and sales

departments.


Forecasting in general: features of forecasting

• It is difficult to forecast accurately.• Forecasts for groups of items are often more accurate than forecasting demand for a single item.• Forecasts for a shorter time period are usually more accurate than forecasting for a longer time horizon.• What happened in the past can be used as an important guideline when making forecasts.


Types of forecasting

• A time series is a time-ordered sequence of observations taken at regular intervals, e.g. daily, weekly, monthly, quarterly or annually.

• Explanatory models, also called regression models, rely on the identification of related variables that can be used to predict values of the variable of interest. A mathematical relationship is developed between demand, for example, and some other factors that cause demand behaviour.


Types of forecasting:qualitative forecasting techniques

Qualitative forecasting techniques are appropriate when little or no quantitativeinformation is available, but sufficient qualitative knowledge exists. It is usually a product of judgement and accumulated knowledge.

Four techniques:

• The Delphi method

• Jury of executive option

• Sales force composite

• Consumer market survey


Long-term and short-term forecasting• Short-term forecasting is crucial for day-to-day planning, such as determining safety stock levels and production plans.• Medium-term forecasts are usually needed for budget purposes, including forecasts of sales, prices and costs for the entire company and different divisions.• Long-term forecasting is primarily needed for capital expansion plans, selecting research and development projects, launching new projects, formulating long-term goals and strategies and adapting to environmental changes.


The forecasting process

• Specify the objectives clearly. • Determine what to forecast. • Determine the time horizon of the forecast. • Gather the data needed to make the forecast. • Do the model selection. • Validate the forecasting models. • Make the forecasts.• Implement the results.• Track the results. Stopped for FT on 10/09/15


Appropriate forecasting models: types of data patterns

a. Level or horizontal pattern

b. Trend pattern

c. Seasonal pattern

d. Cycle


Appropriate forecasting models: selecting an appropriate forecasting model


Measures of accuracyThese measures evaluate how well each forecasting method explains past behaviour of the time series variable. Three of the most common quantitative measures of accuracy are as follows:


Evaluate forecasting technique

Step 1: Divide time series into an initialisation set and a test set.

Step 2: Choose an appropriate forecasting method.

Step 3: Use the initialisation set to estimate the trend and seasonal components. Establish fit accuracy.


Evaluate forecasting technique (continued)

Step 4: Apply the method to the test set and establish forecast accuracy.

Step 5: Repeat steps 2 to 4 for several forecasting techniques, and select the forecasting method which performs best with regard to forecast accuracy and which is also appropriate depending on the data pattern.

Step 6: Use the complete data set and apply the selected technique to do the forecast.


Stationary data: naïve forecastingThe naïve method assumes that the next period’s forecast is equal to the current period’s actual value. The forecast is as follows:Ft+1 = At, whereFt+1 is the forecast for the next period t+1A t is the actual value for the current period t andT is the current time period. Week number Actual sales Naïve forecast

t At Ft

1 2502 245 2503 247 2454 251 2475 250 2516 2507 2508 2509 250

Note that F6 = A5

and F7 = F6, etc.


Stationary data: moving averagesThe simple moving average uses an average of n of the most recent observations to create a forecast for the next period.The following formula is used:

whereFt+1 is the forecast for the next period t+1;A t is the actual value for the current period t;t is the current time period, and n is the number of periods included in the moving average.


Stationary data: moving averages (continued)

Week number Actual sales MA-3

t At Ft

1 250

2 245

3 247

4 251 247,33

5 250 247,67

6 245 249,33

7 248,67

8 247,89


Stationary data: simple exponential smoothing


Data with trend: Holt’s method


Data with trend: linear regression


Forecasting seasonalityMultiplicative seasonality: seasonality is expressed as a percentage of the average. Step 1: Calculate the forecast demand for the known observations using linear regression or Holt’s method. Step 2: Calculate the actual sales as a percentage of the trend by dividing the actual sales by the forecast sales for each time period. Step 3: Calculate the average seasonal index for each season by adding up the values calculated in Step 2 for that season and dividing by the number of time periods. Step 4: Multiply the forecast by the average seasonal index for the corresponding season. This will produce a forecast adjusted for seasons.


Forecasting seasonality: exampleQuarte

rTime

periodUnit

salesLinear

Actual as %

Seasonal

X Y forecastof

trendforecast

Coefficients

1 1 240 242,37 0,99 239,63Interce

pt241,4158

2 2 245 243,33 1,01 242,79 X 0,9556393 3 247 244,28 1,01 245,144 4 249 245,24 1,02 247,69

1 5 243 246,19 0,99 243,41 QuarterSeasonal

index2 6 245 247,15 0,99 246,61 1 0,9886853 7 248 248,11 1,00 248,98 2 0,9978074 8 251 249,06 1,01 251,55 3 1,0035271 9 245 250,02 0,98 247,19 4 1,0099832 10 252 250,97 1,00 250,423 11 253 251,93 1,00 252,824 12 255 252,88 1,01 255,411 13 251 253,84 0,99 250,972 14 254 254,79 1,00 254,243 15 257 255,75 1,00 256,654 16 259 256,71 1,01 259,271 17 257 257,66 1,00 254,752 18 256 258,62 0,99 258,053 19 259 259,57 1,00 260,494 20 263 260,53 1,01 263,131 21 261,48 258,532 22 262,44 261,863 23 263,40 264,32

4 24 264,35 266,99

1. Forecast demand using linear regression.

2. Divide actual sales by linear forecast to get actual as % of trend.

3. Calculate average seasonal index for each quarter.

4. Calculate the seasonal forecast by multiplying the linear forecast with the appropriate seasonal index.


Conclusion

• This chapter presented several methods for forecasting future demand or sales. • Different methods for stationary time series data and non-stationary time series data were presented. • A practical method for addressing seasonality was also suggested. • In each case, the goal was to fit models to the past behaviour of a time series, then to first test the models on test data, and then to fit the appropriate model to project future values.• More advanced techniques were discussed in the appendix of the chapter. • This chapter is only an introduction to forecasting and the techniques should be seen as such.

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Chapter 11: Strategic Leadership Chapter 6 Forecasting supply chain requirements