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OM/U4 Topic 3 Demand Forecasting in Supply Chain, Simple Moving Average, Weighted Moving Average, Exponential Smoothening Method

Demand Forecasting facilitates critical business activities like budgeting, financial planning, sales and marketing plans, raw material planning, production planning, risk assessment and formulating mitigation plans. Outlined below are the impacts of Demand Forecasting on Supply Chain Management:

(i) Improved supplier relations and purchasing terms: Demand Forecasting drives the raw material planning process which facilities the Purchasing Managers to release timely purchase plan to suppliers. Visibility and transparency of raw material demand improve supplier relations and empowers Purchasing Managers to negotiate favorable terms for their companies.

(ii) Better capacity utilization and allocation of resources: Based on the current inventory levels, raw material availability and expected customer orders, production can be scheduled effectively. This leads to improved capacity utilization and judicious allocation of manufacturing resources.

(iii) Optimization of inventory levels: A proper Demand Forecast provides vital information for driving the desired raw material, WIP and finished goods inventory levels. This reduces the Bullwhip effect across the Supply Chain, leading to optimization of inventory levels and reduction in stock-out or over-stocking situations.

(iv) Improved distribution planning and logistics: Apart from small businesses, this is particularly evident in businesses dealing with multiple SKUs and wide distribution networks. Distribution and Logistics Managers are enabled to balance inventory across the network and negotiate favorable terms with Transporters.

(v) Increase in customer service levels: With optimized inventory levels and improved Distribution Planning and Logistics, customer service metrics like on-time delivery (OTD), on-time in-full (OTIF), case-fill/fill-rate, etc. are improved due to right sizing and right positioning of inventory.

(vi) Better product lifecycle management: Medium to long range Demand Forecasts provide better visibility of new product launches and old product discontinuations. This drives synchronized raw material, manufacturing and inventory planning to support new product launches and most importantly, reducing the risk of obsolescence of discontinued products.

(vii) Facilitates performance management: Management can set KPIs and targets for various functions like Sales, Finance, Purchase, Manufacturing, Logistics, etc. based on the medium to long range plans derived from the Demand Forecasting process. Organizational efficiency, effectiveness, and improvement initiatives can be designed for key areas of the company.

Moving Averages Method of Sales Forecasting

In this method the sales forecasting is obtained by taking average of past sales over a desired number of past periods (may be years, months or weeks). Extending the moving average to include more periods may increase the smoothening effect but decreases the sensitivity of forecast.

  1. Simple Moving Average

A simple moving average is formed by computing the average price of a security over a specific number of periods. Most moving averages are based on closing prices.

The simple moving average (SMA) calculates an average of the last n prices, where n represents the number of periods for which you want the average:

Simple moving average = (P1 + P2 + P3 + P4 + … + Pn) / n

For example, a four-period SMA with prices of 1.2640, 1.2641, 1.2642, and 1.2641 gives a moving average of 1.2641 using the calculation [(1.2640 + 1.2641 + 1.2642 + 1.2641) / 4 = 1.2641].

  1. Weighted Moving Averages

The moving averages as calculated in the preceding part are known as un-weighted because the same weight is assigned to each of the numbers whose average is being ascertained. Some enterprises base their forecast on a weighted moving average.

Let us assume that the number of customers who visit during two weeks interval provides a sound basis for third week forecast and let us further assume that first week is less important than second and consequently we assign weights of 0.4 to first week and 0.6 to second week. The weighted average for 9th week would be

0.4 X 549 + 0.6(474) = 220 + 284 = 504

Similarly the weighted moving averages for other weeks are enlisted in the following table:

A forecast based on weighted moving averages for number of customers.

TOPIC 3.1.jpg

Advantages of the Moving Average Method

(i) This technique is simpler than the method of least squares.

(ii) This method is not affected by personal prejudice of the people using it.

(iii) It the period of moving average is equivalent to the period of the cycle. The cyclic variations are eliminated.

(iv) If the trend in the data if any is linear the moving average gives a good picture of long term movement in data.

(v) The moving average technique has the merit of flexibility i.e., if a few years are added the entire calculations are not changed due to adoption of new conditions.

Limitations of the Moving Average Method

(i) It does not result in mathematical relations which may be used for sales forecasting.

(ii) There is a tendency to cut corners which results in the loss of data at the ends

(iii) A great deal of care is needed for the selection of the period of moving average since the wrong periods selected would not give the correct picture of the trend.

(iv) In case of the sharp turns in the original graph, the moving average would reduce the curvature.

(v) It is very sensitive even to small movement in the data.

3. Exponential Smoothing and Moving Average Method

This method of sales forecasting is a modification of the moving average method or in better words it IS an improvement over the moving average method of forecasting. This method tries to eliminate the limitations of moving averages and removes the necessity of keeping extensive past data it also tries to remove the irregularities in demand pattern.

This method represents a weightage average of the past observations. In this case most recent observations is assigned the highest weightage which decreases in geometric progression as we move towards the older observations.

Since the most recent observations which are likely to reflect more up- to-date information or average of the series are given more weightage so it becomes one of the most accurate statistical method of sales forecasting. This method keeps a running average of demand and adjusts it for each period in proportion to the difference between the latest actual demand figure and the latest value of the average.

When there is no trend in the demand for a product or service, sales are forecasted for the next period, by means of the exponential smoothing method by using the expression

Forecast for the next period = a (latest actual demand) + (1 – α) old estimate of latest actual demand where a represents the value of a weighting factor which is referred to as a smoothing factor.

This method follows the equation

Fn= Fn -1 + α (D n-1 – F n-1)

where Fn= forecast for the next period

Fn-1 = forecast for previous period

n-1 = demand in previous period.

If a is equal to 1. then the latest forecast would be equal to previous period actual demand In practice, the value of a is generally chosen between 0.1 and 0.3. The application of technique is demonstrated by using data of moving averages method of sales forecasting on page 78. In the application of the method, we would use the value of a as 0.10.

If the actual demand for 3rd week is 487, the forecast for the 4th week will be

0.10(487) + (1.00 – 0.10)550 = 544

Similarly, if the actual demand for 4th week is 528 customers, the forecast for the 5th week will be

0.10 (528) + (1.00 – 0.10) (544) = 542

If this procedure had been applied during the entire 8 week period the results are shown in the following table. The unadjusted forecast error is also indicated under column D = B – C. If the value of a is not given; it can be determined by an approximate relation of a.

α = 2/ Number of periods in moving average + 1

TOPIC 3.2.jpg

The weight factors a is concerned, it can assume a minimum value 0 and a maximum value of 1. The greater the value of a, the greater is the weight placed on recent data. When the value of a is 1, the forecast will be equal to the demand experienced during the last period.

Although the value of a varies from product to product but most organization have found that a value between 0 06 to 0.20 usually proves to be satisfactory.

When attempting to find out what value of a should be used for a product or service the organization/enterprise can select various values, examine the past forecasts with the use of these values and adopt for future use the one which would have minimized forecast errors in the past.

In this way we go close of the description of exponential smoothing as it is applied when a trend in sales/service is available. In case trend exist, a trend adjustment can be made with this technique but its application becomes bit difficult.


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