The Importance of Forecasting on Sales Management Decision Making

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Table of Contents

Introduction
Factors affecting forecasting of sales
Conclusion
Works Cited

Introduction

In recent times, the business environment has increasingly become more unpredictable. This has made it very important for organizations across the globe to become vigilant when it comes to the issue of forecasting. In fact, for any organization to be successful in today’s business world, its methods of predicting the future in the key areas of its main business has to be improved continually. Otherwise, it faces the threat of becoming obsolete. Forecasting is therefore, a tool to be highly appreciated by the businesspersons of this century.

Forecasting is therefore the process of estimating or predicting the future outcome of different business aspects by use of historical data. These business aspects include; sales, revenue, market share, profits, expenses and many more. Forecasting is formed from two words, “fore”-which implies forward or the future- and “cast”-which implies to shape. In other words, forecasting implies the process of shaping the future (Taylor).

Sales forecasting is among the most basic aspects of a business entity. This is mainly because different departments of the business entity, namely the manufacturing unit, the sales and marketing unit, human resource and finance departments, feel its effect. The sales forecast helps the manufacturing unit of the business in determining the amount of the product in question to produce in a given time.

The same forecasts enable the human resource department to determine the amount of labor to hire/employ. The finance department also uses them in planning the allocation of funds. The top management uses the sales forecasts in setting both short-term and long-term strategies. It is therefore paramount for any business entity to have accurate sales forecasts.

Factors affecting forecasting of sales

Internal factors

These factors may arise from within the organization. Firstly, laborers may present problems from time to time and as a result hinder the smooth flow of production. Consequently, this may affect the amount of sales made seeing, as there may be shortages in production (lost demand).

Secondly, new products present the hardest task in predicting their sales. This is mainly so because they do not have past data/records of past sales. Estimating these kinds of sales solely relies on guesses and hope. Finally, organizations keep changing their production capacity e.g. through automation of process and expansion of the plant and workforces. These actions render previous sales forecasts useless and new forecasts have to be made (Hiller and Hiller,25).

External factors

These factors emanate from outside the organization. Firstly, the ever-changing consumers’ tastes and preferences present a problem that requires the constant revision of the sales forecasts. Secondly, the volatile nature of the world economy has become a hurdle in the recent past in the business of forecasting. This is because it heavily affects the consumers’ spending power and in return affects the amount and type of products the purchase.

Thirdly, the increased competition in almost all areas of business demands that sales forecasts be constantly adjusted in order to better place the organization strategically. Finally, today’s businesses are mostly if not entirely seasonal. This attributable to the fast evolving technology that is the driving force of most businesses. Consequently, sales forecasts have to be made limited to a given timeframe and with allowances to changes (Anderson and Sweeny,90).

Importance of sales forecasting to decision-making process

It is suicidal for any organization to ignore sales forecasting. It would mean that the organization fails to plan. Consequently, the organization will be going into the future blind as a bat. Sales forecasting enables preparation in production in order to meet the estimated demand. This could be done through processes such as; increased marketing activities to reach potential customers, plant expansion to increase capacity, technological update etc.

Secondly, sale forecasting brings to the attention of the management past failures and successes therefore, providing an opportunity for the organization to correct their mistakes and to maximize on their strengths. This may be achieved through SWOT analysis where the organization establishes its strengths, weaknesses, opportunities, and threats. Through SWOT analysis, the organization can forecast its sales based on its strengths and opportunities dealing with its weaknesses and avoiding the eminent threats.

Thirdly, sales forecasting plays a vital role in enabling an organization to maintain constant cash flows. This is important in that it helps the organization remain relevant in the industry it is involved. Sales forecasts are used to estimate and map out future business activities of the organization. This includes covering unforeseen increases and decreases in demand and allowances for seasonal business activities. With the right sales forecasts, the organization is able to cushion itself against such shocks and therefore continue in business.

Moreover, sales forecasting helps in achieving cost efficiency. It enable the production department to know what amount of the product is to be required at every one time and can therefore employ special methods such as Just-in-Time method of inventory control. Consequently, the organization is able to save both time and cost. The effects of cost efficiency are felt in both the statement of financial position and the statement of income (Render, Stair and Hanna,105).

Finally, sales forecasting enables the firm to achieve customer satisfaction and hence customer loyalty. Due to the effects of sales forecasts on the production process, the organization is able to meet lead time/delivery time. The result is greater customer satisfaction and customer loyalty.

Sales forecasting methods

There are three main methods of forecasting namely,

Time series models, causal models and qualitative models

Time series models

Time series models are quantitative methods. They employ past records in the determination of future forecasts. They work under the assumption that the future is not so different from the past.

Moving averages

It is mainly applicable in areas where the future is expected to be fairly even over a period. In this method, the averages of past data are found by simply adding the total sale of the periods and dividing by the number of the periods. If it is in months, one adds the total of latest month and subtracts that of the earliest month then divides with the number of the months. With time, the averages become predictable.

Exponential smoothing

It is an example of moving averages method but in this case the parameters may be more than one and the averages use an exponential formula as opposed to simple averages in the standard moving averages method.

Time series also involves trend projection method and decomposition method. It is also applicable in sales forecasts that are affected by seasons. With a seasonality index, one is able to produce forecasts that sensitive to recurring seasons.

Example:

Al Etisal distribution co. is one of the famous food and consumer goods Distribution Company in Baghdad. They have a wider range of products. Prince ice cream is one of their products that they start selling since 2009. Sales have a steady growth and its seasons have a significant impact on the ice cream sales. Management expects total sales for 2012 to be 3200.

Price ice cream sales unit management is required to set their forecast for 2012. They should find the best way in estimating the demand for the ice cream.

Below are the historical sales data for Prince ice cream for the last three years

The above example is highly affected by seasons and therefore the sales forecasts have to incorporate the seasons in their formulation. It is also an example of a quantitative problem. Therefore, time series is applicable in this problem specifically trend projection and by use of a seasonality index (Agee,258).

Month	Sales by Case 2009	Sales by Case 2010	Sales by Case 2011	average sale for the three years	average monthly demand	seasonal index	sales forecasts2012
Jan	30	33	42	35	186	0.188	50
Feb	28	32	49	36	186	0.196	52
Mar	35	47	55	46	186	0.246	66
Apr	55	67	90	71	186	0.380	101
May	194	180	209	194	186	1.046	279
Jun	290	280	376	315	186	1.698	453
Jul	459	504	703	555	186	2.990	797
Aug	350	490	543	461	186	2.482	662
Sep	189	227	290	235	186	1.267	338
Oct	76	142	188	135	186	0.729	194
Nov	62	109	97	89	186	0.481	128
Dec	42	56	68	55	186	0.298	79
total average sales = 2229
total expected sales = 3200

Average monthly sales = total average sales/ 12 months= 2229/12 =186

Seasonal index = average 3 years sales/ average monthly sales

Causal models

These models employ the use regression models to forecast sales. They come up with a list of variables that have effect on the sales of the product in question and through regression; they plot the various possibilities and therefore come up with reliable forecasts.

Qualitative models

Delphi method uses the views of various professionals or experts in the field who analyse the situation and provide their professional views. Normally, the group of experts includes; key decision makers, staff, and the respondents. The staff and respondents provide assistance based on their areas of expertise to the decision makers, who in turn come up with the forecasts.

Jury of executive methods uses the opinions of a jury made up of high-level managers and key decision makers to make forecasts. The group may however receive support from other technical professionals who provide background information to assist in decision-making.

Other qualitative methods include; sales force composite and consumer market survey. The latter uses consumer opinions while the former employs the opinions of the salespersons to come up with sales forecasts (Pinney,56).

Conclusion

In summary, sales forecasting is an important ingredient to success in the current and future business world. Therefore, management has to put emphasis on it to reap the benefits tied to the application of these tools. Management has to keep improving their approach to this process to remain relevant.

Works Cited

Agee, Marvin H. Quantitative Analysis for Management Decisions. London: Prentice Hall, 2001.

Anderson, David R and Dennis Sweeny. Quantitative Methods for Business. Chicago: South-Western College, 2009.

Hiller, Fredrick S and Mark S Hiller. Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets. London: McGraw-Hill Higher Education, 2010.

Pinney, William E. Management Science: An Introduction to Quantitative Analysis for Management. Toronto: Harpercollins College , 2000.

Render, Barry, Ralph M Stair and Michael E Hanna. Quantitative Analysis for Management. London: Prentice Hall, 2011.

Taylor, Bernard R. Introduction to Management Science. London: Prentice Hall, 2009.

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