Production Planning and Sales Forecast - Haleeb Foods

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Haleeb Foods

Production Planning and Sales Forecast

Improvement Action Report



Executive summary

With ongoing production losses and inability to fulfill market demand; company should revise its sales forecasting method according to new and latest methods. Heavy losses are being incurred due to poor inventory controls, spoiling of perishable goods and on not being able to meet the market demands.

Following steps should be taken to reduce costs and increase profits.

  • A committee should review the current forecasting method
  • Decomposition method should be looked into and employed in forecasting the product sales, since it considers Trend, Cyclic, Seasonal and Erratic events while forecasting

By reviewing and changing the current method a huge amount of revenue can be generated along with cost cutting. The following reports contain several forecasting methods being employed by push type Fast Moving Consumer Good companies. The most suited and balanced method would be Decomposition Method, which has been illustrated with an example in the report.

Introduction:

Production planning means to secure the production aims and to approximate the resources which would be requisite to achieve these aims. It prepares a thorough strategy to achieve the production goals cost-effectively and within time. It preplans each step in the production. It fore tell the issues, which may occur in the production process. It tries to eliminate these problems. And get rid of the reasons of wastage.

Production planning rectifies two main problems given below and provides solutions for them.

  • What work should be done in what precedence and sort?
  • How long would it take to complete different tasks?

So, production planning decides what goals are to be achieved and how to achieve these goals. It shows the course of action taken to get the desired results. It is based on sales forecasting and is a precondition of production.

Objectives of Production Planning:

Following are the twelve major objectives of production planning:

  • Effective utilization of assets: Production planning helps in efficient deployment of assets, working capability and equipments. This results in cost effectiveness and high efficiency of operations.
  • Stable Production Flow: Production planning ensures a stable and sturdy flow of production process. Such that all the equipment and machinery is utilized to maximum capacity. This helps in a smooth productions process without any unstable production outputs.
  • Calculating resources: Production planning helps to approximate the capital and resources like men, materials, etc. The rough is made on calculating sales forecast. So production strategy is kept to meet the market demands.
  • Ensure optimal inventory size : Production planning makes sure that an appropriate inventory is kept .This would in turn help in avoiding stock shortage or cost incurred due to excessive storage or inventory building. Necessary stocks are maintained. Raw material stocks are maintained at a reasonable level in order to satisfy the production demands. Finished goods stock is also kept at a reasonable size to meet the customer demand without creating any bottle neck.
  • Synchronize Department Activities: Production planning helps in synchronizing the interdepartmental activities. For e.g. Production would keep the procurement department in loop about a future rise in production activities and running out of raw material if procurement does not catch up with the pace . This coordination would result in profit to the organization.
  • Diminish wastage of raw materials: Production planning reduces wastage of raw materials. It ensures appropriate inventory of raw materials and materials handling and storage. This helps in reduction of raw material wastage. It also ensures quality control and quality assurance which would help in minimizing the number of rejects.
  • Increase in man-hour productivity: Production planning improves the man-hour productivity which insures maximum utilization of man power. Technical training is imparted to the workers and the profits are transferred to the labor in the form of an increase in wages and other incentives. Workers are motivated with better wages and pay scale and hence overall productivity is increased.
  • Help in expanding and capturing the market: Production planning ensures that the right products are delivered to the right customers at the right time and in right quantity. This is due to the availability of product in the desired market and gives a competitive edge to the company against potential competitors by capturing the market.
  • Better working environment and conditions: Planning the production in advance makes sure that the staff is not over worked or under worked at any time. It provides the staff with regular load of work without stressing them. It ensures better working hours, holidays and paid leaves along with other incentives.
  • Enforce total quality management: Production planning ensures that all quality standards are being observed and that no action is taken outside standard operating procedure of the company. Staff is made quality conscious of the product by training, guidance and quality circles.
  • Result in consumer contentment: Production planning helps in a regular supply of produce and services to the consumers resulting in consumer satisfaction.
  • Minimize the production costs: Production planning results in most advantageous utilization of assets, and it reduction of waste or scrap material. It also helps in maintaining most favorable size of inventories. All this results in a noticeable decrease in the production costs. (Akrani, 2012)

Literature Review:

A part of production planning is inventory control which is an essential and major component of production planning. Inventory management ensures that stock levels are maintained according to desired levels. Too much stock could result in high inventory levels which would incur high storage costs, and too less stock would not be able to cater for high customer demand. This part of production planning has been researched and work on for a long time now. Better production planning and keeping adequate inventory levels would help in increased profits since inventory costs are cut and production process is made more organized. In order to keep adequate inventories, a sales forecast is mandatory which would help in deciding the correct raw material or finished good stock for future sales.

Several forecasting methods are appropriate in connection with inventory control. Such forecasts usually take in account small intervals of time. Very rarely a time span of a year or more is taken into consideration. Generally two models are used to forecast sales considering the previous data.

• Extrapolation of Historical Data:

Forecast is based on extrapolation of previous demand data. Such a technique is easy to and effective, apart from being quick. Several methods are employed to forecast sales depending upon their accuracy

Method 1: Percent over Last Year

This method requires a percentage figure that is expected as an increase or decrease in the sales over the year. The new percentage increase or decrease in sales is added into the old data to obtain a new sales forecast. This data can only be implied if a large previous data is available and expected rise or fall in sales can be guessed accurately. Depending upon the chance factor in this method, it is not reliable and can be somewhat inaccurate. (Edwards, 2015)

Example: Method 1: Percent Over Last Year

The Percent over Last Year formula multiplies sales data from the previous year by a factor you specify and then projects that result over the next year. This method might be useful in budgeting to simulate the affect of a specified growth rate or when sales history has a significant seasonal component.

Forecast specifications: Multiplication factor. For example, specify 110 in the processing option to increase the previous year's sales history data by 10 percent.

Required sales history: One year for calculating the forecast, plus the number of time periods that are required for evaluating the forecast performance (periods of best fit) that you specify.

This table is history used in the forecast calculation:

Jan

Feb

March

April

May

June

July

Aug

Sep

Oct

Nov

Dec

128

117

115

125

122

137

140

129

131

114

119

137

If a percentage increase in sales is expected to be 10% over the year. Then the Following year sales forecast would be like the table below.

Jan

Feb

March

April

May

June

July

Aug

Sep

Oct

Nov

Dec

141

129

127

138

134

151

154

142

144

125

131

151

January forecast equals 128+12.8 = 140.8 rounded to 141.

February forecast equals 117+11.7 = 128.7 rounded to 129.

March forecast equals 115+11.5 = 126.5 rounded to 127. (Edwards, 2015)

Method 2: Calculated percent over last year:

This method compares the sales of a specific period in a year to the sales of the same specific period in the previous year. The comparison gives the percentage increase or decrease in the sales, and then this percentage is multiplied with each period to give out forecast.

A one year sales history along with number of periods of sales order history is required for forecasting through this method. Short term seasonal demands can be forecasted through this method with a clear indication of increase or decrease in sales. (Edwards, 2015)

Example: Method 2: Calculated Percent Over Last Year

Forecast specifications: Range of sales history to use in calculating the rate of growth. For example, specify n =4 in the processing option, which shows the sales history for the 4 most recent period are to be compared to same 4 periods of the previous year. Use the calculated ratio to make the projection for the next year.

This table is history used in the forecast calculation, given n = 4:

Year

Jan

Feb

March

April

May

June

July

Aug

Sep

Oct

Nov

Dec

1

128

117

115

125

122

137

140

129

131

114

119

137

2

-

-

-

-

-

-

-

-

118

123

139

133

Calculation of Percent over Last Year, given n = 4.

Year 2 equals 118 + 123 + 139 + 133 = 513.

Year 1 equals 131 + 114 + 119 + 137 = 501.

Ratio percent = (501/513) × 100%= 97.66%

This table is the forecast for next year, 97.66 Percent/Last Year:

Jan

Feb

March

April

May

June

July

Aug

Sep

Oct

Nov

Dec

125

114

112

122

119

134

137

126

128

111

116

134

January forecast equals 128 × 0.9766 = 125.00 rounded to 125.

February forecast equals 117 × 0.9766 = 114.26 rounded to 114.

March forecast equals 115 × 0.9766 = 112.31 rounded to 112. (Edwards, 2015)

Method3: Moving Average Method:

According to this method, forecasting is done of the year in consideration by taking an average of the actual sales of the proceeding few years. The larger the number of years taken to forecast, the more accurate would be the forecasting. (Nagarajan, 2004)

moving

Example: Method 3: Moving Average Method

Moving

When a forecast is developed for the next period, the sales in the oldest period are dropped from the average and are replaced by sales in the newest period; hence the name “moving averages”. If the company operates in a stable environment a short two or three year moving average may be useful. Moreover, if a firm is in an industry which exhibits cyclical variations, the moving average should use data, equal to length of a cycle or a longer averaging period. (Havaldar, 2010)

The advantages of this method are:

  1. Relatively simple method
  2. Easy to calculate
  3. Widely used for short term or medium term sales forecasts

The disadvantages are

  1. Unable o predict a downturn or upturn in the market
  2. Cannot be used for long term forecast
  3. Historical data is needed

Method 4: Weighted Moving Average:

In moving average method all the data taken under consideration is given equal weightage.If three years are taken to forecast a fourth year. The first three years are each given a weight age of 1/3.But in weighted moving average, different weight age is given to different data according to their importance .All the weight age must add up to 1 in the end. (Don Dayananda, 2002)

The formula for this method is

weighted

Example: Method 4: Weighted Moving Average:

John wants to forecast sales of floor detergent for April using a 4-period weighted moving average method. The sales information of January through March is given in the table below.

weighted

The forecast for April is 650. (Li, 2007)

Method 5: Exponential Smoothing with Trend and Seasonality:

This method is quite similar to moving average method of sales forecasting. By this method the forecaster can influence the forecast with certain specific periods in contrast with some others. The equation for this method is as follow

Sales Forecast for next year=(L)(actual sales this year)+(1-L)(this year’s sales forecast)

Where L is smoothing constant or probability weighing factor

The forecaster would decide the value of L on following bases

  • Review of sales data
  • Knowledge and observation about the conditions
  • Intuition and experience (Krishna K Havaldar, 2007)

Example: Method 5: Exponential Smoothing with Trend and Seasonality:

exponential

Sales forecast for the year 2004 = (0.2)(956)+(0.8)(880)

= 895

Method 6: Decomposition Method:

According to this method the previous sales data is divided considering four major factors influencing it which are trend, cycle, seasonal and erratic events. This method is also quite similar to moving average method of sales forecasting. These components are then recombined to produce the sales forecast. (Havaldar, 2010)

Example: Method 6: Decomposition Method

decomposition

Let’s consider the above example. Assume that various analysis have broken down the previous sales data into the following component into the following component; a growth of 3 percent in sales due to the development in technology, capital formation and population, increased terrorist activities are expected to reduce sales by 5%, a 10% reduction in sales is expected due to recession in demand and the sales in the third quarter of the year are expected to go up by 15% due to festive season, as compared to other three quarters.

The forecaster would combine the different components, as follows, in order to forecast sales of 2008.Supose that the total sales in 2007 was 956million.The trend component shows that 2008 sales would be 985million(1.03(956)).The sales are reduced due to introduction of erratic event component to 936 million(=0.95(985)).The sales forecast changes further due to cyclic component of recession to 842million(=0.9(936))).Thus the annual sales forecast for 2008 is 842million.The quarterly sales forecast would be 210million(0.25(842)),if the seasonal component is not considered. The seasonal component in third quarter would suggest 15 percent increase in sales forecast that is 242million (=1.15(210)) for the third quarter and consistent sales forecast of 200 million (= (842-242)/3) each for the other here quarters. (Havaldar, 2010)

Application:

Haleeb food is one of the biggest dairy product manufacturers in Pakistan. Considering the history and corporate growth, the company has come a long way since it was first established in 1984. The Company was the first in producing pasteurized and UHT treated dairy. It started with just milk and has now expanded to a range of food products including juices, desi ghee, butter, powdered milk and several other dairy liquids. Company got ISO 9002 certification back in 1997 and by the year 2000, haleeb was exporting products to the neighboring countries. In 2002 company contributed to 54% of the country’s packed milk market, making it the leading market share holder in packed milk.

With all the pioneer abilities the company could not hold the dominant position in market in later years. By the year 2012 the company was on the verge of bankruptcy due to poor management. Company had to sale off most of its shares to survive the downfall. The whole management was changed as the company changed hands.

Many a reason could be attributed to the downfall of the company. Which are as follow

  • Expansion of product range without proper market survey and consideration of existing similar products. The development of new products costed a fortune to the company and existing products didn’t give any space to the new products in the present market. Hence a huge loss was done to the company on introduction of new products and their failure.
  • In order to capture the market and sweep off new competitors, huge stocks of products were given on credit. Retailers filled their stocks with the products. The products did not sell as they were supposed to, due to poor sales forecasting. Hence being perishable products the products were returned to the company after expiring and sitting on the shelves of retailers.
  • Poor sales forecasting on the management side was the biggest reason of downfall. The seasonal, cyclic, trending and erratic events were not catered for. The products were not being produced according to the market demands and hence the customer not only moved to the substitute products but changed loyalty after getting used to the new product. Throughout the year the production was kept at the steady pace, without considering the external factors that would create a sharp hike or fall in demand of the product. For instance being a Muslim country the consumption of dairy products is increased at around Muslim religious festivals like Ramadan and Eid. The usage of fresh cream is increased to double in Ramadan and around Eid the consumption is tripled. Such sudden increase in production was not met with proper production planning. Had the planning been done in consideration with “Decomposition Method”. Not only the changing market demands would have been encountered with an aggressive approach but clientage could have been saved.

Above analysis shows how forecasting is an integral part of operations and not following it would not only cost the company but could lead to its bankruptcy.

Conclusion:

The management should change the forecasting method from “Moving Average Method” to “Decomposition Method”. As it is clear from the fact that the current method employed is unable to forecast on long term basis and is unable to predict the sharp changes in the market demands.

Decomposition method would consider trend, cycle, seasonal and erratic events while forecasting and through this method not only huge revenue can be generated by escalated sales but also costs can be cut by maintaining the inventory levels to a desired adequate levels.

References:

1)Nagarajan, K., 2004. Project Management. 2 ed. New Delhi: New Age International(P) Limited.

2)Havaldar, 2010. Business Marketing;Text and Cases. 3 ed. New Delhi: Tata McGraw Hill Education Private Limited.

3)Don Dayananda, R. I. H. H. R., 2002. Capital Budgeting:Financial Appraisals of Investment Projects. 1 ed. Cambridge: Cambridge University Press.

4)Li, L., 2007. Supply Chain Management:Concepts,Techniques and Practices. 1 ed. London: World Scientific Publishing Co. Pte. Ltd..

5)Krishna K Havaldar, V. M. C., 2007. Sales and Distribution Management:Text and Cases. 1 ed. New Delhi: Tata McGraw Hill Publishing Company Limited.

6)Havaldar, K. K., 2010. Business Marketing:Text and Cases. 3 ed. New Delhi: Tata McGraw Hill Education Private Limited.

7)Edwards, J., 2015. Understanding Forecast Levels and Methods. [Online] Available at: http://docs.oracle.com/cd/E16582_01/doc.91/e15111/und_forecast_levels_methods.htm#EOAFM00177 [Accessed 17 March 2015].

8)Akrani, G., 2012. What is Production Planning? Meaning Definition Objectives. [Online] Available at: http://kalyan-city.blogspot.com/2012/01/what-is-production-planning-meaning.html [Accessed 17 March 2015].

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