Management Forecasting

Management Forecasting

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Forecasting is the process is making estimates on sales a business entity expects to achieve over a given period of time. This is a very essential tool in businesses today for both large and small medium enterprises. However, this tool can only be useful to organizations if its accuracy relates to the real situations in the business environment where about 90% of the forecasts are realized. Anything less than 60-70% accuracy in forecasts will not be a good thing for any businesses as this may result in overspending or overproduction by manufacturing companies.(Morlidge & Player, 2010).

Sales forecast allow firms to effectively manage their businesses with very crucial information on the patterns and trends of production throughout the year. Through this, a firm will be able to know how many customers it attracts every year and how many it loses. Firms will also be able to tell which month of the year they experience highest or lowest levels sales in different geographical locations. All these factors above clearly show that businesses should at least have sales forecast if at all they want to realize cost effectiveness in their operations.

A part from the positives of forecasting, it can also be disastrous if not accurately carried out. Wrong information given as a forecast can lead to losses if a company has produced more than what its customers require, or it can lead to shortages in the case of under production. To ensure an accurate forecast, businesses must invest significantly in a sales forecasting system as a vital part of their management systems. This of allow for regular forecasts to be carried out, hence increasing the accuracy level of the whole process. This can intern save the company in terms of running their business. Improving forecasts however does not happen, analysts advice that they be carried out on a monthly basis for about 12 months to give a clear picture of market trends (Zhang, 2004). In the traditional methods, forecasting was not as important because there were just a few companies offering the same products but that however is not applicable in the modern business world due to numerous companies offering similar products.


To: Sales Manager.



Subject: Improving on forecast accuracy

Mrs. XXX

I have attached my monthly report to this email just to elaborate on my latest findings. I just realized that over the past few months, it has not been business as usual.

Our sales have sharply dropped in our perceived major markets by a staggering 30%. This has intern put us in a less commanding position against our major competitors which is a big threat to the future of this company. All these happened in spite of the huge investments we’ve made by incorporating sales forecasting as part of our management systems.

We sold only 2000 units in the first quarter which was nearly 2000 units less of the forecasts made in the last quarter. From the initial investigations through interviewing our customers, it’s crystal clear that most of them didn’t buy not because of high costs of our products or difficult economic times, but most of them cited late deliveries as a major reason for opting for other suppliers. This therefore reveals that forecasting was not after all wrong, but our processes also might have led to the drop in sales.

My recommendations to improve on this will be to try and reduce lead times in our manufacturing processes by either increasing the workforce or by training employees to multitask so that we have one individual who can carry out more than one task. We can reduce the lead time purchase of plastic cover from vendor A by first knowing exactly how much of the plastic material we need for what period of time. This will enable us to have a stock that can last for the said period before another purchase is made. The lead time for purchasing steel can also be reduced once we have the accurate details of our market trend. We can source the steel from vendor C that can last us throughout the quarter. This will help us appreciate the benefits of lean manufacturing process where we have enough resources to produce just what we need. We can also introduce overtimes for interested employees so that we have longer working hours, hence meeting customer demands in time.

If all of these are carefully carried out, and our forecasts done in a more consistent manner, then without a doubt we shall realize an upward trend in our sales

Please let me know if you have any further questions

Best regards


Senior Sales Associate

A chart showing purchase orders

Materials Start date of purchasing Duration (days) Start of production

Steel 2-Jan 28 26-Jan

Plastic cover 5-Jan 21 27-Jan

Cardboard 5-Jan 28 3-Feb

To get the time when the manufacturing of the widget has to start and end, we must factor in all other lead times from purchase of materials to the actual production of the widget. Therefore, we’ll add the leads times for purchasing the raw materials and the lead time for the production of the widget.

Lead time to purchase raw materials = 4 weeks (28 days)

Lead time for the manufacturing of widget = 1week (7 days)

Lead time for packaging = 1 day

The shipping of the widget takes 3 weeks (21 days)

These sums up to a total of 53 days from the time of purchasing the raw materials to the time the product gets to the customer. The product should reach the customer by 3/31/2011. The time by which the production should start is given by 9- (7+1) = 1. Therefore the date is 3/1/2011.Time by which the production of widgets must end is given by 31-21 = 10. Hence the production of widgets should end by 3/10/2011. The date when the product must be shipped to meet the customer’s due date is given by, 31-21 = 10. Hence the date is 3/10/2011. The approximate date when the raw materials must be ordered is given by, counting 53 days backwards from date 3/31/2011. This leads us to 2/3/2011 as the date when the purchase of raw materials must commence. The date when application cover and packaging must take place to meet the customer’s due date is given by, 31-(21+1) = 9. Hence the date is 3/9/2011.


Morlidge, S., & Player, S. (2010). Future ready: How to master business forecasting. Hoboken, N.J: Wiley.

Zhang, G. P. (2004). Neural networks in business forecasting. Hershey, Pa. [u.a.: Idea Group Publ.

Wilson, J. H., & Keating, B. (2009). Business forecasting: With forecastX?. Boston: McGraw-Hill/Irwin.