Role of forecasting in operations management
Forecasting is a technique of predicting future aspects of a situation like business or service. In forecasting, we translate past data or experiences to predict future occurrences. According to Chaman L. Jain (2004), the importance of forecasting arises due to the uncertainty of the future and the need for planning. To maximize production, each organization has to match the supply and demand (Malehorn, 2005). These are critical factors which give forecasting a significant role in operation management of any organization. With the rising competition, each company needs to make accurate predictions which will be used to make decisions on future production of the company. In the past managers did not find it necessary to do forecasting as sales where influenced by the reputation of the name of the company. But with the rising competition, forecasting has become an integral part of operation management. In our discussion, we will describe the role of forecasting in operations management and its application in operation management. We will consequently apply one forecasting technique to the case study of Highline Financial Services Limited and finally use the data to advise Freddie Mack decision to make on the third year demands.
Forecasting and operations management
Forecasting plays a significant role in operation management as it influences directly the delivery of services or products by the organization. According to Shim (2000) Operation management involves the management of all resources necessary for the production as well as service delivery. Materials, human resources as well as information need to be available for the smooth running of any organization. Forecast provide the managers with the estimate of these requirements by analyzing the past and present data available at their disposal as suggested by (Kolli, 2000). With all the information available for the managers they will use their necessary knowledge and experience to make the right decisions.
Highline Financial Services forecasting
The aim of this forecast is to provide Freddie Mack with a valuable prediction for the third year demand of services provided by Highline Financial Services Limited. This estimate will form the basis in which Freddie will make the decision on financial and personnel plans for the third year. The time frame of this forecast is three years with each having four quarters and information for two years is available. In any forecast the first step is to determine the aim of the projection as stated by Malehorn (2005) and in our case is to give Freddie estimates of the demand for the three services provided by the company. The second step in forecasting will be to identify the available data. Two-year data, in this case, are available each year having information on the four quarters. Once we have identified the available information, we proceed to select the forecasting technique. In choosing forecasting method, the important factor to consider will be the type of data available (U. K. Srivastava, 2005).
In this case study, we are going to use the moving average technique to forecast the demand for the four quarters of the third year. This method is used to come up with the trend in the data set available. Carlberg (2002) stated that moving average is average of numbers which may be sales, the demand for raw materials required for a given period. In this case, of Highline financial service limited we are going to use the available data for the two years each comprising of four quarters. According to Robert A. Yaffee (2000), periods used in the moving average technique is arbitrary and it can be four- period moving average or even eight-period moving average. In this case, we are going to use eight-period moving average where these periods are the quarters. In determining the demand for service A in the first quarter of the third year, we will add all values of the demand for service A in the eight quarters and divide by eight getting the value of 75((60+45+100+75+72+51+112+86)/8). In the second quarter of the third year, the demand for service A will be ((45+100+75+72+51+112+86+75)/8) which is 77. This method of calculation will be used to calculate the demand value for the four quarters of the third year in all the three categories of services. Then the table will be drawn from all the results calculated, and the comparison is made with the available data set for the two years .
Observation and analysis
The table below shows the demand data for the first two years
Highline Financial services demand
From the two years, it is observed that the total demand for service C remains nearly constant over the two years. The total demand in the first year is 383 and in the second year is 387 which is a slight increase. Service A experience significant increase in demand. This service has a growing demand and represents a real potential for the company. On the other hand service, B experienced the tremendous drop in the demand falling from total of 340 in the first year to a total of 296 in the second year. Let us look at the forecasted values and compare with this data .
Forecasted demand for the services Moving Average Method
From forecasted values, it is seen that the demand for this service in the third year will remain relatively constant with a slight drop from the previous year. However, this demand is higher than the demand in the first year. Freddie should note that this is service has a potential to grow with the level of improvement in the demand. Freddie will need to put more resources and hiring is recommended to improve the service delivery. With improvement, the company will make more profits. It is evident that Freddie needs to do some hiring to ensure that the company meets the increasing demand. The company is seen to be experiencing an increase in demand; therefore, some workers need to be hired to ensure the organization meets the demand of service .
This is the service with the highest fluctuation in the company with a total of 340 in the first year. This value reduced to 296 in the second year while it is forecasted to increase to 306 in the third year. Freddie will need to investigate the cause of all these fluctuations as it may lead to losses if no measures are taken. Resources should be allocated for promotion activities to aid in marketing . The company ensures that the quality of this service is improved and guaranteed. Freddie also needs to investigate the role of the competing providers of this service to the demand of the company’s service. With these fluctuations and decreasing demand, it warrants laying off some workers, so Freddie has to reduce the number of employees to ensure that the company remains competitive. Lessening the number of workers will reduce operational cost
This service is in maturity stage as seen from the stability of its demand. It is the key service to the company as it experiences constant demand. Freddie needs to maintain all the resources to this service as no hiring or layoff is required. The available stuff should only be encouraged to retain the quality of services they are providing. Freddie will need to invest in the motivation techniques to ensure this service remains to sell. In this service, Freddie will not need to alter the number of employees. No laying off of workers or hiring is needed .
The forecast on the case study of the Highline Financial Services Limited enables us to appreciate the role of forecasting in operations management. With these data on the demand, Freddie Mack will be able to make a better decision on the delivery of the services. Forecasts information gives him the chance to plan for each service independently. Each service, in this case, experiences different demand profile, and a universal marketing strategy will not be applied. Given that each one is analyzed independently, proper mechanisms will be put in place. However, Freddie needs to ensure he monitor the situation in the third quarter and avoid on over relying on the forecast data on all decisions. It is important to note that forecasting in not the real truth of the situation but an insight.
Appel, G. (1998). The Moving Average Convergence-divergence Trading Method. New York : Traders Pr.
Carlberg, C. G. (2002). Business Analysis with Microsoft Excel. New York: Que Publishing.
Chaman L. Jain, J. M. (2004). Benchmarking Forecasting Practices: A Guide to Improving Forecasting Performance. New York: GraceWay publishing Company.
Kolli, S. (2000). Production & Operations Management Essentials. chicago: Research and Education Association, Piscataway.
Malehorn, C. L. (2005). Practical Guide to Business Forecasting. New York: GraceWay Publishing Company.
Rob J Hyndman, G. A. (2014). Forecasting: principles and practice. Carlifonia: OTexts.
Robert A. Yaffee, M. M. (2000). An Introduction to Time Series Analysis and Forecasting. New York: John Wiley and sons Publisher.
Shim, J. K. (2000). Strategic Business Forecasting: The Complete Guide to Forecasting Real World Company Performance. London: St. Lucie Press.
Stephen A. DeLurgio, C. B. (1991). Forecasting Systems for Operations Management. New Delhi: Business One Irwin.
Stevenson, W. (2011). Operations Management (11th ed.). New York: McGraw-Hill Education.
U. K. Srivastava, G. V. (2005). Quantitative Techniques for Managerial Decisions. New Delhi: New Age International (P) limited.
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