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The IUP Journal of Operations Management :
Market Demand Forecast Method Selection and Application: A Case Study in Hero MotoCorp Ltd.
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In this dynamic era, the market is very competitive and the demand for a product fluctuates every now and then. Under these circumstances, it is quite essential to correctly estimate the market demand to survive the competition. If the estimated demand becomes less, the company loses the business but if the estimated demand is high, the company blocks the working capital till such time the excess production is sold in the market. In view of this, an attempt has been made to explore market demand forecast method selection criteria and apply them in practice to find out which one is most suitable for forecasting the demand for products of the industry in the market. The theoretical part of this study starts with the forecast method classification done by different researchers. The analysis of the different forecast methods reveals that it is rather difficult to determine the advantage of any method in forecast estimation as there always remains the risk of wrong method selection. Each forecast method has its advantages and disadvantages under different situations. Therefore, the analysis and differentiation of the main forecast method selection criteria is expedient. The literature review suggests that the selection of the forecast method should be based on several criteria such as forecast accuracy level, forecast time span, the scope of initial data, and the level of result appropriateness. This study was carried out in Hero MotoCorp Ltd. for the selection of a forecast method most suitable for the company for forecasting the motorcycle demand based on the actual demand experienced by the company for the period April 2016 to March 2017. The comparison of the forecast accuracy assessment indicators that were estimated by using different forecast methods indicates that the lowest motorcycle sales forecast error values were achieved by applying exponential smoothing method where smoothing constant, a = 0.3.

 
 

Demand forecasting is an important issue for manufacturing companies. Several decision-making processes need accurate forecast in order to choose proper actions relevant to production planning, sales budgeting, new product launches, etc. For this reason, over the years, practitioners have devoted particular attention as to how forecasting can be improved to increase forecast accuracy. The adoption of structured forecasting techniques has been studied by several authors (Mentzer and Cox, 1984; Dalrymple, 1987; Sanders and Ritzman, 2001; and Sanders and Manrodt, 2003). The plethora of studies on forecasting reveals the use of quantitative and qualitative approaches mentioned by different researchers. The debate is still open on whether the adoption of structured forecasting techniques is always beneficial in improving forecast accuracy. In particular, several authors have challenged the assumption that the greater the adoption of complex forecasting techniques, the better the forecast accuracy. For instance, many authors attempted to demonstrate that the efficacy of forecasting techniques in improving forecast accuracy depends on the fit between the type of technique adopted and the context (Wright et al., 1996; Makridakis et al., 1998; and Sanders and Manrodt, 2003). Moreover, several researchers suggested that forecasting technique adoption is not enough to guarantee good forecast accuracy (Armstrong, 1987; Mentzer and Bienstock, 1998; and Moon et al., 2003).

 
 

Operations Management Journal,About Hero MotoCorp Ltd,Market Demand Forecast Method Selection,Concept and Types of Forecast Methods.