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Abstract:
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In case of novel products with short shelf life, sales data was either
unavailable or scarcely available. The available methods for the estimation of
demand of such products were direct survey methods, collection of opinion or
indirect survey methods, comparison with established products and limited
market trial. From literature review it was concluded that existing literature for
predicting the demand of novel and short life products were scarce. This led to
identification of problem namely demand forecast of relatively novel and short
life products. Initially conventional methods like naive, exponential smoothing
and moving average methods were used to predict the demand. Markov based
model was then applied to forecast errors of the conventional methods. This
model or algorithm requires only demand data of two consecutive months and
hence is suited for demand forecast of novel products.This algorithm was then applied to two novel baked products, one of
relatively large quantity and another of relatively small quantity. Naive,
exponential smoothing and moving average methods were applied to this data
and the forecasts as well as error for all the working days of two consecutive
months were estimated. Markov based algorithm was then applied for these
errors and the steady state probability was determined for each state of
demand. A state of a system is where the system was at a point of time. The
demand corresponding to the state with maximum probability was selected
and the corresponding profit was estimated. The obtained profits were then
compared and the combination with maximum profit was identified and the
method is validated by estimating the annual savings that this method will
bring to the firm when compared to existing methods in case of products A
and BThe suitability of the model was validated by the fact that its
implementation on product A and product B fetched more annual savings
when compared to existing practice. Return on investment increased for
product A and product B when compared to existing methods.
Thus it was concluded that a firm can further enhance its profit by
implementing this model or algorithm for more number of products. Further the
model can be generalized by applying it to more types of novel products with
short shelf life. The forecasting of novel and short life products was not much
explored in previous research works. This model can act as the benchmark for
future researches in forecasting of novel and short life products |