Optimization of a demand uncertainty model considering consumers valuation and price sensitivity

Abstract

In today’s ever changing markets, maintaining an efficient and flexible supply chain is critical for every enterprise, especially given the prevailing volatilities in the business environment with constantly shifting and increasing consumer expectations. One of the key sources of uncertainty in any production-distribution system is the product demand. Failure to account for significant demand fluctuations could either lead to unsatisfied consumer demand translating to loss of market share or excessively high inventory holding costs. The traditional demand models are concerned with only improving forecast accuracy rather assessing uncertainty. Uncertainty concern can help manage the risk associated with alternative plans. In this thesis a demand model is developed considering the combined effect of price sensitivity and consumers’ valuation or satisfaction as a source of uncertainty. The uncertainty model is developed considering consumers’ valuation, price, price sensitivity, the market size, wholesale price and quantity ordered by the retailer on profit maximization. The retailer price and the order quantity that the retailer places with the manufacturer are the decision variables and total profit is the objective function which is to be maximized. Two meta-heuristics Genetic Algorithm and Sequential Quadratic Programming are used to solve the non linear constrained form of objective function as they can generate accurate result with a shorter computational time. Some numerical examples have been presented to explain the model. The results obtained from these algorithms and the results of the existing forecasting model of a renowned company were compared with actual sales data and the algorithm results were found satisfactory.