Ürün ikamesi ve üretim karmaşıklığı maliyeti gözeten ürün gamı planlanması

While increasing product variety may have a positive effect on market share, it may lead to difficulties in product management and forecasting, increased inventory holding costs and setup times. We propose two methods to help a tire manufacturer manage their assortment to find a balance between variety and sales. The first method evaluates the marginal complexity cost of new products so that a data-driven decision can be made while introducing new products. The second method determines the set of tires to be included in the assortment that increases the total profit. We estimate the fractions of demands substituted by the products left in the assortment. Customer no-purchase information, exact timings of stock-outs and sales up to those times were unobserved. Market share information for a particular group of tires for the company were unavailable. Based on these incomplete data, the substitution probabilities were estimated using an iterative method. In order to estimate the break time savings, a machine learning model was used. The additional profit due to potential new sales and the conversion cost saved due to discontinued products were added to the profit function. A large-scale assortment optimization problem with a complex objective function was formulated. The results found using a genetic algorithm show that the new assortment has between 3.8% and 15.2% less products than the initial assortment with additional profits between 0.6% and 4.6% of the company's annual income. The results show that considering complexity costs in assortment decisions leads to substantially different assortments and additional savings.