Determining the best price with linear performance pricing and checking with fuzzy logic

Abstract

One of the reasons behind the success in the business world is the optimal pricing for products and parts. As a matter of fact, it is known that the best price has a very strong effect on income, profitability and growth factors of businesses. Businesses aim to define the best price for the products or the parts taking the quality, performance, and cost triangle into consideration. Knowing the supplier's price and lead time is strategically important for competitive advantage in enterprises due to its cost-reducing effect. Defining a price for the buyers based on the performance of the product can sometimes be a rather complicated and time-consuming process. Procurement cost is a Key Performance Indicator (KPI) that is vital to supply chain management. The purpose of procurement savings is to reduce procurement costs, improve supplier conditions and reduce product prices. This article focuses on material procurement (supply) cost using regression-based linear performance pricing (LPP), a tool developed for pricing processes to reduce the unit cost of parts in a large automotive original equipment manufacturer (OEM). Although the method is widely used in the automotive industry in the US and Europe, there is a gap in the literature due to the lack of discussion about the applicability of the LPP method. In this context, it is aimed to contribute to the literature with a detailed example to popularize and disseminate the use of the LPP technique in purchasing and pricing processes. However, it was also aimed to show that pricing problems can be addressed with intelligent approaches as an alternative to classical mathematical models in these processes. Since the data in the study are suitable for the fuzzy logic method, the accuracy of the savings obtained from LPP, in the problem was checked with Fuzzy Logic.