Procurement Management Optimization Based on Life-Cycle-Cost Analysis for Telecom Companies

Abstract

Under the tide of convergence in telecommunications industry, almost every telecom company is considering transformation strategies. Besides, increasing demands for communication services have stimulated increasing deployment of ICT infrastructure in emerging market. As the situation becomes more urgent, telecom operators are required to invest more than ever to purchase large quantities of equipment. Since procurement costs account for more than 50 percent of overall costs of telecom companies, how to effectively reduce these costs is very important for their transformation strategies, and especially critical for continual development of telecom companies.The concept of Life-cycle-cost (LCC) was initially proposed by the U.S. Department of Defense, which refers to total costs involved in the entire life cycle of a product, mainly covering research, experimentation, procurement, maintenance, transport and storage. Procurement Life-cycle-cost (PLCC) is the core concept of life cycle management of procurement, which implies that the procurement activity determines not only the current costs, but also latter costs related to the product. Comparing with traditional approaches of procurement cost management, during which companies only pay attention to the original purchase price and lose control of the quality of the product, PLCC extends cost consideration to the entire life cycle of the product, which includes maintenance costs, usage costs, fault losses, etc. It provides an efficient solution to quality pre-controlling and cost reduction (including CAPEX and OPEX) for companies, and will have a direct impact on the value of business, quality and competitive status.Based on Life-cycle-cost theory, this paper focuses on procurement management of a domestic mobile telecom company. Through an empirical analysis of PLCC of 25 types of equipment in a mobile telecom company, we firstly identify LCC-sensitive factors for each type of the equipment, then calculate the degree of sensitivity, and try to find out key points of cost-control for future procurement activity. Furthermore, in order to make procurement management more efficient and orderly, we adopt the cluster analysis function of SPSS to classify all the equipment into eight categories, and bring forward corresponding procurement optimization strategies, which could be an useful reference for setting and controlling procurement budget and making right purchase decisions for the company. We believe that the method and optimization strategies would be valuable for other telecom companies and similar capital-intensive enterprises.