Transportation Cost Improvement for an Electric Appliance Manufacturer

Abstract

Nowadays, Thailand has developed itself to become a more industrial country. There are many companies who decide to invest and establish their manufacturers here and distribute their products to customers domestically and internationally. Transportation discipline plays an important role in many organisations as 30% of logistics cost goes to transportation cost. Thus, this research aims to improve the studied company’s transportation management which also results in transportation cost improvement. This research is a study and development of a heuristic which helps manage the transportation discipline using online orders, of an electrical appliance manufacturer in Thailand. It focuses on delivering products from the company's main warehouse to customers which are located in other provinces around Samutprakarn. This research has developed 2 systems which are customer clustering system and route designing system, with an objective to improve transportation cost, as well as improve vehicle utilisation and delivery performance. The customer clustering system is designed to reduce the complexity when designing a delivering route. The system will cluster the customers which have similar characteristic together into the same group, for example; group of customers which are located in the same province. This group of customer will be used in route designing system in order to select a suitable vehicle and create a travelling route plan which conform to the transportation conditions and regulations. In order to achieve the objective, the proposed systems applied the First-Fit Algorithm and the Nearest Neighbourhood Algorithm respectively. The researcher has experimented and analysed the abilities of the proposed systems by comparing the results of the experiment and the company’s actual recorded data of 7 operating days. There are 1,004 order lines in total. As a result, the proposed systems are able to improve 14.80% of vehicle utilisation and reduce any delayed delivery. Therefore, it leads to 8.24% reduction of overall transportation cost. However, these proposed systems only provide initial solutions which need to be adjusted when implementing to a company.