ANALYSIS OF PREDICTION REPAIRMENT STRATEGY AND LOSS MANAGEMENT OF RAW MATERIALS IN SUPPORTING THE APPLICATION OF SUPPLY CHAIN MANAGEMENT AT PT. HPPM - EFENDI HARAHAP (NIM : 29107358); Pembimbing: Prof., Ir. Togar M. Simatupang, M-Tech., Ph.D., RUSDIN
The industry of motorcycles in Indonesia is increasing each year and influences competition between existing Agen Tunggal Pemegang Merek (ATPM). An appropriate strategy will therefore keep a business survive and win this competition. With that consideration, PT. HPPM, as a producer of Primary Driven Gear (PDG) supplying parts to PT AHM, has discovered an obstacle in dealing with changing orders from PT AHM. This problem includes the way to plan the supply of raw materials sufficient for production as it fulfills the unstable demand from PT AHM. From the result of research, it is concluded that the supply availability is influenced by internal and external factors. Internal factors include the undetected loss of raw materials which results in inaccurate status of materials, and this loss tends to increase every year. Externally, there is a problem in inaccuracies in the raw materials planning which is influenced by a forecast error from PT AHM. This results in instability of raw materials, and therefore, leads to the inability of the supplier, PT HTI, to fulfill the demand within the due date or if there is an immediate demand, as there is no real collaboration in execution planning of the forecast. The negative impact will be seen in supply chain, such as bullwhip effect, ineffective supply management, and inefficient production planning. There are two ways to resolve this problem. First, collaborative forecasting strategy will be used to solve external problems, which involve every part in the supply chain. Also, information exchange is needed in order to support the success of the strategy application. Second, Process Failure Mode and Effect Analysis (PFMEA) will be used to solve internal problems. This is done to identify potential failure mode by looking at the highest Risk Priority Number (RPN) value which is analyzed by pareto diagram. Solutions and implementation plans involve peak management and appropriate socialization which hopefully will give a positive impact. This solution is chosen to increase prediction accuracy, repair supply management, and improve production planning.