Perencanaan Produksi UMKM dengan Menggunakan Pendekatan Metode Transportasi

Abstract

Micro, Small and Medium Enterprises (MSMEs) are an important part of the Indonesian economy. Many micro, small and medium enterprises (MSMEs) are making new innovations in the fields of handicrafts, food and drinks, among others. The development of MSMEs faces demands that change every month. To meet this demand, they must carry out optimal production planning. Production planning and control can be used as a reference for determining production quantities and forecasting product demand. By carrying out good production planning and control, MSMEs can reduce or anticipate risks, thereby increasing their profits. Coconut jelly MSMEs are one of the businesses that are also affected by poor production planning. On average, in 2023 there will be an excess production of 150 pieces per month. For small and medium businesses (MSMEs), fluctuations of this magnitude are very disruptive to company finances. Effective production planning should be able to optimize the use of all organizational resources, including raw materials, materials, labor and finance. This article carries out production planning using a transportation method approach. Initial planning is carried out following a pattern of forecasting the number of requests in the future. Forecasting is carried out using 3 methods, namely single moving average, double exponential smoothing and trend analysis. Of these three methods, the one with the smallest Mean Square Error (MSE) value will be selected. Production planning will be outlined in the Master Production Schedule (JIP). Determination of JIP is based on the results of production planning through 2 alternatives, namely permanent and changing workforce. From the results of the transportation method approach, it was found that the minimum production cost was found in the variable labor alternative, namely IDR 178,326,000, there was a cost difference from the fixed labor alternative of IDR 1,451,200. The approach method used in this article still needs a lot of improvement, especially in terms of determining the cost of each production component which can be more dynamic. The approach that can be taken can use heuristic methods such as genetic algorithms. So, it is hoped that the results obtained can be closer to reality.