Determination of Performance Indicators For Cooking Oil Logistic Using Fuzzy-Delphi Method

Abstract

Logistic issues are still become a serious matter for Indonesian society. This problem should be seriously considered by Indonesia. Many sectors that support logistic growth should be arranged properly in order to increase the value of performance of logistic in Indonesia. Agriculture sector is one of the sectors that has important role in national development integration. Unfortunately, production growth for this sector is still relatively low compared to some other sectors with value 1.85%. One of the agricultural sectors in Indonesia was palm oil plantations that produces cooking oil. Currently, Indonesia was suspected by having problems on the cooking oil distribution. Cooking oil distribution margins are tending to increase, while margin is one of the efficiency indicators in distribution system. The increasing of distribution margin indicates that the distribution of the commodity is inefficient. Thus, we need to improve efficiency and effectiveness in all logistics activities. The issue is, what are the relevant indicators to manage the logistics of cooking oil in an effort to improve the efficiency and effectiveness. Determination of performance indicators can be done by getting the opinion and consensus of the experts who understand the logistics of cooking oil. The method used is the Delphi method that will integrated with fussy method. The method is called Fuzzy-Delphi method.

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“…Fuzzy-Delphi has the capacity to capture inherent vagueness within data. Many researchers use this method across a wide range of subject areas; these include business resilience and sustainability (Kumar and Anbanandam, 2019; Torres Vergara et al ., 2023); Demand forecasting (Khan et al ., 2023a, b; Zhu et al ., 2023); inventory management (Khan et al ., 2023a, b; Saputra, 2023); supplier selection (Ambilkar et al ., 2023; Ebrahimi et al ., 2023); risk management (Jahanvand et al ., 2023; Tuni et al ., 2023); production planning and quality control (Rana et al ., 2023); supply chain optimization (Khan et al ., 2023a, b; Tseng et al ., 2022); new product introduction (Lianto, 2023); sustainability and environmental impact (Mukherjee and Pradeep, 2023) and resource allocation (Shao et al ., 2023). Fuzzy logic allows for the representation of uncertainty and vagueness in the data (Lin et al ., 2018; Zhang et al ., 2023).…”