Diagnosing Feed Concentrate Supply Chain Performance in an Agribusiness Cooperative via "SCOR-AHP"

Abstract

This study aims to measure the supply chain performance of the Animal Feed Concentrate (MAKO) business unit at KUD Sarwa Mukti Cisarua, particularly for the F1 product, which faces recurring problems of production-sales mismatch, inventory fluctuation, delivery delay, and demand forecasting inaccuracy. A descriptive quantitative approach with a case study design was employed, integrating the Supply Chain Operation Reference (SCOR) model, Snorm De Boer normalization, and the Analytical Hierarchy Process (AHP). Data were collected through observation, interviews, AHP pairwise comparison questionnaires, and company documentation for the 2024 period. Fifteen performance metrics were mapped across the five core SCOR processes (plan, source, make, deliver, return), categorized as Larger is Better or Lower is Better, normalized to a 0–100 scale, and weighted through pairwise comparisons with a consistency ratio (CR) of no more than 0.10. The results show an overall supply chain performance score of 64.36, classified as Average. The Make process recorded the lowest attribute score (56.94), while three indicators fell into the red category of the traffic light system, namely Production Plan Adjustment Time, Production Volume Accuracy, and Production Cost per Ton. These findings indicate that the company's supply chain performance has not yet been optimal, particularly in production planning and control, so improvement priorities are recommended through a rolling forecast mechanism, periodic evaluation of the production plan based on actual demand data, and more efficient production scheduling. This study contributes to the limited body of SCOR-AHP research on agribusiness cooperatives by simultaneously integrating SCOR, AHP, and Snorm De Boer normalization for a feed-concentrate production unit, an operational context that has received far less attention than plantation- or manufacturing-based supply chains. Practically, the findings provide cooperative managers with a concrete, low-cost diagnostic tool for prioritizing production-planning interventions without requiring sophisticated information-technology infrastructure.