The objectives of this research were to determine the work posture risk level and suggest recommendation of improvement on small factory of corn chips. NBM questionnaire was used to find out workers’ complaints encountered from the work they have done. OWAS method was conducted with WinOWAS software, involving six workers and 31 work postures. NBM indicates that workers do enduring the feeling of sore on their neck, back, hand, and feet during and after work. 42%, 6%, 26%, and 26% of activities were at category 1, category 2, category 3, and category 4 respectively

The purposes of this study are to explain the conditions of supply chain for organic rice product and to determine the supply chain risk order organic rice products in MUTOS Seloliman. Risk measurement is performed using fuzzy FMEA method then the risk priority level is obtained. The supply chain structure of organic rice product consist of farmers as suppliers, MUTOS as manufacturer, PT Herbal Estate, PPLH Surabaya, Kaliandra, and CV Mandalabimasakti SM as distributors, and Ranch Market Galaxy Mall as retailer and consumer. The distribution network of organic rice product is retail storage with customer pickup network. The priority risk order in this supply chain based on research from the highest risk to the lowest risk are risk of product return, risk of damage or loss quality, risk of product contamination during process, risk of lack of stock, risk of competitor existence, quality incompability risk, risk of contain chemical contaminants, risk of supply delays, risk of processing delays, risk of damage during process, risk of machine damage during process, risk of demand changing, risk of damaged during storage, and risk of production decreased

Purpose: The purpose of this paper is to propose an instrument to evaluate the collaboration quality of sustainable supply chains by considering collaboration behaviour and sustainability factors.Design/methodology/approach: The proposed model integrates Quality Function Deployment (QFD), Fuzzy Analytical Network Process (FANP) and Data Envelopment Analysis (DEA) to assess the efficiency score of each stakeholder involved. A case of a sugar company in Indonesia was analysed as the illustration of the application of the model.Findings: The integrated QFD-FANP-DEA method enables to incorporate collaboration behaviour and sustainability factors in a single assessment model. The results of the case study showed the benchmarking results associated with the performance variance of stakeholders and the number of samples.Research limitations/implications: Future research could consider other DEA approaches, extend the research by simulating a different number of scenarios and evaluating the improvement/deterioration, and expand the scope of collaboration.Practical implications: Each stakeholder will be able to obtain the collaboration assessment results, and the improvements suggested by the model.Social implications: Smallholders and farmers will be able to increase their benefits by using the existing resources.Originality/value: This paper provides a methodological contribution by introducing new collaboration quality assessment methods that can accommodate any variance in multi-stakeholders, involve qualitative and quantitative measurements, and benefit all stakeholders.

The purpose of this study is to provide information on the conditions of horticultural products supply chain that is managed by the Cooperative Brenjonk and identifying performance problems that occur from the beginning of the process of organic products to be a viable product to be marketed. This study uses the identification of supply chain performance to determine the performance of each supply chain actors. Identification used is the identification of supply chain performance in phase 1 and 2, where the first stage of the planning elements are identified, supply, process, delivery and returns. In phase 2 conducted in-depth identification of the identification performance on stage 1. Respondents experts used are the primary members of the supply chain of horticultural products. Performed on the identification results obtained in phase 1 that each actor has a horticulture supply chain constraints on the 5 elements namely supplier with 12 obstacles, with 14 manufacturing constraints, distributors and retailers with a 3 constraint with 4 constraints. Constraints are most numerous in the manufacturing and the most dominant elements contained in the make. The results of the first stage followed by analysis of the phase 2. Stage 2 found elements that affect the source-stocked-product (S1), make-to-order (M2), delivering on-stocked product (D1) and return defective product (SR1) and deliver excess return product (DR3)