Human Aspect on Chain of Custody (CoC) System Performance

The tropical forests cover 24% of tropical land area. They are the most productive terrestrial ecosystems on earth with high priorities for biodiversity conservation. These forests store a substantial amount of carbon in biomass and soil, and they also regulate the transfer of carbon into the atmosphere as carbon dioxide (CO2). Indonesia is having the third tropical forest area in the world after Brazil and Congo. Over 50 years forest has been felled both legally as well as illegally. High rate of forest degradation resulted from unsustainable forest management, rampant illegal logging, forest area encroachment, conversion and natural disaster. All urges rapid improvement of management system of Indonesia’s forest resources (Holmes, 2002). Forest certification is one tool that can support the achievement of sustainable forest management goal. Under current operation of join certification protocol between the Forest Stewardship Council (FSC) and the Indonesian Ecolabelling Institute (LEI) in Indonesia, forest management units must be able to show the required performance indicated in LEI criteria and indicator as well as FSC principles and criteria to attain certification of their products. The gap between current practices and performance required by forest certifications schemes is still enormous. The performance of forest certification system from LEI is determined very much by the human that is involved in the process of planning and operation. The name of certification system is chain of custody (CoC) certification. CoC operation involves activities such as tracing raw material from the forest to the factory, through shipping and manufacturing, to the final end product. In all of the above processes, the roles of human are critical, although the specific roles played from one process to another are different. In this paper we present an identification of human aspect and other factors that predominantly affect CoC system performance

Creating an Electro Galvanized Steel Price Index for Jiangsu Aucksun Metal Co., Ltd.

The goal of this project was to establish a price index model for Aucksun\u27s electro galvanized steel products so that the top management would be able to accurately account for the salesmen\u27s contributions to Aucksun\u27s profit. By indentifying and analyzing various key factors affecting the supply and demand of the electro galvanized steel, we were able to create a price index model using multiple linear regression analysis. Additionally, we suggested detailed procedures for implementing and improving this price index model

Dynamic small-series fashion order allocation and supplier selection: a ga-topsis-based model

The fashion industry is currently confronted with significant economic and environmental challenges, necessitating the exploration of novel business models. Among the promising approaches is small series production on demand, though this poses considerable complexities in the highly competitive sector. Traditional supplier selection and production planning processes, known for their lengthy and intricate nature, must be replaced with more dynamic and effective decision-making procedures. To tackle this problem, GA-TOPSIS hybrid model is proposed as the methodology. The model integrates Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) evaluation into the fitness function of Genetic Algorithm (GA) to comprehensively consider both qualitative and quantitative criteria for supplier selection. Simultaneously, GA efficiently optimizes the order sequence for production planning. The model's efficacy is demonstrated through implementation on real orders, showcasing its ability to handle diverse evaluation criteria and support supplier selection in different scenarios. Moreover, the proposed model is employed to compute the Pareto front, which provides optimal sets of solutions for the given objective criteria. This allows for an effective demand-driven strategy, particularly relevant for fashion retailers to select supplier and order planning optimization decisions in dynamic and multi-criteria context. Overall, GA-TOPSIS hybrid model offers an innovative and efficient decision support system for fashion retailers to adapt to changing demands and achieve effective supplier selection and production planning optimization. The model's incorporation of both qualitative and quantitative criteria in a dynamic environment contributes to its originality and potential for addressing the complexities of the fashion industry's supply chain challenge

Research in Supply Chain Management: Issue and Area Development

Today the study of supply chain management (SCM) is growing rapidly and provides a great opportunity to do research both empirical and theoretical development. Research opportunities in SCM has been reviewed by many researchers and grouped into many categories. This paper contains a review of research SCM and classify into 7 categories, namely (1) SCM Operational Management & Strategy, (2) knowledge management, (3) Relationship Management, (4) Information Technology in SCM, (5) Supply Chain Design, Logistics & Infrastructure, (6) Global Issues, (7) Environment, Legal & Regulations. The issue in each category and research opportunities will be discussed in this paper. Keywords: Supply Chain Management, Research Opportunities in SCM, Issue in SC

A Study of the Strategic Alliance for EMS Industry: The Application of a Hybrid DEA and GM (1, 1) Approach

Choosing a partner is a critical factor for success in international strategic alliances, although criteria for partner selection vary between developed and transitional markets. This study aims to develop effective methods to assist enterprise to measure the firms’ operation efficiency, find out the candidate priority under several different inputs and outputs, and forecast the values of those variables in the future. The methodologies are constructed by the concepts of Data Envelopment Analysis (DEA) and grey model (GM). Realistic data in four consecutive years (2009–2012) a total of 20 companies of the Electronic Manufacturing Service (EMS) industry that went public are completely collected. This paper tries to help target company—DMU1—to find the right alliance partners. By our proposed approach, the results show the priority in the recent years. The research study is hopefully of interest to managers who are in manufacturing industry in general and EMS enterprises in particular

A fuzzy decision-making approach for evaluation and selection of third party reverse logistics provider using fuzzy ARAS

Business environment is full of ups and down and this makes companies to develop different ways of using resources. By expanding life cycle of products, these ways can be cost effective and not harmful for environment. As Reverse Logistics (RL) uses a product after end of its life, it reduces pollution, therefore it has been considered as a part of sustainable development. The core goal of current research is developing a framework by which it evaluates Third Party RL Provider (3rdPRLP) using Multi-Criteria Decision-Making (MCDM) based on Fuzzy Additive Ratio ASsessment (FARAS). Thirty-seven criteria were identified, which are classified into seven main criteria. The main criteria were ranked as follows: product lifecycle position C1, RL process function C2, organizational performance C3, organizational role of RL C4, IT system and communication C5, general company consideration C6, geographical location C7. Market coverage, destination, financial considerations, integrated system, reclaim, efficiency and quality, and growth are each group’s dominant sub-criteria. In addition, the current research helps the logistics managers to better understand the key attributes’ complex relationships in the environment of decision-making. First published online 21 January 202