A Literature Review on The Design of Intelligent Supply Chain for Natural Fibre Agroindustry

Natural fibre is an environmentally friendly raw material that has a great potential to develop, and is abundantly available in nature [1]. Currently, the growth of natural fibre processing industries in the world has been increasingly important [2]. Processing of abundant natural fibre in both upstream and downstream productions requires effective and collaborative supply chain management in terms of information sharing. Thus, an intelligent system would be implemented in supply chain management from upstream to downstream. Based on review of 46 scientific papers discussing on types of natural fibre, process, technology, and methods, as well as application areas of natural fibre in downstream industries. According to review on different aspects in 55 scientific papers, there were 5 aspects mapped, i.e. supply chain analytic, value chain, performance, collaboration, big data, and decision support system. A concept of 4.0 industry underlies utilization of opportunities for application of supply chain analytic [3]. Upcoming research opportunities include mediating relationship in supply chain network by utilizing Internet of things (IoT) and Big data (BD), in a collaborative relationship to use information sharing. The most possibly contributing research is the development of collaboration between supply chain and genetic algorithm [4]. Integration between production and inventory planning becomes an approach that utilizes Particle swarm optimization (PSO) by developing production planning [5], and production and inventory planning [6]. There is a research opportunity in the design of intelligent supply chain for natural fibre agroindustry by implementing IoT and BD as a tool in supply chain analytic, collaboration through Collaboration prediction forecasting and replenishment (CPFR) that occurs between stakeholders with the aim of improving agroindustry supply chain performance in production integration material and inventory, and performance measurement by integrating the Value chain operation reference (VCOR) model developed in supply chain analytic

Item-level RFID for enhancement of customer shopping experience in apparel retail

In the customer-oriented apparel retail industry, providing satisfactory shopping experience for customers is a vital differentiator. However, traditional stores generally cannot fully satisfy customer needs because of difficulties in locating target products, out-of-stocks, a lack of professional assistance for product selection, and long waiting for payments. Therefore, this paper proposes an item-level RFID-enabled retail store management system for relatively high-end apparel products to provide customers with more leisure, interaction for product information, and automatic apparel collocation to promote sales during shopping. In this system, RFID hardware devices are installed to capture customer shopping behaviour and preferences, which would be especially useful for business decision-making and proactive individual marketing to enhance retail business. Intelligent fuzzy screening algorithms are then developed to promote apparel collocation based on the customer preferences, the design features of products, and the sales history accumulated in the database. It is expected that the proposed system, when fully implemented, can help promote retail business by enriching customers with intelligent and personalized services, and thus enhance the overall shopping experience. © 2015 Elsevier B.V.postprin

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

Neuro-fuzzy inference systems approach to decision support system for economic order quantity

Supply chain management (SCM) has a dynamic structure involving the constant flow of information, product, and funds among different participants. SCM is a complex process and most often characterized by uncertainty. Many values are stochastic and cannot be precisely determined and described by classical mathematical methods. Therefore, in solving real and complex problems individual methods of artificial intelligence are increasingly used, or their combination in the form of hybrid methods. This paper has proposed the decision support system for determining economic order quantity and order implementation based on Adaptive neuro-fuzzy inference systems - ANFIS. A combination of two concepts of artificial intelligence in the form of hybrid neuro-fuzzy method has been applied into the decision support system in order to exploit the individual advantages of both methods. This method can deal with complexity and uncertainty in SCM better than classical methods because they it stems from experts’ opinions. The proposed decision support system showed good results for determining the amount of economic order and it is presented as a successful tool for planning in SCM. Sensitivity analysis has been applied, which indicates that the decision sup- port system gives valid results. The proposed system is flexible and can be applied to various types of goods in SC

Supply chain inventory control for the iron and steel industry

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A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

A Review of Supply Chain Data Mining Publications

The use of data mining in supply chains is growing, and covers almost all aspects of supply chain management. A framework of supply chain analytics is used to classify data mining publications reported in supply chain management academic literature. Scholarly articles were identified using SCOPUS and EBSCO Business search engines. Articles were classified by supply chain function. Additional papers reflecting technology, to include RFID use and text analysis were separately reviewed. The paper concludes with discussion of potential research issues and outlook for future development

“Domain Of Supply Chain Management - A State Of Art”.

A supply chain is a network of facilities and distribution channels that encompasses the procurement of materials, production, assembly and delivery of product or service to the customer. The management of the supply chain and the roles of various actors involved differ from industry to industry and company to company. As a result Supply Chain Management (SCM) has become a vital issue for manufacturers, professionals and researchers. It is felt that to manage the supply chain effectively entire structure of supply chain must be understood properly. This paper attempts to provide the reader a complete picture of supply chain management through a systematic literature review. It presents a state of art on SCM by systematically arranging main activities in supply chain. In addition the step-by-step approach for understanding the breadth and depth of Supply Chain is proposed which consequently explores the domain of SCM.A supply chain is a network of facilities and distribution channels that encompasses the procurement of materials, production, assembly and delivery of product or service to the customer. The management of the supply chain and the roles of various actors involved differ from industry to industry and company to company. As a result Supply Chain Management (SCM) has become a vital issue for manufacturers, professionals and researchers. It is felt that to manage the supply chain effectively entire structure of supply chain must be understood properly. This paper attempts to provide the reader a complete picture of supply chain management through a systematic literature review. It presents a state of art on SCM by systematically arranging main activities in supply chain. In addition the step-by-step approach for understanding the breadth and depth of Supply Chain is proposed which consequently explores the domain of SCM.A supply chain is a network of facilities and distribution channels that encompasses the procurement of materials, production, assembly and delivery of product or service to the customer. The management of the supply chain and the roles of various actors involved differ from industry to industry and company to company. As a result Supply Chain Management (SCM) has become a vital issue for manufacturers, professionals and researchers. It is felt that to manage the supply chain effectively entire structure of supply chain must be understood properly. This paper attempts to provide the reader a complete picture of supply chain management through a systematic literature review. It presents a state of art on SCM by systematically arranging main activities in supply chain. In addition the step-by-step approach for understanding the breadth and depth of Supply Chain is proposed which consequently explores the domain of SCM