An Intelligent Approach to Prioritize Logistics Requirements in Food Industry

The aim of this study is to analyze dairy food industry and to determine the priorities of important logistics requirements (LR) based on customer requirements as a part of a supply management system. For product or service development, quality function deployment (QFD) is a useful approach to maximize customer satisfaction. The determination of the priorities of the LR is an important issue during QFD process for product or service design. For this reason, in this work, an integrated approach integrating fuzzy logic and QFD methods is proposed to identify and prioritize the LR in dairy food industry for the improvement of customer satisfaction. In addition, a case study in Turkish dairy food industry is given to illustrate the proposed approach for potential readers

CAD Software Evaluation for Product Design to Exchange Data in a Supply Chain Network

The sharing information in a supply chain environment, especially CAD models and drawings are so important companies. So, the selection of the most satisfying computer-aided design (CAD) software which enables to exchange data through supply chain network has been major issues for companies in a supply chain. The selection process of CAD software among the raising number of alternatives in the market has been very vital and critical issue for companies that aim to make their design and engineering related activities automated towards computer integrated manufacturing (CIM) environment. Therefore, most companies have used various methods to successfully carry out this difficult and time-consuming process. Of these methods, Analytic Hierarchy Process (AHP) has been widely used for Multiple Criteria Decision Making (MCDM) problems in both academic researches and practices. But, in some cases, due to the vagueness and uncertainty on judgments of the decision-maker(s), the crisp pair wise comparison in the conventional AHP seems to be insufficient and imprecise to capture the right judgments of decision-maker(s). Therefore, a fuzzy logic is introduced in the pair wise comparison of AHP to make up for this deficiency in the conventional AHP, called as fuzzy AHP. In this paper, a fuzzy AHP-based approach is proposed to evaluate a set of CAD software alternatives in the market to reach the best satisfying one based on the needs of company

Supplier selection with Shannon entropy and fuzzy TOPSIS in the context of supply chain risk management

Supplier selection is the process of finding the right suppliers, at the right price, at the right time, in the right quantities, and with the right quality. The aim of this paper, is supplier selection in the context of supply chain risk management. Thus nine criteria of quality, on time delivery and performance history and six risks in the supply chain including supply risk, demand risk, manufacturing risk, logistics risk, information risk and environmental risk considered for evaluating suppliers. Shannon entropy is used for weighing criteria and fuzzy TOPSIS is applied for ranking suppliers. Findings show that, in the spare parts supplier selection problem, demand risk is the most important factor

Supply Chain Risk Management of Liquefied Natural Gas (LNG) in Australia

This research examines the supply chain risk management of Australia’s Liquefied Natural Gas (LNG) supply chain. The study develops a risk management methodology based on quality function deployment and 0-1 multiobjective optimization model. The research reveals 33 LNG supply chain risks and 30 risk management strategies (RMSs) for Australian LNG supply chain. Optimal sets of RMSs are found using the methodology which would be beneficial for the LNG risk managers in a limited resources scenario

Shipbuilding 4.0 Index Approaching Supply Chain

The shipbuilding industry shows a special interest in adapting to the changes proposed by the industry 4.0. This article bets on the development of an index that indicates the current situation considering that supply chain is a key factor in any type of change, and at the same time it serves as a control tool in the implementation of improvements. The proposed indices provide a first definition of the paradigm or paradigms that best fit the supply chain in order to improve its sustainability and a second definition, regarding the key enabling technologies for Industry 4.0. The values obtained put shipbuilding on the road to industry 4.0 while suggesting categorized planning of technologies

Supplier Selection And Supplier Performance Evaluation At PT. Indolakto

Dairy supply chain is one of food supply chain that has its own uncertainty both in upstream and downstream process due to the durability of product. Dairy market has good demand trend, because the supply is still below the consumption level. Indonesia use imported dairy product rather than use the domestic ones, because the supply of domestic dairy still below the demand. So, there are opportunities for dairy company to compete in this industry and reach competitive advantage by solving the upstream problems. Selecting supplier is one of upstream supply chain area which affected the quality of dairy product and mitigate supply chain risk management from the beginning. This research aim to develop a framework for supplier selection and improve a supplier performance evaluation form. According to AHP method this research will be determine main criteria by interview, pair wise comparison on developing the AHP, determine sub criteria based on main criteria, and rank the supplier. After selecting the supplier, this research conduct interview for determining main and sub criteria, developing the AHP method with pair wise comparison and forming supplier performance evaluation. The result is forming a framework of supplier selection and forming supplier performance evaluation form based on company requirements. Also, the main criteria for supplier selection are quality, quantity, delivery, warranty, and pricing with sub main criteria which already deployed. For supplier performance evaluation, there are four main criteria which are quality, quantity, delivery and warranty. Maltodextrin A will be choose rather than Maltodextrin B. The sensivity analysis also shown that all of criteria were robust

An Integrated Collaboration Framework for Sustainable Sugar Supply Chains

Stakeholders in sugar supply chains face challenges to identify strengths and weaknesses, as well as opportunities and capabilities of all collaborating stakeholders. This paper proposes a conceptual model as a framework for sustainable sugar supply chains to achieve an effective collaboration system. This model identifies essential elements and how they are linked in the boundary of sustainability. An existing general collaboration model is selected as the basis to develop a new supply chain collaboration model based on the characteristics, the synergies, and the elements. The framework consisting of six elements: boundary and context; drivers (value proposition, assets, and supply chain capabilities); stakeholder requirements; collaboration system requirements; quality indicators; and a common goal. This conceptual model gives insight to the sugar stakeholders who join in collaboration regarding what are the specific drivers and elements that possible and essential to propose a sustainable collaboration, what their roles are, and how they are linked. The finding can be used as the basis for a collaboration quality assessment model

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

Analysis of Decision Support Systems of Industrial Relevance: Application Potential of Fuzzy and Grey Set Theories

The present work articulates few case empirical studies on decision making in industrial context. Development of variety of Decision Support System (DSS) under uncertainty and vague information is attempted herein. The study emphases on five important decision making domains where effective decision making may surely enhance overall performance of the organization. The focused territories of this work are i) robot selection, ii) g-resilient supplier selection, iii) third party logistics (3PL) service provider selection, iv) assessment of supply chain’s g-resilient index and v) risk assessment in e-commerce exercises. Firstly, decision support systems in relation to robot selection are conceptualized through adaptation to fuzzy set theory in integration with TODIM and PROMETHEE approach, Grey set theory is also found useful in this regard; and is combined with TODIM approach to identify the best robot alternative. In this work, an attempt is also made to tackle subjective (qualitative) and objective (quantitative) evaluation information simultaneously, towards effective decision making. Supplier selection is a key strategic concern for the large-scale organizations. In view of this, a novel decision support framework is proposed to address g-resilient (green and resilient) supplier selection issues. Green capability of suppliers’ ensures the pollution free operation; while, resiliency deals with unexpected system disruptions. A comparative analysis of the results is also carried out by applying well-known decision making approaches like Fuzzy- TOPSIS and Fuzzy-VIKOR. In relation to 3PL service provider selection, this dissertation proposes a novel ‘Dominance- Based’ model in combination with grey set theory to deal with 3PL provider selection, considering linguistic preferences of the Decision-Makers (DMs). An empirical case study is articulated to demonstrate application potential of the proposed model. The results, obtained thereof, have been compared to that of grey-TOPSIS approach. Another part of this dissertation is to provide an integrated framework in order to assess gresilient (ecosilient) performance of the supply chain of a case automotive company. The overall g-resilient supply chain performance is determined by computing a unique ecosilient (g-resilient) index. The concepts of Fuzzy Performance Importance Index (FPII) along with Degree of Similarity (DOS) (obtained from fuzzy set theory) are applied to rank different gresilient criteria in accordance to their current status of performance. The study is further extended to analyze, and thereby, to mitigate various risk factors (risk sources) involved in e-commerce exercises. A total forty eight major e-commerce risks are recognized and evaluated in a decision making perspective by utilizing the knowledge acquired from the fuzzy set theory. Risk is evaluated as a product of two risk quantifying parameters viz. (i) Likelihood of occurrence and, (ii) Impact. Aforesaid two risk quantifying parameters are assessed in a subjective manner (linguistic human judgment), rather than exploring probabilistic approach of risk analysis. The ‘crisp risk extent’ corresponding to various risk factors are figured out through the proposed fuzzy risk analysis approach. The risk factor possessing high ‘crisp risk extent’ score is said be more critical for the current problem context (toward e-commerce success). Risks are now categorized into different levels of severity (adverse consequences) (i.e. negligible, minor, marginal, critical and catastrophic). Amongst forty eight risk sources, top five risk sources which are supposed to adversely affect the company’s e-commerce performance are recognized through such categorization. The overall risk extent is determined by aggregating individual risks (under ‘critical’ level of severity) using Fuzzy Inference System (FIS). Interpretive Structural Modeling (ISM) is then used to obtain structural relationship amongst aforementioned five risk sources. An appropriate action requirement plan is also suggested, to control and minimize risks associated with e-commerce exercises