A knowledge diagnostic system for product defects

The need to fulfill customer satisfaction and increase product quality has motivated many manufacturing firms to investigate and diagnose their product failure. To gain a correct and accurate diagnostic, the entire processing root must be recorded and controlled in every step of the manufacturing process. In this research, a prototype system has been developed for a tile manufacturing company to diagnose tile defects and to recommend actions for improvement. This system consists of two main components, the knowledge base and inference engine. The knowledge base has been developed by capturing data and information that are related to tile defects, such as symptoms, probable causes, types of defects, processes, sub processes, tile classifications, etc. On the other hand, the inference engine has been built by implementing the forward chaining and depth first searching methods to search for the causes of defects. The analysis proves that this system can help the workers in the company to diagnose tile defects and solve the problems. Besides this, the system can also help to share and transfer knowledge among the knowledge workers in the company

A new integrated multi-criteria decision-making model for resilient supplier selection

Unexpected worldwide disruptions brought various challenges to supply chain management thus manipulating the research direction towards resilience. Since the supplier is one of the important supply chain elements, the challenges can be overcome through resilient supplier selection. Supplier selection is a multi-criteria decision-making problem where several criteria are involved. In this study, GRA-BWM-TOPSIS was proposed to evaluate resilient suppliers. Seven resilience criteria which were Quality, Lead Time, Cost, Flexibility, Visibility, Responsiveness and Financial Stability have been proposed and five experts were selected to provide judgments for the selection process. By using the proposed method, the criteria importance levels were obtained using GRA and the criteria weights were computed using BWM, together with a consistency test. TOPSIS was applied to evaluate the suppliers’ performances. Through a case study in a food manufacturing company, 10 suppliers were evaluated and ranked. A validation process was carried out and the managerial implications were provided to ensure the effectiveness of the proposed model. GRA-BWM-TOPSIS is suitable for resilient supplier selection when there are uncertainties and incomplete data

A simulation-based product diffusion forecasting method using geometric Brownian motion and spline interpolation

This study addresses the problem of stochasticity in forecasting diffusion of a new product with scarce historical data. Demand uncertainties are calibrated using a geometric Brownian motion (GBM) process. The spline interpolation (SI) method and curve fitting process have been utilized to obtain parameters of the constructed GBM-based differential equation over the product's life cycle (PLC). The constructed stochastic differential equation is coded as the forecast model and is simulated using MATLAB. The results are several sample demand paths generated from simulation of the forecast model. To evaluate the forecasting performance of the proposed method it is compared with Holt's model, using actual data from the semiconductor industry. The comparison results confirm the applicability of the proposed method in the semiconductor industry. The method can be helpful for policy-makers who require the prediction of uncertain demand over a time horizon, such as decisions associated with aggregate production planning, capacity planning, and supply chain network design. Especially for the semiconductor industry with intensive capital investment the proposed approach can be useful for making decisions associated with capacity allocation and expansion

A comprehensive and integrated stochastic-fuzzy method for sustainability assessment in the Malaysian food manufacturing industry

Manufacturing activities carry significant burdens for all three dimensions of sustainability, i.e., environment, economy and society. However, most of the available sustainability assessment methods for manufacturing are based on environmental concerns only. Moreover, it is hard to find a sustainability assessment method that considers both stochastic and fuzzy uncertainties concurrently and a comprehensive set of weighted and applicable indicators. Thus, the main purpose of this paper was to develop and test an integrated sustainability assessment method that included both stochastic and fuzzy uncertainties. Both quantitative and qualitative, and weighted sustainability indicators for the Malaysian food manufacturing industry needed to be considered, with reliable assessment results. In order to achieve the objective, the Monte Carlo simulation and fuzzy logic approaches were employed. An overall unit-less sustainability index was calculated to evaluate the current sustainability level. This method was demonstrated using a real-world case study of a Malaysian food manufacturing company. The results highlighted and traced the company-wide major low and high performing areas for all three dimensions of sustainability. The results unveiled that the case company could improve its sustainability performance more effectively by decreasing the amount of air emissions, polluted wastewater, etc., and improving the working conditions. This would enable the practitioners and decision-makers to allocate resources accordingly and more efficiently. Finally, the developed method was validated and the implications and conclusions of the research were presented

細菌細胞膜に含まれるカルジオリピンの抗原性とその抗体の細菌に対する反応性

The ecological issues arising from manufacturing operations have led to the focus on environmental sustainability in manufacturing. This can be addressed adequately using a closed-loop supply chain (CLSC). To attain an effective and efficient CLSC, it is necessary to imbibe a holistic performance measurement approach. In order to achieve this, there is a need to adopt a specific approach for a particular product rather than being generic. Since sustainability has direct environmental footprints that involve organizational stakeholders, suppliers, customers and the society at large, complexities surrounding supply chain performance measurement have multiplied. In this study, a suitable approach has been proposed for CLSC performance measurement in the automotive industry, based on reviewed literature. It is believed that this approach will result in increased effectiveness and efficiency in CLSC performance measurement

Effect of information sharing and capacity adjustment on the healthcare supply chain: a case of flood disaster

In recent years, flood has become a tragic disaster in Malaysia causing loss of lives and properties. The damages brought by the flood are partly due to lack of preparedness and responses. Researchers have reported studies on the flood preparedness and response. Nevertheless, limited studies have provided a holistic perspective on the flood response operations. This paper attempts to analyze the flood preparedness holistically through system dynamics modelling approach. A system dynamic model consisting of a flood evacuation sub-model and a healthcare supply chain sub-model is developed. The hydrological data for the Kelantan River basin in Malaysia is used to populate the model. Decisions on the evacuation are based on the river level and flood risk information. Bullwhip Effect is formulated as a performance indicator to evaluate the efficiency of the healthcare supply chain model. The effects of information sharing and the capacity adjustment delay on the bullwhip effect were investigated. The findings suggest that reducing the capacity adjustment time and sharing demand information to the upstream healthcare supply chain yields a better overall performance for the health care supply chain

Sustainable supplier selection and order lot-sizing: an integrated multi-objective decision-making process

Within supply chains activities, selecting appropriate suppliers based on the sustainability criteria (economic, environmental and social) can help companies move toward sustainable development. Although several studies have recently been accomplished to incorporate sustainability criteria into supplier selection problem, much less attention has been devoted to developing a comprehensive mathematical model that allocates the optimal quantities of orders to suppliers considering lot-sizing problems. In this research, we propose an integrated approach of rule-based weighted fuzzy method, fuzzy analytical hierarchy process and multi-objective mathematical programming for sustainable supplier selection and order allocation combined with multi-period multi-product lot-sizing problem. The mathematical programming model consists of four objective functions which are minimising total cost, maximising total social score, maximising total environmental score and maximising total economic qualitative score. The proposed model is developed based on the parameters achieved through the preprocessing of suppliers’ social, environmental and economic data by a rule-based weighted fuzzy approach and fuzzy analytical hierarchy process. The proficiency and applicability of the proposed approach is illustrated by a case study of packaging films in food industry. Considering sustainability criteria in the proposed model reveals that a higher value of sustainable purchasing is achievable in comparison with a single-objective cost-based model

Identifying and prioritizing the effective factors in the implementation of green supply chain management in the construction industry

In recent years, environmental protection and sustainability have become significant issues and have attracted everyone's attention. And many organizations are now interested in using it as their strategy to gain customer satisfaction and market share and outperform competitors. This article aims to identify and prioritize the main factors that implement green supply chain management (GSCM) in the construction industry. To achieve the goal, the integrated approach combining is fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and fuzzy analysis network Process (FANP) developed. The parameters employed are in this approach identified through an extensive literature review, and validation is criteria introduced through the experts’ opinions to discuss data uncertainty. First, the FDEMATEL method sets up the interrelationships between the criteria, which used for determining are the most important factors in the GSCM approach. Then, the local weight of the criteria calculated using the FANP approach based on cause and effect relationships, and through the FDEMATEL method. The results of this study show that external factors are the most important and influential factors in the GSCM approach, Therefore, the findings of this study can guide managers to make better use of the GSCM approach in the Iranian construction industry

Simultaneous selection and scheduling with sequence-dependent setup times, lateness penalties, and machine availability constraint : heuristic approaches

Job selection and scheduling are among the most important decisions for production planning in today's manufacturing systems. However, the studies that take into account both problems together are scarce. Given that such problems are strongly NP-hard, this paper presents an approach based on two heuristic algorithms for simultaneous job selection and scheduling. The objective is to select a subset of jobs and schedule them in such a way that the total net profit is maximized. The cost components considered include jobs' processing costs and weighted earliness/tardiness penalties. Two heuristic algorithms; namely scatter search (SS) and simulated annealing (SA), were employed to solve the problem for single machine environments. The algorithms were applied to several examples of different sizes with sequence-dependent setup times. Computational results were compared in terms of quality of solutions and convergence speed. Both algorithms were found to be efficient in solving the problem. While SS could provide solutions with slightly higher quality for large size problems, SA could achieve solutions in a more reasonable computational tim