Salwa Hanim Abdul-Rashid

The impact of sustainable manufacturing practices on sustainability performance: Empirical evidence from Malaysia

Sustainable manufacturing practices are one of the significant environmental initiatives taken by manufacturing industries to preserve the environment and improve the quality of human life while performing manufacturing activities. The emergence of the value creation concept, economic value no longer counts as a single factor for measuring manufacturing performance. Within the sustainability context, the impact of manufacturing activities on the environmental and social aspects should be taken into account as the basis for assessing manufacturing performance, which is called sustainability performance. The purpose of this paper is to examine the relationship of sustainable manufacturing practices with sustainability performance, which considers the environmental, economic and social aspects.,A questionnaire survey is carried out among 443 ISO 14001 certified manufacturing companies in Malaysia. Structural equation modelling is used to evaluate the relationship of sustainable manufacturing practices with sustainability performance.,The findings of this study indicate that manufacturing process is the manufacturing stage that gives the most impact on the improvement of sustainability performance. Hence, it is concluded that manufacturing companies in Malaysia are highly focussed on the production bound when implementing sustainable manufacturing practices.,Although this study indicates a good estimation of the proposed model, additional variables might be added to improve the prediction strength of the proposed model such as considering type of industries, economic scale or ownership. Adding the comparison of sustainable manufacturing practices between different countries also a valuable research to investigated.,The framework proposed here can also assist manufacturing industries to conduct sustainability assessments by providing elements of sustainability performance and can serve as a guideline to select appropriate sustainable manufacturing practices and to what level the practices need to be improved to leverage companies’ sustainability performance.,The framework proposed here can also assist manufacturing industries to conduct sustainability assessments by providing elements of sustainability performance and can serve as a guideline to select appropriate sustainable manufacturing practices and to what level the practices need to be improved to leverage companies’ sustainability performance.

Development of a fuzzy economic order quantity model for imperfect quality items using the learning effect on fuzzy parameters

This paper develops an inventory model for items with imperfect quality in a fuzzy environment by assuming that learning occurs in setting the fuzzy parameters. This implies that inventory planners collect information about the inventory system and build up knowledge from previous shipments, and thus learning process occurs in estimating the fuzzy parameters. So, it is hypothesized that the fuzziness associated with all fuzzy inventory parameters is reduced with the help of the knowledge acquired by the inventory planners. In doing so, the study developed a total profit function with fuzzy parameter, where triangular fuzzy number is used to quantify the fuzziness of the parameters. Next, the learning curve is incorporated into the fuzzy model to account for the learning in fuzziness. Subsequently, the optimal policy, including the batch size and the total profit are derived using the classical approach. Finally, numerical examples and a comparison among the fuzzy learning, fuzzy and crisp cases are provided to highlight the importance of using learning in fuzzy model.

A fuzzy EOQ model with backorders and forgetting effect on fuzzy parameters

This paper considers a fuzzy lot-sizing problem with forgetting effect.A new fuzzy EOQ model with backorders and forgetting is developed.The result suggests decreasing the maximum inventory and the total inventory cost. The study of learning effect on inventory models with imprecise parameters is a research topic that has recently emerged. The research papers have published so far studied this aspect from a theoretical point of view and thus the literature lacks the investigation of this topic from a practical standpoint. To close this research gap, we conducted a semi-structured interview with a number of industry experts to gain insights into the prevalence of learning and forgetting in real applications. Based on the insights gained from the interviews, we have developed a recently published model by countering the assumption of full transfer of learning. The model developed herein proposes a situation where the knowledge gained by the operator in setting imprecise parameters deteriorates over the planning cycles due to intermittent planning process. A numerical study suggests that accounting for the effect of knowledge depreciation/forgetting on imprecise parameters leads to reduction in maximum inventory, which consequently reduces the total cost of the system.

A Fuzzy Reverse Logistics Inventory System Integrating Economic Order/Production Quantity Models

Abstract This paper develops a reverse inventory model where the recoverable manufacturing process is affected by the learning theory. We propose the inclusion of the fuzzy demand rate of the serviceable products and the fuzzy collection rate of the recoverable products from customers in the total cost function of the model. Two popular defuzzification methods, namely the signed distance technique, a ranking method for fuzzy numbers, and the graded mean integration representation method are employed to find the estimate of the total cost function per unit time in the fuzzy sense. We provide a comprehensive numerical example to illustrate and compare the results obtained by the two mentioned defuzzification methods. This is one of the only few attempts in the related literature comparing the performance of these methods with the effect of the fuzziness of both of the demand and the collection rate in the presence of the learning simultaneously. The results indicate that deciding on which method could be used depends on the target strategy that could focus on the total cost, ordering lot size, or recovery lot size.

Sustainable supply chain design-a configurational approach

Designing the right supply chain that meets the requirements of sustainable development is a significant challenge. Although there are a considerable number of studies on issues relating to sustainable supply chain design (SSCD) in terms of designing the practices, processes, and structures, they have rarely demonstrated how these components can be aligned to form an effective sustainable supply chain (SSC). Considering this gap in the literature, this study adopts the configurational approach to develop a conceptual framework that could configure the components of a SSC. In this respect, a process-oriented approach is utilized to classify and harmonize the design components. A natural-resource-based view (NRBV) is adopted to determine the central theme to align the design components around. The proposed framework presents three types of SSC, namely, efficient SSC, innovative SSC, and reputed SSC. The study culminates with recommendations concerning the direction for future research.

An economic order quantity model considering different holding costs for imperfect quality items subject to fuzziness and learning

This paper extends an economic order quantity (EOQ) model for items with imperfect quality based on two different holding costs and learning considerations. This is one of the few attempts aiming at combining the EOQ model, learning theory, and fuzzy technique in solving an EOQ problem. In present research, a fuzzy model is developed in which both parameters and decision variables are fuzzified and represented by triangular fuzzy numbers (TFNs). The total profit per unit time is obtained using fuzzy arithmetic operations, and then defuzzified by the graded mean integration value (GMIV) method. Using Karush-Kuhn-Tucker (KKT) conditions, the optimal lot size is obtained from the defuzzified total profit per unit time function. A numerical example for investigating the behavior of the model in a fuzzy situation is presented, and directions for future study are proposed. Besides, the results of the developed fully fuzzy model are compared with some previous ones in the literature.

A fuzzy lot-sizing problem with two-stage composite human learning

Due to the repetitive nature of inventory planning over the planning horizon, the operator in charge has to perform planning tasks repetitively, and consequently s/he becomes more familiar with the tasks over time. Familiarity with the tasks suggests that learning takes place in inventory planning. Even though the operator’s learning over time might improve his/her efficiency, prior research on fuzzy lot-sizing problems mostly overlooked the effect of human learning in their models and its impact on the operator’s performance. To close the research gap in this area, this paper models the operators learning in a fuzzy economic order quantity model with backorders. The paper models a situation where the operator applies the acquired knowledge over the cycles in setting the fuzzy parameters at the beginning of every planning cycle, where his/her learning ability includes the cognitive and motor capabilities of a human being. Subsequently, a mathematical model which takes account of a two-stage human learning over the planning cycles is developed, which is then analytically investigated using sample data-sets. The results indicate that both operator’s capabilities, cognitive and motor, affect the efficiency of the fuzzy lot-sizing inventory model, but the influence of the cognitive capability is more profound, which in turn suggests the importance of training programmes for the workforces. The results of the sensitivity analysis also draw some managerial insights for the case that some model parameters vary over the planning horizon.

Fuzzy inventory models

Display Omitted We give an overview of the fuzzy inventory models literature gathering 210 papers from 1987 onward.The current state of the literature is categorized into 13 areas under five main groups.We provide details to describe the applied methodologies.The research is concluded with future research directions. Over the years since the advancement of inventory management and fuzzy set theories, a vast number of studies have been published to integrate these concepts. Nonetheless, no comprehensive and systematic literature review can be found that analyzed the studies in this research stream. It motivated us to conduct this survey as a systematic and comprehensive review in the field of fuzzy inventory management to identify major achievements attained so far and shed light on future directions. First, the earlier review papers are presented to reveal the necessity of this study, and then methodology applied in collecting sample papers is described, followed by an in-depth analysis of the papers. Totally, a sample of 210 papers is identified and classified according to the common characteristics of the models. Several aspects of the models are assessed that led to identification of some areas overlooked by researchers so far.

Economic order quantity models for items with imperfect quality and emission considerations

ABSTRACTIncorporation of quality and environmental concerns in production and inventory models has received considerable attention in the inventory management literature; however, researchers studied these topics mostly independently. Thus, it is required to jointly incorporate those two relevant aspects in a single research to support decisions, compare the results and obtain new insights for complexities in practice. This paper takes a step in this line of thought and revisits some economic order quantity (EOQ) models with imperfect quality from a sustainable point of view. The objective is to investigate the impact of emission costs on the replenishment order sizes and the total profit of a buyer (retailer) in an imperfect supply process, where the buyer receives the batches containing a percentage of imperfect quality items. First, an EOQ model with imperfect quality items and emission costs, which are the result of warehousing and waste disposal activities, is formulated. Next, the model is extended ...

The Development of a Strategic Prioritisation Method for Green Supply Chain Initiatives

To maintain a competitive position, companies are increasingly required to integrate their proactive environmental strategies into their business strategies. The shift from reactive and compliance-based to proactive and strategic environmental management has driven companies to consider the strategic factors while identifying the areas in which they should focus their green initiatives. In previous studies little attention was given to providing the managers with a basis from which they could strategically prioritise these green initiatives across their companies’ supply chains. Considering this lacuna in the literature, we present a decision-making method for prioritising green supply chain initiatives aligned with the preferred green strategies alternatives for the manufacturing companies. To develop this method, the study considered a position between determinism and the voluntarism orientation of environmental management involving both external pressures and internal competitive drivers and key resources as decision factors. This decision-making method was developed using the analytic network process (ANP) technique. The elements of the decision model were derived from the literature. The causal relationships among the multiple decision variables were validated based on the results of structural equation modelling (SEM) using a dataset collected from a survey of the ISO 14001-certified manufacturers in Malaysia. A portion of the relative weights required for computation in ANP was also calculated using the SEM results. A case study is presented to demonstrate the applicability of the method.