F. Mhada, A. Hajji, R. Malhamé, A. Gharbi and R. Pellerin
This paper seeks to address the production control problem of a failure‐prone manufacturing system producing a random fraction of defective items.
Abstract
Purpose
This paper seeks to address the production control problem of a failure‐prone manufacturing system producing a random fraction of defective items.
Design/methodology/approach
A fluid model with perfectly mixed good and defective parts has been proposed. This approach combines the descriptive capacities of continuous/discrete event simulation models with analytical models, experimental design, and regression analysis. The main objective of the paper is to extend the Bielecki and Kumar theory, appearing under the title “Optimality of zero‐inventory policies for unreliable manufacturing systems”, under which the machine considered produced only good quality items, to the case where the items produced are systematically a mixture of good as well as defective items.
Findings
The paper first shows that for constant demand rates and exponential failure and repair time distributions of the machine, the Bielecki‐Kumar theory, adequately revisited, provides new and coherent results. For the more complex situation where the machine exhibits non‐exponential failure and repair time distributions, a simulation‐based approach is then considered. The usefulness of the proposed models is illustrated through numerical examples and sensitivity analysis.
Originality/value
Although the decisions taken in response to demands for productivity have a direct impact on product quality, management quality and production management have been traditionally treated as independent research fields. In response to this need, this paper is considered as a preliminary work in the intersection between quality control and production control issues.
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João Cláudio Soares, Sérgio Sousa and Anabela Tereso
The general objective of this research was to identify the practices of the mass production industries, on the decisions related to the detection of defective products and to…
Abstract
Purpose
The general objective of this research was to identify the practices of the mass production industries, on the decisions related to the detection of defective products and to identify relevant criteria, actions, effects and variables to be used in a decision-making model.
Design/methodology/approach
A survey questionnaire was developed and structured in 20 questions, with 18 closed questions and 2 open questions. The questions were constructed based on the literature review, with the identification of 15 theoretical and practical concepts of quality. Seven other information requests were included, concerning the initial characterization of the defective items and industry. The company, the product, the processes and the defects were contextualized, and then the decision-making process was framed, to understand the factors that influenced it.
Findings
The industries of the Industrial Pole of Manaus discard or rework their defective products influenced by promoting the lowest cost and the required quality. The factors with the biggest influence on decisions are rework and disposal costs, and time available for rework and replacement. The main requirements defined for the decision-making model were: 1) compare the rework and discard options, only if the rework reaches the required quality level; and 2) identify and account the effects of defective items on quality, productivity and costs.
Research limitations/implications
Overall, 109 questionnaires were sent, representing 21.3% (109/512) of the companies at the Industrial Pole of Manaus. The respondents represent 14.6% (75/512) of the population.
Originality/value
Defects can occur, requiring a decision that promotes the lowest cost and the required quality. Quality cost models do not show a systematic analysis for identification, accounting, evaluation of effects and criteria associated with the destination of manufactured defective items. Therefore this research was done to fill this gap.
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The purpose of this paper is to study kanban‐controlled pull‐systems with machines having the exponential reliability model and with buffers finite capacities.
Abstract
Purpose
The purpose of this paper is to study kanban‐controlled pull‐systems with machines having the exponential reliability model and with buffers finite capacities.
Design/methodology/approach
Using Markovian analysis closed formulas are derived for the performance metrics such as customer service level, average inventory level, and throttling of raw part release, for one‐machine systems. Then, a recursive procedure is developed to extend the performance evaluation technique to multi‐machine systems.
Findings
Once system‐theoretic properties of the systems under consideration are investigated, the paper reveals methods for evaluating the smallest, i.e. lean, number of kanbans that ensure the desired level of customer service which are developed.
Originality/value
Production systems can operate in either push or pull regime. The push‐systems have been analyzed extensively for over 50 years, while the pull‐systems have been studied to a much lesser extent.
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Kazhal Gharibi and Sohrab Abdollahzadeh
To maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by…
Abstract
Purpose
To maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (second objective function). To calculate the efficiency of disassembly centers by SDEA method, which are selected as suppliers and maximize the total efficiency (third objective function). To evaluate the resources and total efficiency of the proposed model to facilitate the allocation resource process, to increase resource efficiency and to improve the efficiency of disassembly centers by Inverse DEA.
Design/methodology/approach
The design of a closed-loop logistics network for after-sales service for mobile phones and digital cameras has been developed by the mixed-integer linear programming method (MILP). Development of MILP method has been performed by simultaneously considering three main objectives including: total network profit, green supply chain factors (environmental sustainability) and maximizing the efficiency of disassembly centers. The proposed model of study is a six-level, multi-objective, single-period and multi-product that focuses on electrical waste. The efficiency of product return centers is calculated by SDEA method and the most efficient centers are selected.
Findings
The results of using the model in a case mining showed that, due to the use of green factors in network design, environmental pollution and undesirable disposal of some electronic waste were reduced. Also, with the reduction of waste disposal, valuable materials entered the market cycle and the network profit increased.
Originality/value
(1) Design a closed-loop reverse logistics network for after-sales services; (2) Introduce a multi-objective multi-echelon mixed integer linear programming model; (3) Sensitivity analysis use Inverse-DEA method to increase the efficiency of inefficient units; (4) Use the GSC factors and DEA method in reverse logistics network.
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Himanshu Prajapati, Ravi Kant and Ravi Shankar
Reverse logistics has attracted many industries due to product recalls, enormous waste generation, competitive reasons, vast opportunity in the waste management market, and to get…
Abstract
Purpose
Reverse logistics has attracted many industries due to product recalls, enormous waste generation, competitive reasons, vast opportunity in the waste management market, and to get the maximum value out of waste recovery. Selection of the right implementation strategy is vital for reverse logistics to function efficiently. Therefore, this research aims to evaluate the criteria for selecting reverse logistics strategy and help to choose the preferred strategy for its implementation.
Design/methodology/approach
Three reverse logistics implementation strategies, namely, in-house, joint venture and outsourcing, are proposed. A novel hybrid fuzzy analytical hierarchy process (F-AHP) and fuzzy measurement of alternatives and ranking according to COmpromise Solution (F-MARCOS) based framework is developed to fulfil the research objective. A survey is performed on Indian manufacturing industry to demonstrate the applicability of the proposed framework.
Findings
The result shows that government policy and regulations, reverse logistics risks and reduced emission have prime importance for a manufacturing industry which needs to implement reverse logistics into its supply chain. Outsourcing is the preferred reverse logistics strategy followed by joint venture and in-house that a manufacturing firm in India can implement.
Research limitations/implications
The research results are based on the responses of the survey received. This research considers various industry sectors to test the applicability of the framework. However, for actual implementation, this survey must first be limited to a particular industry as the results will apply to that industrial sector only.
Practical implications
This developed framework simplifies the procedure of selecting the strategy when the industry needs to implement reverse logistics. For industries working with a smaller set of criteria, this framework is a powerful and dynamic approach for reducing and choosing the most pertinent one that helps accomplish their objectives of reverse logistics implementation strategy selection.
Originality/value
Based on the literature and current applicability of reverse logistics, this research proposes three models to implement reverse logistics in Indian industries. A novel hybrid F-AHP and F-MARCOS based framework is developed to handle the selection of suitable reverse logistics strategy.
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Nadia Bahria, Imen Harbaoui Dridi, Anis Chelbi and Hanen Bouchriha
The purpose of this study is to develop a joint production, maintenance and quality control strategy involving a periodic preventive maintenance policy.
Abstract
Purpose
The purpose of this study is to develop a joint production, maintenance and quality control strategy involving a periodic preventive maintenance policy.
Design/methodology/approach
The proposed integrated policy is defined and modeled mathematically.
Findings
The paper focuses on finding simultaneously the optimal values of the preventive maintenance period, the buffer stock size, the sample size, the sampling interval and the control chart limits, such that the expected total cost per time unit is minimized.
Practical implications
The paper attempts to integrate in a single model the three main aspects of any manufacturing system: production, maintenance and quality. The considered system consists of one machine subject to a degradation process that directly affects the quality of products. The process and product quality control is carried out using an “x-bar” control chart. In the proposed model, a preventive maintenance action is performed every
Originality/value
The existing models that simultaneously consider maintenance, inventory and control charts consist of a condition-based maintenance (CBM) policy. Periodic preventive maintenance (PM) has not been considered in such models. The proposed integrated model is original, in that it links production through buffer stocks, quality through a control chart and maintenance through periodic preventive maintenance (different practical settings and modeling approach than when CBM is used). Hence, this paper addresses practical situations where, for economic or technical reasons, only systematic periodic preventive maintenance is possible.
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Alireza Arab, Mohammad Ali Sheikholislam and Saeid Abdollahi Lashaki
The purpose of this paper is to review studies on mathematical optimization of the sustainable gasoline supply chain to help decision-makers understand the current situation, the…
Abstract
Purpose
The purpose of this paper is to review studies on mathematical optimization of the sustainable gasoline supply chain to help decision-makers understand the current situation, the exact dimensions of the problem and the models provided in the literature. So, a more realistic mathematical optimization model can be achieved by fully covering all dimensions of the supply chain of this product.
Design/methodology/approach
To evaluate and comprehend the mathematical optimization of the sustainable gasoline supply chain research area, a systematic literature review is undertaken that covers material collection, descriptive analysis, content analysis and material evaluation steps. Finally, based on this process, 69 related articles were carefully investigated.
Findings
The results of the systematic literature review show the main areas of the published papers on mathematical optimization of sustainable gasoline supply chain problems and the gaps for future research in this field presented based on them.
Research limitations/implications
This approach is subject to limitations because the protocol of the systematic review of the research literature only included searching for the considered combination of keywords in the Scopus and ProQuest databases. Furthermore, the protocol used in this paper restricts documents to English.
Practical implications
The results have significant implications for both academicians and practitioners in this field. It can be useful for academics to comprehend the gaps and future trends in this field. Also, for practitioners, it can be useful to identify and understand the parts of the mathematical optimization model, which can help them model this problem effectively and efficiently.
Originality/value
No systematic literature review has been done in this field by considering gasoline to the best of the authors’ knowledge and delivers new facts for the future development of this field.
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Zulfiquar N. Ansari, Ravi Kant and Ravi Shankar
Re-use of products in the supply chain has become a significant consideration in the last decade. It has resulted in the development of several product recovery alternatives…
Abstract
Purpose
Re-use of products in the supply chain has become a significant consideration in the last decade. It has resulted in the development of several product recovery alternatives. Remanufacturing in the supply chain is one such product recovery option that yields social, economic and environmental benefits. This study aims is to identify and evaluate the key performance indicators (KPIs) of the remanufacturing supply chain (RSC).
Design/methodology/approach
The KPIs of RSC are classified along with the five primary management processes (plan, source, make, deliver and return) of the supply chain operations reference (SCOR) model. A grey decision-making trial and evaluation laboratory (DEMATEL) technique is applied to investigate the complex interrelationships amongst the identified KPIs and categorize them into cause and effect group. The applicability of the proposed framework is demonstrated through a case organization involved in remanufacturing business.
Findings
The KPIs are identified based on literature analysis and subsequent discussion with decision panel experts. The present research work results reveal that “consumer awareness program”, “technological compatibility” and skilled workforce' are the most influential indicators.
Originality/value
This research work provides a framework to evaluate the causal relationship between the RSC KPIs. The framework proposed in this study is empirically applied to a case organization. Based on the study findings some important recommendations are presented to the decision-makers/policy planners to help them develop an action plan. This would help the case organization reduce resource consumption, increase market share and enable sustainable development.
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Sameh M Saad, Ramin Bahadori and Hamidreza Jafarnejad
This study proposes the Smart SME Technology Readiness Assessment (SSTRA) methodology which aims to enable practitioners to assess the SMEs Industry 4.0 technology readiness…
Abstract
Purpose
This study proposes the Smart SME Technology Readiness Assessment (SSTRA) methodology which aims to enable practitioners to assess the SMEs Industry 4.0 technology readiness throughout the end-to-end engineering across the entire value chain; the smart product design phase is the focus in this paper.
Design/methodology/approach
The proposed SSTRA utilises the analytic hierarchy process to prioritise smart SME requirements, a graphical interface which tracks technologies' benchmarks under Industry 4.0 Technology Readiness Levels (TRLs); a mathematical model used to determine the technology readiness and visual representation to understand the relative readiness of each smart main area. The validity of the SSTRA is confirmed by testing it in a real industrial environment. In addition, the conceptual model for Smart product design development is proposed and validated.
Findings
The proposed SSTRA offers decision-makers the facility to identify requirements and rank them to reflect the current priorities of the enterprise. It allows SMEs to assess their current capabilities in a range of technologies of high relevance to the Industry 4.0 area. The SSTRA assembles a readiness profile allowing decision-makers to not only perceive the overall score of technology readiness but also the distribution of technology readiness across the main smart areas. It helps to visualise strengths and weaknesses; whilst emphasising the fundamental gaps that require serious action to assist the program with a well-balanced effort towards a successful transition to Industry 4.0.
Originality/value
The SSTRA provides a step-by-step approach for decision-making based on data collection, analysis, visualisation and documentation. Hence, it greatly mitigates the risk of further Industry 4.0 technology investment and implementation.