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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A. Molina, A. Gabaldón, M. Kessler, J.A. Fuentes and E. Gómez
The main objective of this paper is to obtain the duty‐cycle probability forecast functions of cooling and heating aggregated residential loads. The method consists of three…
Abstract
The main objective of this paper is to obtain the duty‐cycle probability forecast functions of cooling and heating aggregated residential loads. The method consists of three steps: first, the single loads are modelled using systems of stochastic differential equations based on perturbed physical models; second, intensive numerical simulation of the stochastic system solutions is performed, allowing several parameters to vary randomly; and third, smoothing techniques based on kernel estimates are applied to the results to derive non‐parametric estimators, comparing several kernel functions. The use of these dynamical models also allows us to forecast the indoor temperature evolution under any performance conditions. Thus, the same smoothing techniques provide the indoor temperature probability forecast function for a load group. These techniques have been used with homogeneous and non‐homogeneous device groups. Its main application is focused on assessing Direct Load Control programs, by means of comparing natural and forced duty‐cycles of aggregated appliances, as well as knowing the modifications in customer comfort levels, which can be directly deduced from the probability profiles. Finally, simulation results which illustrate the model suitability for demand side – bidding – aggregators in new deregulated markets are presented.
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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.
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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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Naureen Akber Ali, Anam Feroz, Noshaba Akber and Adeel Khoja
Coronavirus disease 2019 (COVID-19) pandemic has led to unprecedented mental health repercussions in the lives of every individual including university students. Therefore, study…
Abstract
Purpose
Coronavirus disease 2019 (COVID-19) pandemic has led to unprecedented mental health repercussions in the lives of every individual including university students. Therefore, study on students’ psychological state and its associated factors during the pandemic are of importance. This study aims to discuss the aforementioned issue.
Design/methodology/approach
An online survey was done on a total of 207 university students of Pakistan to collect information on socio-demographic characteristics, concerns or fears amidst COVID-19 and mental distress. Validated tools; Perceived Stress Scale (PSS), Generalized Anxiety Disorder Scale (GAD-7) and Patient Health Questionnaire (PHQ-9)-Depression were used to assess stress, anxiety and depression, respectively.
Findings
Around 14% of the university students were experiencing severe stress and anxiety, while 8.2% had severe depression. The authors found that stress among university students was related to psychiatric illness or symptoms (OR = 5.1: 1.1, 22.9) and unpredictability due to the pandemic (OR = 3.7: 1.2, 11.2). The significant determinants of anxiety were psychiatric illness/symptoms (OR = 6.6: 3.4, 12.9), implementation of public health measures (OR = 3.7: 1.1, 11.6), employed mothers (OR = 2.4: 1.1, 5.0) and lack of support from university administration (OR = 2.2: 1.0, 5.0). While the factors associated with depression included psychiatric illness or symptoms (OR = 8.4: 3.3, 21.5), unpredictability due to pandemic (OR = 6.8: 2.2, 20.7), impaired social support system (OR = 3.7: 1.3, 10.4) and studying without a scholarship (OR = 2.1: 1.0, 4.4).
Research limitations/implications
These findings call for an urgent need to develop appropriate interventions and educational programs that could address the psychological needs of students.
Practical implications
The study directs the role of university and faculty in dealing the mental health needs of the student in COVID-19 pandemic time.
Social implications
Educational programs are important that could address the psychological needs of students in COVID-19 pandemic.
Originality/value
University students reported mental distress during COVID-19 pandemic which shows that younger people are at risk of COVID-19 repercussions. Moreover, several stressors (i.e. impaired social support system and lack of support from universities) were revealed that could be mitigated by implementing appropriate strategies.
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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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Robert Pellerin and Ali Gharbi
It is assumed that the production system responds to planned demand at the end of the expected life of each individual piece of equipment and unplanned demand triggered by…
Abstract
Purpose
It is assumed that the production system responds to planned demand at the end of the expected life of each individual piece of equipment and unplanned demand triggered by equipment failures. The difficulty of controlling this type of production system resides in the variable nature of the remanufacturing process. In practice, remanufacturing operations for planned demand can be executed at different rates, referring to different component replacement and repair strategies. A sub‐optimal control policy in which inventory thresholds trigger the use of different execution modes has been formulated in previous research to address this problem when unplanned demands are processed under an exponential time distribution. The aim of this study is to extend this control policy to more realistic unplanned demand arrival and processing times distributions.
Design/methodology/approach
The proposed approach is based on a combination of analytical modeling, simulation experimentation and regression analysis. The model was validated by comparing the obtained simulation results with those obtained under an exponential processing time distribution.
Findings
The results demonstrate that the structure of optimal control can be approximated by the sub‐optimal multiple hedging point policy with non‐significant cost variations.
Practical implications
The simulation results demonstrate that hedging point control policies could be applicable to a wide variety of complex remanufacturing problems in which analytical solutions are not easily obtained.
Originality/value
The paper extends the concept of hedging point policy to the control of real‐word repair and remanufacturing operations. Once calculated, the sub‐optimal policy parameters can be simply implemented by practitioners through the definition of stock‐level parameters.
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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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Richard Chawana, Anastacia Mamabolo and Evangelos Apostoleris
Africa has the most deaths from infections yet lacks adequate capacity to engage in vaccine development, production and distribution, the cornerstone of efficiently managing and…
Abstract
Purpose
Africa has the most deaths from infections yet lacks adequate capacity to engage in vaccine development, production and distribution, the cornerstone of efficiently managing and eliminating several infectious diseases. Research has scarcely explored the role of institutional logics in vaccine development, production and distribution, collectively known as end-to-end vaccine manufacturing. This study aims to explore how institutional logics influence firms to engage in the vaccine manufacturing value chain in Africa.
Design/methodology/approach
We conducted multiple case study research using five vaccine manufacturing firms from four African countries in three regions. Qualitative interviews were conducted among 18 executives in 5 vaccine manufacturing firms.
Findings
We identified that the state, corporate and market institutional logics disparately influence the different parts of the vaccine manufacturing value chain. These institutional logics co-exist in a constellation that also shapes the organizational forms. Their constellation has dominant logics that guide behavior, while subdominant and subordinate logics influence behavior to a limited extent. The findings show that institutional logics are a function of contextual factors, such as historical events, technological changes and pandemics.
Originality/value
The study developed a typology that identifies vaccine manufacturing firm archetypes, institutional logics and their constellations underpinned by contextual factors. The findings have implications for firms and policymakers, as they may guide the end-to-end vaccine manufacturing interventions adapted for their regions.