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PurposeLine–cell conversion and rotation of operators between cells are common in lean production systems. Thus, the purpose of this study is to provide an integrated look at these two practices through integrating job rotation scheduling and line-cell conversion problems, as well as investigating the effect of rotation frequency on flow time of a Seru system.Design/methodology/approachFirst, a nonlinear integer programming model of job rotation scheduling problem and line–cell conversion problem (Seru-JRSP) was presented. Then, because Seru-JRSP is NP-hard, an efficient and effective invasive weed optimization (IWO) algorithm was developed. Exploration process of IWO was enhanced by enforcing two shake mechanisms.FindingsComputations of various sample problems showed shorter flow time and less number of assigned operators in a Seru system scheduled through job rotation. Also, nonlinear behavior of flow time versus number of rotation periods was shown. It was demonstrated that, setting number of rotation frequency to one in line with the literature leads to inferior flow time. In addition, ability of developed algorithm to generate clusters of equivalent solutions in terms of flow time was shown.Originality/valueIn this research, integration of job rotation scheduling and line–cell conversion problems was introduced, considering lack of an integrated look at these two practices in the literature. In addition, a new improved IWO equipped with shake enforcement was introduced.

PurposeThis paper aims to unfold the role that job rotation plays in a lean cell. Unlike many studies, the authors consider heterogeneous operators with dynamic performance factor that is impacted by the assignment and scheduling decisions. The purpose is to derive an understanding of the underlying effects of job rotations on performance metrics in a lean cell. The authors use an optimization framework and an experimental design methodology for sensitivity analysis of the input parameters.Design/methodology/approachThe approach is an integration of three stages. The authors propose a set-based optimization model that considers human behavior parameters. They also solve the problem with two meta-heuristic algorithms and an efficient local search algorithm. Further, the authors run a post-optimality analysis by conducting a design of experiments using the response surface methodology (RSM).FindingsThe results of the optimization model reveal that the job rotation schedules and the human cognitive metrics influence the performance of the lean cell. The results of the sensitivity analysis further show that the objective function and the job rotation frequencies are highly sensitive to the other input parameters. Based on the findings from the RSM, the authors derive general rules for the job rotations in a lean cell given the ranges in other input variables.Originality/valueThe authors integrate the job rotation scheduling model with human behavioral and cognitive parameters and formulate the problem in a lean cell for the first time in the literature. In addition, they use the RSM for the first time in this context and offer a post-optimality analysis that reveals important information about the impact of the job rotations on the performance of operators and the entire working cell.

Purpose Interrelationships among some common factors of human resource (HR) management and quality management are still unexplored. Changes in work patterns due to the Covid-19 pandemic have aroused interest in some of these factors, such as working-hours, work pressure, work–life balance practices, job satisfaction. The purpose of this study is to explore the interrelationships among such factors. Specifically, the influence of work hours, work pressure, job rotation and work–life balance on job satisfaction is evaluated both directly and under the mediating influence of working conditions. Design/methodology/approach A questionnaire-based survey was conducted in Indonesia among diversified organisations. A total of 432 responses were gathered, and they were examined using hypothesis testing and partial least square based structural equation modelling. Findings The study confirms the statistically proven impact of work pressure, job rotations and work–life-balance practices on working conditions. Job rotations, work–life balance practices and working conditions directly influenced job satisfaction. Work pressure did not influence job satisfaction directly, but it significantly influenced working conditions, which eventually affected job satisfaction. Working hours neither affected working conditions nor job satisfaction in a significant manner. Practical implications Covid-19 necessitated working from home, which is a peculiar work–life balance situation. The findings are helpful for organisations in planning strategies related to work–life-balance, working hours, multi-skilling, working conditions and other quality of work life factors in both regular working conditions and under Covid-19 conditions. Social implications The proven influence of work pressure and work–life-balance practices may result in the formation of informal organisations, social groups and increased social networking. As working hours are not diagnosed as an influencing factor for job satisfaction, organisations may think about increasing them, affecting the social fabric of the working community. Originality/value Previously unexplored interrelationships among various quality of work life factors are established. Under Covid-19 circumstances, factors such as working hours, work–life-balance and work pressure are investigated in a novel manner. The factors and their interrelationships are important to both quality management professionals and HR professionals.

The proverb “a change is as good as a rest” expresses one of the main advantages of job rotation. In this article, we examine ways to set-up effective job rotation schedules that balance ergonomic risks among workers. The practical relevance of the problem is comprehensively discussed on examples from the automobile industry. We present the ergonomic job rotation scheduling problem and show that it is NP-hard in the strong sense. Therefore, the development of specialized solution methods is important. Exploiting the problem structure, we propose a fast and effective smoothing heuristic which can be integrated into solution methods for computing initial solutions and/or as a local re-optimization procedure. We find that integrating the smoothing heuristic into a suited tabu search approach is particularly recommendable. In computational experiments, this combination of approaches is able to solve almost all instances of a practical data set in very short computation times of some seconds, whereas the standard solver FICO Xpress and the best-known heuristic from literature perform considerably worse concerning computation time and solution quality.

This paper presents a new model dealing with the job rotation scheduling problem, which is less studied, focusing on human characteristics such as boredom. Existing literature on conceptualizing boredom shows that researchers evaluate boredom in terms of exposure to the same tasks. We developed it to “exposure to similar tasks” and defined its functionality based on the need of assigning jobs with more similarity to each worker in the smallest period of planning which lowers the external interruption effect on worker’s concentration. To address the imbalance between number of jobs and that of workers in many industrial settings, we developed a multi-period imbalance assignment model. The proposed model is to rotate workers during a given planning horizon such that the total cost including assignment and boring cost will be minimized. The applicability of the model is described by presenting some real cases and validated through solving several randomly produced test problems by using Lingo software. Two search algorithms, genetic algorithm (GA) and imperialist competitive algorithm (ICA), designed to conquer the algorithmic complexity of model and their parameters adjusted using Taguchi’s method were used. The efficiency of algorithms is shown, comparing it with Lingo computation times, and it is shown that ICA solutions have better quality than GA solutions as well.

Purpose Nowadays, the phenomenon of increased competition between firms and their need to respond effectively to rapidly changing operational conditions, as well as to personnel requirements, has escalated the necessity to identify those factors that affect employee performance (EP). The purpose of this paper is to examine the interrelations between firm/environment-related factors (training culture, management support, environmental dynamism and organizational climate), job-related factors (job environment, job autonomy, job communication) and employee-related factors (intrinsic motivation, skill flexibility, skill level, proactivity, adaptability, commitment) and their impact on EP. Design/methodology/approach A new research model that examines the relationships between these factors and EP is proposed utilizing the structural equation modeling approach. Findings The results indicate that job environment and management support have the strongest impacts (direct and indirect) on job performance, while adaptability and intrinsic motivation directly affect job performance. Research limitations/implications A potential limitation of this research is that it is not focused only on one business sector (i.e. the sample is heterogeneous). Originality/value In this study, firm/environmental-related factors, job-related factors, employee-related factors and EP are incorporated in a single model using data from small- and medium-sized enterprises. Overall, the final model can explain 27 percent of EP variance (first-level analysis) and 42 percent of EP variance (second-level analysis).

Purpose Considering the unprecedented supply chain disruptions due to the COVID-19 pandemic, especially in the agri-food sector, the possession of dynamic capabilities (DCs) – particularly, the need for higher agility – seems to be the key to survival in highly uncertain environments. This study aims to use the dynamic capability view (DCV) theory to analyze how three key supply chain capabilities – organizational flexibility, integration and agility – should be combined to obtain the desired supply chain performance. Design/methodology/approach The authors designed a conceptual model in which the relationships between these three key capabilities and supply chain performance were hypothesized. The model was first tested through partial least square regression using survey data collected from 98 members of the Peruvian coffee supply chain. A fuzzy-set qualitative comparative analysis (fsQCA) was conducted to uncover how DCs could be combined in successful supply chain configurations. Findings The authors show that organizational flexibility is a driver of higher agility in agri-food supply chains, together with external and internal supply chain integration, that have a direct impact on agility, which positively affects supply chain performance. Higher levels of supply chain agility are necessary but insufficient to guarantee high performance, as sufficiency is reached when both integration (internal and/or external) and agility are present. Originality/value This study represents a pioneering attempt to apply the DCV theory to agri-food supply chains – characterized by many sources of uncertainty. All the DCs are included within the same model and the joint use of PLS regression and fsQCA provides evidence about the relationships between DCs and how they can empower agri-food supply to obtain the desired performance.

Opponents of mandatory rotation argue that a change of partner is bad for audit quality, as it results in a loss of client-specific knowledge. On the other hand, proponents argue that a change of partner is beneficial, as it results in a positive peer review effect and a fresh perspective on the audit. We test the impact of mandatory partner rotation on audit quality using a unique dataset of audit adjustments in China. Our results suggest that mandatory rotation of engagement partners results in higher quality audits in the years immediately surrounding rotation. Specifically, we find a significantly higher frequency of audit adjustments during the departing partner's final year of tenure prior to mandatory rotation and during the incoming partner's first year of tenure following mandatory rotation.

Currently, the largest percentage of the employed workforce in the manufacturing industry is involved in the assembly process, making ergonomics a key factor when dealing with assembly-related problems. During these processes, repetitive tasks and heavy component handling are frequent for workers, who may result overloaded from an energetic point of view, thus affecting several aspects not only relating to the human factor but also to potentially reduced productivity. Different organizational strategies and technological solutions could be adopted to overcome these drawbacks. For these purposes, the present paper proposes a genetic algorithm for solving the typical problem of assembly line balancing, taking into account job rotation and human–robot collaboration for enhancing ergonomics of workers. The objectives of the problem are related to both economic aspects and human factor: (i) the cost for implementing the assembly line is minimized, evaluated on the basis of the number of workers and differentiated by skill levels and on equipment installed on workstations, including collaborative robots, and (ii) the energy load variance among workers is also minimized, so as to smooth their energy expenditure in performing the assigned assembly operations, calculated according to their movements, physiological characteristics, job rotations and degree of collaboration with robots. The paper finally presents and discusses the application of the developed tool to an industrial assembly case.