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The evaluation of time-based performance is a valued approach in the lean management thinking which is based on delivering value from customer's perspective. This approach contributes for long-term competitiveness and success in today's business environment. The focus of this study is to analyse the cycle time and manufacturing lead time with value stream mapping (VSM) in the preparatory stage of the textile fabric manufacturing process and to identify and improve the non-value adding activities in the value chain (VC). The study presents an insight on the translation of performance improvement across functions and how upstream supply chain (SC) segments can be linked in the performance improvement program. It also covers how the application of VSM improves visibility and planning flexibility in textile fabric manufacturing process.

The time-based performance was evaluated using VSM and recording of the activity times in the existing process. The impact of the quality of supplier's raw material was also measured contributing to identify the strategy for procurement and the means to establish a feedback system to the upstream segments of the SC. The methodology of VSM, observation of the practice and the expertise of the individuals involved with the process were utilised to develop the value stream maps and to identify value adding activities, non-value adding activities, existing gaps and plans for improvement.

The means for improving the time-based performance were identified and their impact was measured. The factors responsible for improvement are related to the production system and with the procurement strategy. The improvement was achieved in terms of available capacity utilisation, balancing the work flow in the preparatory stages, visibility of the process by measuring its capability and flexibility for the planning function. The study revealed that the effectiveness and enhancement of VSM and related tools should be adopted to address the issue of limiting success rates of long term and repeating application of such tools. Continuous improvement, innovations and the systematic embedding of VSM in the process life cycle provide the ways for achieving long-term success.

The paper presents a real and in-depth study on the application of VSM in the textile manufacturing process. The scope of the study is broad; it covers activities across functions with actual estimates of activity times in the manufacturing process for the focused value streams. It offers researchers the opportunity to analyse the translation of productivity improvement across functions and how upstream SC segments can be linked in a performance improvement program.

The study offers useful insight for the managers in textile manufacturing and other sectors for improving the time-based performance and achieving higher utilisation of capacity. It identified the production factors and their impact on warping and sizing cycle time in selected value streams and those which share common activities. It also identified the directions for future research when repeating the application of VSM in the continuous improvement cycle. Furthermore, since the industries need to progress towards advanced systems including Industry 4.0 standards, adoption of advanced VSM tool with relevant technology can align their production systems to develop the required capability. This will also bring a sustainable competitive advantage in the system.

The focused sector is stagnant in terms of productivity and innovation. The adoption of the advanced tools can facilitate the implementation of continuous improvement and innovation strategies.

The main focus of this study is to analyse and improve the cycle time in the preparatory stage of the fabric manufacturing process. This has impact on other important and tangible measures including capacity utilisation and work flow and intangible measures including production planning flexibility and process visibility. The improvement impact is across departments.

Although extensive research has been conducted on precast production, irregular component loading constraints have received little attention, resulting in limitations for transportation cost optimization. Traditional irregular component loading methods are based on past performance, which frequently wastes vehicle space. Additionally, real-time road conditions, precast component assembly times, and delivery vehicle waiting times due to equipment constraints at the construction site affect transportation time and overall transportation costs. Therefore, this paper aims to provide an optimization model for Just-In-Time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time.

In order to propose a JIT (just-in-time) delivery optimization model, the effects of the sizes of irregular precast components, the assembly time, and the loading methods are considered in the 3D loading constraint model. In addition, for JIT delivery, incorporating real-time road conditions in the transportation process is essential to mitigate delays in the delivery of precast components. The 3D precast component loading problem is solved by using a hybrid genetic algorithm which mixes the genetic algorithm and the simulated annealing algorithm.

A real case study was used to validate the JIT delivery optimization model. The results indicated this study contributes to the optimization of strategies for loading irregular precast components and the reduction of transportation costs by 5.38%.

This study establishes a JIT delivery optimization model with the aim of reducing transportation costs by considering 3D loading constraints, real-time road conditions and assembly time. The irregular precast component is simplified into 3D bounding box and loaded with three-space division heuristic packing algorithm. In addition, the hybrid algorithm mixing the genetic algorithm and the simulated annealing algorithm is to solve the 3D container loading problem, which provides both global search capability and the ability to perform local searching. The JIT delivery optimization model can provide decision-makers with a more comprehensive and economical strategy for loading and transporting irregular precast components.

In this study, the authors investigate a different routing and scheduling problem in the field of home health care (HHC) management system. The purpose of this paper is to route and schedule the workday of health workers, assign the patients to suitable health workers, make accurate decisions to minimize costs, provide timely services and, in general, enhance the efficiency of HHC centers.

A mixed-integer linear programming model is developed to assign health workers to patients. The model considers health professionals with different skills, namely nurses and physicians. Additionally, three groups of patients are considered: patients who need a nurse, patients who need a physician and patients who need both. In the third group, the nurse must be present at the patient’s home following the physician’s visit in order to perform the required tasks.

The results of this study show a reduction in costs which results from the fewer health workers employed and dispatched in comparison with traditional approaches. With the help of our solution approach and model, HHC centers may not only successfully reduce their costs but also manage to meet their patients’ demands by assigning suitable nurses and physicians.

Previous studies have often focused on problems involving only one group of health professionals and rarely address problems involving multiple groups. The authors consider this a shortcoming, because in many cases, patients should be visited several times and by various health professionals.

The purpose of this paper is to evaluate if, given personal, supply-related features, and labour demand-related variables, there is a difference in the share of women finding more stable jobs with respect to men, in an eight-year time span.

Fragmentation leads to a lower probability of transitioning into more certain, full-time work positions. The authors analyse a rich cohort of dependent workers in Emilia-Romagna to investigate whether part-time jobs lead to full-time jobs in a “stepping-stone” fashion and whether this happens with the same probability for men and women. The focus is on the cost of part-time jobs rather than the contrast between permanent and temporary jobs, as often observed in the literature. The authors also evaluate the transition between part-time job formulae and open-ended work arrangements to determine whether women's transition to full-fledged, stable work positions is slightly rarer than their male counterparts. Even if the authors allow for the fact that part-time contracts can be a choice and not an obligation, these contracts generate more flexibility in managing the equilibrium between private and work life and create more uncertainty than full-time contracts because of the fragmentation associated with these arrangements.

The authors find that women have a more fragmented working career than men, in that they hold more contracts than men in the same time span; moreover, the authors find that part-time jobs act more as bottlenecks for women than for men.

The authors use a large administrative dataset with over 600,000 workers observed in the 2008–2015 time span, in Emilia Romagna, Italy. The authors can disentangle the number of contracts per worker and observe individual, anonymise personal features, that the authors consider in the authors' propensity score estimate. The authors ran a robustness check of the PSM estimates through coarsened exact matching (CEM).

This study aims at developing an understanding of action pathways when adverse events force micro-enterprises to change their operations.

This qualitative study draws upon empirical data collected from entrepreneurs facing the same adverse event—the COVID-19 pandemic—to build theory on different types of actions that micro-enterprises take and what leads up to these actions.

The findings suggest three types of action pathways. The first pathway is set off by losses stretched out over time and generates open-ended actions. The second pathway is set off by immediate losses and generates survival-oriented actions. The third pathway is set off by potential long-term losses and generates developmental-oriented actions.

This study offers novel insights into action pathways in response to adverse events, heterogeneity of such actions and processes that precede the choice of actions. It also expands the existing literature by showcasing actual actions instead of desired actions, which have already been extensively studied.

The COVID-19 pandemic spurred major, and possibly enduring, changes in paid work. In this chapter, we explore the continuity and change in several work day dimensions, including where it is performed, the amount of time spent working, the length of the work day, and who people are with when they work, as well as variation across population subgroups. We use nationally representative data from the American Time Use Survey (ATUS) to analyze change across the 2019 to 2021 period. While the shift to working primarily at home in 2020 is dramatic and continuing into 2021, working primarily at the workplace remains the modal experience for Americans. We find differences by gender, education, parental status, and age in which workers perform their jobs at home, and we find much more continuity in how much people work and when they work.

This exploratory paper aims to examine attitudes and practices with regard to the COVID-19 pandemic, as well as the effects of mandatory teleworking from home in the wake of the first state of emergency orders in Japan in 2020.

An online survey of married employees retrospectively assessed changes in work style, subjective well-being, work–family conflict and job performance before and during forced teleworking from home in Tokyo and three of the surrounding prefectures.

Regular employees reported high levels of anxiety and to have thoroughly implemented government-recommended hygiene and safety practices. A majority of respondents were satisfied with mandatory telework from home and desired to continue partial telework after the end of the pandemic. The strongest predictor of satisfaction with mandatory telework from home turned out to be adequate workspace at home for both men and women. However, the antecedents of the desire to continue working from home differed by gender.

These findings can help individuals, firms and governments better understand the effects of mandatory teleworking from home and devise countermeasures to maximize employee well-being and job performance. This is all the more crucial, as Japan has had successive waves of the virus and has declared numerous states of emergency since the beginning of the pandemic, forcing office workers to continue social distancing and remote working for the time being.

To the best of the authors’ knowledge, this research is one of the first to provide insights on how imposed teleworking from home in the context of COVID-19 in Japan affected regular employees’ personal and professional lives and to identify predictors of satisfaction with teleworking and the desire to continue doing so.

This paper aims to investigate the grey scheduling, which is the combination of grey system theory and scheduling problems with uncertain processing time. Based on the interval grey number and its related definitions, properties, and theorems, the single machine scheduling with uncertain processing time and its general forms are studied as the research object. Then several single machine scheduling models are reconstructed, and an actual production case is developed to illustrate the rationality of the research.

In this paper, the authors first summarize the definitions and properties related to interval grey numbers, especially the transitivity of the partial order of interval grey numbers, and give an example to illustrate that the transitivity has a positive effect on the computational time complexity of multiple interval grey number comparisons. Second, the authors redefine the general form of the single machine scheduling problem with uncertain processing time according to the definitions and theorems of interval grey numbers. The authors then reconstruct three single machine scheduling models with uncertain processing time, give the corresponding heuristic algorithms based on the interval grey numbers and prove them. Finally, the authors develop a case study based on the engine test shop of K Company, the results show that the proposed single machine scheduling models and algorithms with uncertain processing time can provide effective guidance for actual production in an uncertain environment.

The main findings of this paper are as follows: (1) summarize the definitions and theorems related to interval grey numbers and prove the transitivity of the partial order of interval grey numbers; (2) define the general form of the single machine scheduling problem with interval grey processing time; (3) reconstruct three single machine scheduling models with uncertain processing time and give the corresponding heuristic algorithms; (4) develop a case study to illustrate the rationality of the research.

In the further research, the authors will continue to summarize more advanced general forms of grey scheduling, improve the theory of grey scheduling and prove it, and further explore the application of grey scheduling in the real world. In general, grey scheduling needs to be further combined with grey system theory to form a complete theoretical system.

It is a fundamental work to define the general form of single machine scheduling with uncertain processing time used the interval grey number. However, it can be seen as an important theoretical basis for the grey scheduling, and it is also beneficial to expand the application of grey system theory in real world.

User-generated content (UGC) refers to semantic and behavioral traces created by users on various social media platforms. While several waves of platforms have come and gone, the long-term sustainability of UGC activities has become a critical question that bears significance for theoretical understanding and social media practices.

Based on a large and lengthy dataset of both blogging and microblogging activities of the same set of users, a multistate survival analysis was applied to explore the patterns of users' staying, switching and multiplatforming behaviors, as well as the underlying driving factors.

UGC activities are generally unsustainable in the long run, and natural attrition is the primary reason, rather than competitive switching to new platforms. The availability of leisure time, expected gratification and previous experiences drive users' sustainability.

The authors adopted actual behavioral data from two generations of platforms instead of survey data on users' switching intentions. Four types of users are defined: loyal, switcher, multiplatformer and dropout. As measured by the transitions among the four states, the different sustainability behaviors are thereby studied via an integrated framework. These two originalities bridge gaps in the literature and offer new insights into exploring user sustainability in social media.