•This paper examines the joint impact of CSR policy and market structure on environmental sustainability in supply chains.•Optimal government interventions are determined via game theory ...analysis.•Effectiveness of policies in promoting sustainability is investigated under monopoly/oligopoly market structures.•Insights provided for policymakers and managers on CSR regulations, market structure, and sustainability.
The integration of Corporate Social Responsibility (CSR) and Smart Manufacturing (SM) has emerged as a promising strategy for addressing carbon emissions. Governments can play a crucial role in promoting sustainable practices and environmental sustainability by implementing targeted policies and regulations. In this study, we examine the sustainability performance of competing smart supply chains (SSCs) that offer substitutable products under different CSR regulatory policies. Specifically, we investigate five CSR policies: Deregulation (Drg.), Direct Tariff on Market (DTM), Sustainability Penalty and Sustainability Credits (SP&SC), Direct Limitation on Sustainability (DLS), and Government Cooperative Sustainability Efforts (GCSE). Employing a Stackelberg game theoretical framework, we formulate and analyze the effectiveness of each CSR policy within monopoly and oligopoly market structures. Our analysis yields several interesting findings. For instance, the DTM policy in the monopoly market is shown to be the preferred regulatory approach as it effectively enhances both supply chain profitability and environmental sustainability. Furthermore, the DTM policy in the oligopoly market, along with the DLS policy in the monopoly market, results in a greater market share of sustainable products. Our study uncovers the importance of considering the synergistic effects of market structure and CSR when designing sustainability strategies for policymakers and supply chain managers. Understanding these dynamics enables policymakers to make informed decisions and develop policies that maximize the environmental benefits of CSR practices, considering varying market structures.
Selling during online influencers’ livestreaming has become prevalent in the retailing sector. Critical decisions for firms to keep the business sustainably profitable include whether it is worth ...collaborating with influencers, collaboration approaches, and the corresponding pricing and inventory decisions. Based on industrial practice, we investigate two business models: the Influencer-oriented Short-window (IOSW) model and the Market-oriented Long-window (MOLW) model, to understand how the models benefit a firm and how decision-making affects profit sustainability. To this end, we build stochastic optimization models and derive managerial insights. We find that neither of the two models dominates the other, so firms should not irrationally follow a case with successful influencer collaboration experiences. Moreover, a firm’s innovation capacity is the critical competence in the IOSW model, and an influencer’s fan base size is the fundamental driver for profits in the MOLW model. Firms should prioritize the “lowest price” strategy if they are not over-optimistic about the success of the livestream shows.
Promptly predicting defects during an additive manufacturing process using only copious log data provides many advantages, albeit with computational limitations. We focus on predicting defects during ...electron beam melting with the black box nature of the manufacturing machine. For an accurate prediction of defects, which are rare (
), we extract temporal information to track abnormalities and formulate a feature selection algorithm that maximizes the expected value of a cost-sensitive accuracy. Correct identification of features responsible for the defects increases predictive power and informs manufacturers of potential corrective/preventive actions for process improvement. We solve the feature selection through resampling strategies integrated with ensemble procedures to handle data uncertainty and imbalance. Exploiting data uncertainty in our search leads to finding robust features with consistent predictive power. Our proposed methodology shows a 43% improvement in predicting defects (recall) without losing precision. Beyond additive manufacturing, this methodology has general application for rare-event prediction and imbalanced datasets.
This study explores to what extent the adoption and performance of smart manufacturing technologies builds on the adoption of lean principles. Primary explorative survey data on the level of adoption ...of smart manufacturing technologies and lean principles and various operational performance outcomes were collected from a set of Dutch manufacturers and analysed using Cluster Analysis, ANOVA, and Necessary Condition Analysis (NCA). The Cluster Analysis shows that while lean is also applied without smart (“lean-only” companies), smart technologies are mostly applied in conjunction with lean (“lean and smart” companies), suggesting that the presence of lean principles is necessary for smart implementation. A third group of companies shows a low use of lean and smart (“non-adopters”). The NCAs further specify the extent of this necessity by showing that all individual smart manufacturing technologies used in our construct require presence of lean principles, with MES systems having the strongest dependency. Performance wise, lean-only and lean and smart companies have comparable superior performance compared to non-adopters when considering an aggregate operational performance measure using the dimensions of quality, delivery, flexibility and cost. When analysed separately, the aggregate level results remain true for quality and delivery performance. However, for flexibility, the superiority of lean-only companies is more apparent, while for cost, lean and smart companies are superior. This shows that implementing smart requires lean, but lean may suffice depending on the specific performance objectives strived for.
•Impact brought by exceptions can be greatly reduced at assembly stations.•The overall architecture guarantees the interactions among processes and resources.•The dynamic priority provides a ...practical tool for rescheduling with flexibility.•A case scenario was studied to examine the effectiveness of the models.
Assembly stations are important hubs that connect massive material, information, human labor, etc. The fixed-position assembly systems for complex products may deal with hundreds of thousands of processes, making them vulnerable to manufacturing exceptions. Many scheduling problems were described and solved in the past decades, however, the gap between theoretical models and industrial practices still exist. To achieve a practical method for the dynamic scheduling in case of exceptions while reducing the impact brought by the exceptions, an Intelligent Collaborative Mechanism (ICM) was proposed where negotiations on resource configuration may happen among tasks (i.e. assembly processes). The intercommunication among resources was guaranteed by the data-driven ICM framework. The Petri-net-based workflow analysis and the constraint matrix can pick out the tasks that are currently not bound by other ones. The dynamic priority of the processes was defined and obtained using grey relational analysis. The matching strategy among the selected tasks and operators can provide a scheduling plan that is close to the initial plan, so the assembly systems may remain effective even when exceptions occur. The proposed models were analyzed in a case scenario, where the impact brought by exceptions can decrease by 44.3% in terms of the operators’ utilization rate, and by 60.26% in terms of the assembly time. This research has provided a practical strategy to improve the flexibility and effectiveness of assembly systems for complex products.
This study examines the capabilities of technology provider ecosystems within smart manufacturing implementation projects. Whilst the study of capabilities for technology implementation is well ...acknowledged, the existing literature lacks a focused analysis on the dynamic capabilities required from ecosystems of technology providers engaging with adopter firms for the development of smart manufacturing solutions. More specifically, research has overlooked how such provider capabilities address the adopter's requirements and facilitate the related innovation outcomes for the adopter firm. Thus, our findings provide several contributions to the literature. Firstly, by examining two smart manufacturing projects within Pharmaceutical and Semi-conductor manufacturing contexts, we provide an in-depth analysis of the complexity of adopter requirements. Secondly, we uncover the nature of three main technology provider dynamic ecosystem capabilities. These reflect comprehensive skills in technology search and learning, project implementation, and knowledge transfer capabilities supporting the development of solutions for decision-making and predictive maintenance. Thirdly, we reveal how provider ecosystems build on these capabilities to address the complex requirements of the adopter firm and facilitate different types of process innovation outcomes. Respectively linked to performance, sustainability and evolving process sustainability.
In 2013, the German government implemented the strategic initiative Industry 4.0 to establish itself as a leader in advanced manufacturing and the transformation to digitalisation. Numerous ...manufacturers continue to explore ways to evaluate and enhance their capabilities to tackle market competition. This empirical study adopts a maturity model that is based on the Economic Development Board's Singapore Smart Industry Readiness Index. The model will allow companies to conduct self-assessments so that they can systematically and comprehensively align themselves with Industry 4.0. This study conducts a clustering analysis to explore the maturity of Industry 4.0 in Taiwan, the clustering's index factors, and the impact of key factors on companies' self-assessment. Classifying 80 Taiwan enterprises into four categories, it shows a significantly positive correlation among process, technology, and organisation. Further, a majority of the 80 companies tested under the maturity model still appear to be immature or partially mature, and needing much improvement and the re-evaluation of their transformation strategies related to Industry 4.0. Finally, the Singapore Smart Industry Readiness Index is suitable to conduct self-assessments in Taiwan-based enterprises. These findings could serve as useful maturity and grouping guidelines for practitioners and researchers.
Converging technology is the technology created by integrating at least two disparate fields of technologies with innovation potential, and its commercial applications open multiple business paths. ...Despite this, the management literature has paid little attention to the converging technology as firm-specific resources and its relationships with firm business diversification and performance. In the context of convergence in smart manufacturing, this study investigates the impact of firms’ converging technologies on the relationship between business diversification and financial performance. Hence, the study empirically analyzes a firm-level panel dataset combining financial and patent data of applicant firms in the field of smart manufacturing technologies for the period 2010–2015. The results show a U-shaped relationship between business diversification and financial performance at the early stage of the convergence paradigm. The joint effect of firms’ converging technologies and technological competencies reinforces this relationship by increasing benefits and mitigating costs at higher levels of business diversification. This result indicates that firms’ converging technologies create beneficial joint impacts with technological competencies on realizing value-added business diversification. The empirical findings present managerial implications for innovation management in the convergence paradigm.
•Converging technology leads to innovation creation in other technological fields.•The study introduces a new measure to identify converging technologies.•Converging technologies create beneficial impacts with technological competencies.•The joint impacts lead to increasing business diversification performance.•Portfolio management helps firm overcome convergence phenomenon challenges.
Driven by technological transformation, changing competency requirements are receiving increased attention. Technological developments, such as digitization, automation, and cyber-physical systems, ...will change occupational requirements. Additionally, many companies are already confronted with a shortage of a skilled workforce due to demographic change. Companies can satisfy their demand for skilled labor through training or hiring. Both options are expensive and require careful planning. However, human capital is often neglected in this financial planning process. The aim of the present research is to devise and test a novel tool for companies to better plan the current and future organizational and employee competencies, and to coordinate their human and financial resources. Employing a budget-allocation approach, our exploratory study is based on a survey of 228 human resource and production managers. Respondents provide monetary valuations of competencies required by mechatronics technicians in the face of new technological challenges of smart manufacturing systems. Results show that managers are willing to allocate a relatively high budget to competencies such as complex problem-solving, analytical thinking, and troubleshooting. In addition, domain-based knowledge remains essential and valuable. We also find some differences in the monetary valuation between human resource and production managers.
•Intuitive assessment method for monetary valuation of competencies•Case study on the value of competencies from blue-collar workers•Complex-problem solving in particular is seen as very valuable•Different disciplines value competency categories differently
Robotic cell scheduling problem with batch-processing machines (RCSP-BMs) needs to determine the processing sequence and the transferring sequence simultaneously. The buffer size before and after the ...batch-processing machines has a big influence on the scheduling solution. A big amount of energy is always consumed by batch-processing machines. Hybrid flow shop scheduling has been proven NP-hard, and the features of the batch-processing machines in a flow shop make the hybrid flow shop scheduling more difficult. This study proposes a multiobjective differential evolution (DE) algorithm to address these issues. First, a mathematical optimization model is formulated for the RCSP-BMs to minimize makespan and energy consumption of the batch-processing machines. Second, the multiobjective DE algorithm (MODE) is developed. A green scheduling algorithm is designed to decode the individuals to balance the makespan and energy consumption. A local search method is also presented to help the searching escape from the local optimum. Finally, experiments are carried out, and the results show that the MODE can solve the robotic cell scheduling problem with batch-processing machines effectively and efficiently. Note to Practitioners -This study focuses on the robotic cell scheduling problem with batch-processing machines (RCSP-BMs) and discusses the influence of the buffer sizes and different batching methods on scheduling. In this study, we propose a green scheduling algorithm and a multiobjective differential evolution algorithm to optimize the makespan and the energy consumption of the batch-processing machines simultaneously. In future research, we will address more complicated situations, such as many-objective optimization and many-robot scheduling.
