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The purpose of this study is to develop a novel general mathematical model to find the optimal product mix of commercial graphite products, which has a complex production process with alternative sub-processes in the graphite mining production process.

The network optimization was adopted to model the complex graphite mining production process through the optimal allocation of raw graphite, byproducts, and saleable products with comparable sub-processes, which has different processing capacities and costs. The model was tested on a selected graphite manufacturing company, and the optimal graphite product mix was determined through the selection of the optimal production process. In addition, sensitivity and scenario analyses were carried out to accommodate uncertainties and to facilitate further managerial decisions.

The selected graphite mining company mines approximately 400 metric tons of raw graphite per month to produce ten types of graphite products. According to the optimum solution obtained, the company should produce only six graphite products to maximize its total profit. In addition, the study demonstrated how to reveal optimum managerial decisions based on optimum solutions.

This study has made a significant contribution to the graphite manufacturing industry by modeling the complex graphite mining production process with a network optimization technique that has yet to be addressed at this level of detail. The sensitivity and scenario analyses support for further managerial decisions.

The rapid, yet low-profit, expansion of the production capacity of state-owned enterprises (SOEs) represents a remarkable phenomenon. However, the motivation behind this key operational decision remains underexplored, especially concerning the prioritization of sociopolitical and financial goals in operations management. Drawing on the multiple-goal model in the behavioral theory of the firm (BTOF), the authors' study aims to examine how SOE capacity expansion is driven by performance feedback regarding the sociopolitical goal of employment provision and how SOEs differently prioritize sociopolitical and financial goals based on negative versus positive feedback on the sociopolitical goal.

The authors' study uses panel data on 826 Chinese SOEs in manufacturing industries from 2011 to 2019. The authors employ the fixed-effects model with Driscoll–Kraay standard errors, which are robust to heteroscedasticity, autocorrelation and cross-sectional dependence.

The authors find that SOEs increase capacity expansion as sociopolitical feedback becomes more negative, but they may not increase capacity expansion in response to positive sociopolitical feedback. Moreover, negative profitability feedback strengthens SOEs' capacity expansion in response to negative sociopolitical feedback. In contrast, negative profitability feedback weakens their response to positive sociopolitical feedback.

The authors' study offers a novel behavioral explanation of SOEs' operational decisions regarding capacity expansion. While the literature has traditionally assumed multiple goals as either hierarchical or compatible, the authors extend the BTOF's multiple-goal model to illuminate when firms pursue sociopolitical and financial goals as compatible (i.e. the activation rule) versus hierarchical (i.e. the sequential rule), thereby reconciling their tension in distinct performance situations. Practically, the authors provide fine-grained insights into how operations managers can prioritize multiple goals when making operational decisions. The authors' study also shows how policymakers can influence SOE operations to pursue sociopolitical goals for public benefit.

This paper analyzes the ways in which accounting enables operations managers to enter and perform multiple roles in their interplay with organizational groups on the shop floor and in management, and the associated negotiations that operations managers have with “the self.”

Using field-based studies in a mining organization, the study draws on Goffman’s backstage–frontstage metaphor to analyze how operations managers enter and perform several roles with the aid of accounting.

The findings show that accounting legitimizes operations managers when they cross organizational boundaries, as accounting gives them an “entry ticket” that legitimizes their presence with the group. Accounting further allows operations managers to embrace more than one role by “putting on a mask” to become an outsider or insider in relation to a group. In performing their roles, operations managers exhibit varying attributes and knowledge. Accounting can thereby be withheld from, or shared with, organizational groups. The illusion of accounting as deterministic presented frontstage is not necessarily negotiated that way backstage. Rather, alternatives discussed backstage often become silenced in the frontstage performance. The study concludes that operations managers cross boundaries, embrace roles and exert agency as they navigate with accounting, enrolling it into their performance simultaneously as they backstage reflect upon accounting and its role for their everyday work.

This study relies on the frontstage/backstage metaphor to visualize the discrepancies in how accounting is enrolled into role performances and how seemingly categorical fronts do not necessarily share that dominant position backstage.

Production systems occupy geographically dispersed organizations with limited visibility and transparency. Such limitations create operational inefficiencies across the Supply Chain (SC). Recently, researchers have started exploring applications of Digital Twins Technology (DTT) to improve SC operations. In this context, there is a need to provide comprehensive theoretical knowledge and frameworks to help stakeholders understand the adoption of DTT. This study aims to fulfill the research gap by empirically investigating DTT readiness to enable transparency in SC.

A comprehensive literature survey was conducted to develop a theoretical model related to Supply Chain Transparency (SCT) and DTT readiness. Then, a questionnaire was developed based on the proposed theoretical model, and data was collected from Indian manufacturers. The data was analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) to confirm the proposed relationships.

The findings from the study confirmed a positive relationship between DTT implementation and SCT. This study reported that data readiness, perceived values and benefits of DTT, and organizational readiness and leadership support influence DTT readiness and further lead to SCT.

This study contributes to the literature and knowledge by uniquely mapping and validating various interactions between DTT readiness and sustainable SC performance.

The effectiveness of the Just-in-Time (JIT) production system in non-Japanese contexts is a topic of diverse findings. This study conducts a meta-analysis of empirical research on JIT and its relationship with performance, focusing on studies published since 1995. Additionally, it examines the moderating influence of National Culture (NC) values on JIT outcomes.

A total of 59 empirical studies with 211 effects and 17,008 observations from 18 countries are meta-analyzed. A meta-regression using hierarchical linear modeling (HLM) is performed to explore how four dimensions of National Culture (NC) moderate the impact. (viz. institutional collectivism, uncertainty avoidance future orientation, and power distance,) based on the Global Leadership and Organizational Behavior Effectiveness (GLOBE) culture model.

The meta-analysis results show that improved production efficiency, product quality and reduced wastes achieved through JIT deployments translate into the overall performance of organizations. The meta-regression results shed light on how local cultures influence the effectiveness of JIT across different countries.

The findings of meta-analysis have implications for multinational manufacturers in realizing efficacy of JIT. The research adds to the international operations management literature by examining how NC values influence strategies and decisions in operations management.

This study examines the case of an initial training, called “Dojo”, invented and implemented at a production site in the Czech Republic. It clarifies the initial training program implementation process and offers a conceptual framework for cooperative management of subsidiary activities at the site and firm.

This study conducts an in-depth analysis of qualitative data from the Czech production site over a five-year period. The theoretical base is the theorization and labeling phase of management innovation (MI), the final phase of which legitimizes a new management practice. Interview data, archival data, pictures and financial data are used for the analysis.

To legitimize the Dojo in the operational flow controlled by the site and firm, the Czech production site acquires validation of the Dojo from employees and board members of the Japanese and European headquarters, helping the site build trustful relationships with them. Training programs, process standardization and skills standardization of the workers offer benefits to the trainees, production site and firm.

The authors offer theoretical insights into MI at the subsidiary-level, which past studies have not differentiated at the firm-level. The authors also provide details of the implementation and management of initial training for newly hired blue-collar workers at the production site. The findings complement related literature on human resource management and operational management.

This paper aims to investigate the impacts of Industry 4.0/5.0 (I4.0/5.0) on the glass manufacturing workplace. Specifically, it studied the workplace, which represents complex manufacturing lines of high variety and volume of products.

A case study based on semi-structured interviews was conducted with managers responsible for I4.0 implementation, and the responses were treated by content analysis.

Findings reinforce I5.0 aspects to be considered in terms of work organization. The interviewees highlight work and human factors as important for technology implementation, which includes workers tasks, skills, nature of work, human resources development, hiring process and organizations strategies. It was also found that knowledge sharing poses a huge challenge.

In lieu of gaps in the literature, this research further discusses management challenges to support digital transformation and impacts on workers and organizations.

This study applies organizational information processing theory (OIPT) to investigate managing production process variability in uncertain environments using information technology (IT) capabilities.

We conduct an empirical analysis using the three-stage least squares (3SLS) technique on 1,612 manufacturing firms over ten years.

The findings show that IT capability plays a dual role: it reduces the positive impact of environmental uncertainty on production process variability and mitigates the negative relationship between production process variability and operational performance.

Our findings suggest that managers should focus on reducing production process variability by strengthening their firms' IT capabilities. This is particularly crucial in volatile environments where external uncertainties can significantly impact operational processes.

Variability in the production process is a significant source of inefficiency and disruption within business processes. Using OIPT, our study contributes to the field by empirically analyzing the role of IT capabilities in reducing production process variability under environmental uncertainty.

The study aims to synthesize existing knowledge and proposes a research framework for building a resilient supply chain (SC) through artificial intelligence (AI) technology. It also identifies existing literature gaps and paves the way for a future research agenda.

A systematic literature review has been carried out to identify the peer-reviewed articles from Scopus and Web of Science databases. Then, the selected articles published between 2012 and 2023 are analyzed using descriptive and thematic analysis methods to unearth research gaps and offer new research directions.

Descriptive and thematic analysis reveals the overall development of literature on the role of AI for supply chain resilience (SCR). Based on the findings of the thematic analysis, the motivation, application, capability and outcome (MACO) framework has been developed and propositions have been proposed. Several future research directions have also been suggested in terms of theory, context and methodology (TCM).

The study provides a fresh perspective on the integration of AI technology within the realm of SCR. The developed MACO framework serves as a practical tool for supply chain management (SCM) professionals, offering a nuanced understanding of AI's applications across various functional areas to streamline operations, minimize waste and optimize resource utilization, thereby helping them in strategic planning.

This study contributes to the literature on the role of AI for building SCR by uncovering gaps, offering research directions and developing propositions for future research directions.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.