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The main objective of this study is to present the practical application of the Theory of Constraints (TOC) and its Thinking Process Tools (TPT) to identify, analyze, and eliminate constraints in the production department of a machinery company specializing in the production of bearing cages. A case study was used as the research method. Interviews, direct observations, and performance indicator analysis were used to identify the most critical constraints in the production department of the studied company. The research included the application of five TOC-TPT tools: Goal Tree (GT), Current Reality Tree (CRT), Evaporating Cloud (EC), Future Reality Tree (FRT), and Prerequisite Tree (PRT). These tools were used to identify organizational goals, diagnose the root causes of key constraints, resolve internal conflicts, design future solutions, and outline the path for implementing solutions. The study focused on an in-depth analysis of the key constraint in the form of the lack of work instructions at each stage of production. The analysis revealed that the root causes of this limitation were insufficient investment in training and management development. Determining a dedicated budget and resources for a structured, ongoing training and development program that aligns with the company’s strategic goals and develops both technical and interpersonal skills at all levels of staff was identified as a critical solution to this problem. The advantages and limitations of using TOC-TPT were also analyzed in response to comments from the working group participating in the study. The results showed the effectiveness of TOC-TPT in solving complex operational problems in the study company and the technical and organizational problems associated with the use of TOC thinking tools. The study indicated that strategic commitment of top management, cross-functional teamwork, and targeted training are key to the successful implementation of TOC tools. The results have practical implications for manufacturing companies seeking to improve the effectiveness and efficiency of their production systems through comprehensive constraint management.

Supported by the principal-agent theory, higher-order theory, and financing constraint theory, this study aimed to examine the effect of disciplinary inspection commission (DIC) participation in BG on Chinese state-owned firms' financing constraints. This relationship was also compared between state-owned and private firms, both of which are striving towards a circular economy. Data from a sample of A-share listed Chinese firms on the capital market from 2013 to 2020 was put through regression analysis and robustness testing. The analyses revealed that the involvement of DICs in BG has more a detrimental effect on private firms' financing constraints. These findings are helpful for enterprises to rationally allocate and play the role of the DIC based on specific scenarios. The findings further provide an empirical basis for government departments and financial institutions to develop circular policies to alleviate corporate financing difficulties.

The Internet of Things (IoT) has become critical to the implementation of Industry 4.0. The successful operation of smart manufacturing depends on the ability to connect everything together. In this research, we applied the TOC (Theory of Constraints) to develop a wireless Wi-Fi intelligent programmable IoT controller that can be connected to and easily control PLCs. By applying the TOC-focused thinking steps to break through their original limitations, the development process guides the user to use the powerful and simple flow language process control syntax to efficiently connect to PLCs and realize the full range of IoT applications. Finally, this research uses oil–water mixer equipment as the target of continuous improvement and verification. The verification results meet the requirements of the default function. The IoT controller developed in this research uses a marine boiler to illustrate the application. The successful development of flow control language by TOC in this research will enable academic research on PLC-derivative applications. The results of this research will help more SMEs to move into smart manufacturing and the new realm of Industry 4.0.

Purpose The purpose of this paper is to know which growth-impeding constraints are perceived to act upon operations of small- to medium-sized (SME) companies by their owner-managers and to recommend transitionary paths to elevate constraints and increase contribution levels made by SMEs’ operations. To do so, this research has been primarily founded upon Hayes et al.’s (2005) operations contribution model for differentiating between different levels of operations’ contribution, and secondarily on the theory of constraints philosophy to explain the perceptions of constraints found at each level – current and future. Design/methodology/approach An open-ended survey and a series of group workshops have gathered new empirical data about these perceptions, which were coded using the relational content analysis to identify a parsimonious set of perceptual growth-impeding constraint categories. The most popular transitions were identified and a correlation of frequency rank orders between “perceived current” and “perceived future” constraints categories was calculated, and likely transitionary paths for growth are discussed. Three SME case studies were documented in related action research to contextualise survey findings. Findings The most popular transition was from “neutral” to “leading”. A lack of people capability was perceived to be the most commonly reported growth-impeding constraint category, followed by a combined lack of process competence and product and service innovation, further followed by a lack of skills in information technology automation. In addition, a new conceptual model has been generated inductively to address shortcomings found in the original operations contribution model (Hayes et al., 2005) during its application to UK SMEs. The new model is referred to in this paper as the “Operations Growth Rocket”. Research limitations/implications This research only used data from UK SMEs. Practical implications This work should help SME owner-managers to overcome growth-impeding constraints that act upon their operations and assist them to develop more effective actions and paths to increase the contribution levels made by their operations. This in turn should support growth of their organisations. Findings will also inform teaching about more effective operations management in SMEs. Social implications This work should help UK SMEs to grow, which in turn will strengthen the UK economy. Originality/value A novel approach and new data from 208 SMEs modify a classical operations contribution model (Hayes et al., 2005). This is achieved by considering transitionary paths to be meta-categories continua abstracted from constraint categories combined with case data for moving towards higher levels of operations contribution, rather than using discrete growth-impeding and growth-constraining “levels”. This research has inductively generated a new version of the classical contribution model that should be more suitable for stimulating growth in (UK) SMEs.

Artificial intelligence (AI) has traditionally been used for quantitative analysis using explicit data. However, much of the information critical to decision making remains undocumented and is not stored in a structured way. This study explores the integration of AI, specifically ChatGPT, into Theory of Constraints (TOC) Thinking Process (TP) tools.In this study, we applied ChatGPT to a real-world IT project management case using a variety of research methods, including international literature analysis, observation, and personal experience. The use of the TOC TP allowed us to understand the decision-making process of ChatGPT and to systematically explore its advantages and limitations in creating logical trees of TOC TP.ChatGPT significantly enhanced efficiency and depth in TOC TP data collection and analysis, effectively addressing logical leaps for more coherent structures. It also promoted deeper analytical thinking and aided root cause identification. The integration of ChatGPT into the TOC TP process led to faster decision-making, reduced bias, and clearer analysis. Challenges of ChatGPT including the need for human oversight, specific TOC TP training, and ethical considerations were noted.This study provides an initial investigation into the use of ChatGPT in TOC TP tools. The results suggest that ChatGPT has the potential to be a valuable tool for organizations seeking to improve their decision making and performance. However, further research is needed to validate these findings and explore the full potential of AI in TOC TP.

PurposeDrum-Buffer-Rope (DBR) is a method to adjust production flows, synchronize the release of materials and enable a process of focused improvement in production systems. Literature on DBR applications in engineer-to-order (ETO) production systems, where customers participate in product design decisions and, consequently, in the way production is planned and executed, is rare. However, the interest in improving production management in ETO systems has received attention from the scientific and business communities. The goal of this research was to evaluate the implementation of DBR in an ETO productive system, critically analyzing the necessary adaptations for its use.Design/methodology/approachThis research was conducted through a case study in a company that manufactures electronic equipment, known as avionics, in the aerospace sector.FindingsIn this context, the contribution of this study consists of evaluation of the implementation of DBR in an ETO productive system, describing the implementation and the necessary adaptations of the DBR to the ETO productive system explored, comparing it with the DBR theoretical proposals and Simplified Drum-Buffer-Rope (S-DBR) methods.Originality/valueThe study contributes to knowledge by expanding the field of the DBR application to make it more precise, and by applying the theory of constraints, in a general manner, to this type of productive environment.

This study proposes a novel approach to support sustainable decision-making in knitted shoe manufacturing by integrating activity-based costing (ABC), the theory of constraints (TOC), and carbon emission costs into a comprehensive mathematical programming model. The model is applied to evaluate the impact of different carbon tax and carbon trading policies on the profitability and product mix of a knitted shoe company in Taiwan. The model considers single-period and multi-period scenarios, as well as continuous and discontinuous carbon tax functions, with and without carbon trading. The results show that a continuous carbon tax leads to higher profitability in single-period models, while a continuous carbon tax function combined with carbon trading yields the highest profits in multi-period models. Reducing the carbon emission cap is found to be more effective in curbing emissions than raising carbon taxes. This research contributes to sustainable operations management by providing a holistic approach that integrates cost control, profit optimization, and environmental sustainability in the context of Industry 4.0. The findings offer valuable insights for footwear manufacturers in making strategic decisions and for governments in designing effective carbon tax and emission trading schemes to drive industrial transformation towards a low-carbon economy.

Efficient inventory management, including optimal safety-stock levels, is crucial for operational continuity and cost-effectiveness in various industries. This study seeks the optimal inventory management strategy to minimize costs and determine ideal safety-stock levels. It compares five approaches: the company’s (STAR) current “number of days” method, two alternative models from the literature (the theory of constraints (TOC) replenishment model and the service-level approach), and two newly developed hybrid methodologies (the TOC replenishment model with ABC–XYZ classification and the service-level approach with ABC–XYZ classification). The analysis focused on financial performance, considering inventory holding and shortage costs. Monthly production plans were established and fixed as constant based on predetermined optimum month-end inventory levels derived from each method. Through simulation, actual month-end inventory levels were assessed, comparing total inventory costs (TICs). While unit holding costs (UHCs) were documented in financial records in the company, unit shortage costs (USCs) were not; thus, USCs were examined in three scenarios. The results show that the second proposed hybrid model consistently outperformed the other four methods, including the company’s current approach, significantly reducing TIC. The analysis emphasizes the importance of demand variation in setting safety stocks and demonstrates the second hybrid methodology’s effectiveness in optimizing safety-stock strategies and improving overall inventory management efficiency.

In the last 20 years, with the liberalization of the economy, and the trend of industrial globalization, people have gradually paid more attention to environmental protection. With the tremendous advances in information technology, enterprises facing such a severe impact on the business operations, business administrative models must be innovative and adaptable in order to survive and flourish. The paper industry is not only a highly polluting industry, but in the case of long-term overcapacity, the price of paper products is often suppressed, which lowers profitability. The purpose of this study, which is based on the production data of a paper company, is to pose a mathematical programming decision model which integrates green manufacturing technologies, activity-based costing (ABC), and the theory of constraint (TOC); this model should assist in preparing the best production plans, and achieve the optimal profitable product mix. In addition, this study also proposes that the most popular related technologies developed by Industry 4.0 be applied to production control in recent years in order to enhance production efficiency and quality. The findings of this study should contribute to the improvement of the competitiveness of the paper industry, and provide insights into the value of an integrated mathematical programming model applied for product-mix decision. At the same time, we have also applied the related technologies developed by Industry 4.0 to machine maintenance and quality control in manufacturing workshops. With its tremendous benefits, we can actively arouse the industry’s understanding of, and attention to, Industry 4.0, thereby increasing the interest in industrial 4.0-related technology investments.

Several authors agree on the importance of integrating sales and operations (S & OP) when it comes to improving the management of organisations. Thus, the relationship between S & OP can be critical from a competitive point of view, and therefore strategic. However, most of the literature on this topic focuses on the field of repetitive manufacturing. Consequently, this work aims to enhance the scarce literature related to S & OP in organisations that produce make-to-order (MTO). The present study is an analysis of cases of two industrial companies managed according to the theory of constraints. In both cases, the relationship between sales and operations was initially poor. Their approaches were mainly founded on their experience and intuition, as well as on simple data. The development of a new approach, integrating S & OP, has demonstrated the potential advantages of integrated management. As a result, we have identified some aspects of interest from a managerial perspective. Furthermore, we consider the findings of this study to be relevant to improving the competitiveness of this kind of organisation.