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This study aims to investigate the conflicting views in supply chain and strategic management literature regarding cooperative supply chain relationships (CSCR) and firm performance. Supply chain literature suggests a universally positive impact of CSCR on performance, irrespective of a firm’s strategy. In contrast, strategic management literature contends that the effectiveness of CSCR depends on their alignment with the firm’s competitive strategy. The research aims to clarify this disparity, offering insights into the strategic use of CSCR for enhancing firm performance.

This paper theorizes the integration of perspectives for the impact of CSCR on firm performance by examining the relationships considering the alignment of cost leadership and product differentiation strategies with supplier and customer relationships. Plant-level survey data is analyzed using regression techniques to test four hypotheses.

All four main relationships (cost leadership, product differentiation, supplier relationship and customer relationship) on firm performance are statistically significant. However, cost leadership firms are better aligned to their chosen strategy when they have strong relationships with suppliers, whereas similar relationships with customers create misalignment, negatively influencing firm performance. In contrast, product differentiators benefit by investing in relationships with customers rather than with suppliers.

A firm’s performance does not solely depend on its CSCR efforts but on aligning them with the firm’s overall strategy. Therefore, managers need to be cognizant of the firm’s competitive strategy when investing in CSCR. Failing to do so could negatively impact firm performance and, eventually, its ability to compete in the marketplace.

Scholars have advocated for the importance of examining competing perspectives of phenomena, both within and across various bodies of literature, as cross-disciplinary analysis often brings enhanced focus and depth, leading to improved understanding. This research is one of the initial efforts to empirically analyze the varying perspectives on CSCR in supply chain and strategic management literature. This cross-disciplinary approach can yield a more integrated perspective.

This chapter of the book aims to achieve sustainability and productivity in light of the interaction between managers and engineers in a lean and agile supply chain management system in today’s organizations. The main innovation of this chapter is the use of the balanced scorecard (BSC) model and fuzzy analysis network process (FANP) to create a suitable platform for the realization of this interaction between managers and engineers and to identify exactly which expert system is ideal for the main purpose. Indeed, this chapter introduces its readers to the application of strategic management tools such as the BSC accompanied by FANP in the elements of supply chain management where data analysis of lean and agile networks in supply chain management can create a competitive advantage in the organization.

This paper presents a new and well-structured framework that aims to assess the current environmental impact from a Greenhouse Gas (GHG) emissions perspective. This tool includes a new set of Lean Key Performance Indicators (KPIs), which translates the well-known logic of Overall Equipment Effectiveness in the field of GHG emissions, that can progressively detect industrial losses that cause GHG emissions and support decision-making for implementing improvements.

The new metrics are presented with reference to two different perspectives: (1) to highlight the deviation of the current value of emissions from the target; (2) to adopt a diagnostic orientation not only to provide an assessment of current performance but also to search for the main causes of inefficiencies and to direct improvement implementations.

The proposed framework was applied to a major company operating in the plywood production sector. It identified emission-related losses at each stage of the production process, providing an overall performance evaluation of 53.1%. The industrial application shows how the indicators work in practice, and the framework as a whole, to assess GHG emissions related to industrial losses and to proper address improvement actions.

This paper scrutinizes a new set of Lean KPIs to assess the industrial losses causing GHG emissions and identifies some significant drawbacks. Then it proposes a new structure of losses and KPIs that not only quantify efficiency but also allow to identify viable countermeasures.

The construction industry shows an increased interest in how to manage logistics within construction projects. Often construction logistics is outsourced to a logistics service provider (LSP). However, construction logistics is normally approached either as a strategic decision or as an operational issue and rarely as a tactical concern. The purpose of this study is to explore how to organize the logistics outsourcing decision at strategic, tactical and operational levels.

This study is performed as a single-case study within a construction corporation, containing (amongst others) a building contractor (BC) and a construction equipment rental company (CERC) offering logistics services.

The study shows that to procure construction logistics service successfully, BCs need logistics capabilities at strategic and tactical levels to maintain an alignment between the use of logistics services and operational characteristics. Simultaneously, CERC’s need to design their service offerings to correspond to the needs of the BC.

This study builds on a single-case study of a Swedish construction corporation. Further research is needed to better understand current logistics outsourcing and development practices and how these can be improved to foster better logistics management at the project level.

BCs find suggestions of different logistics organization structures and suitable outsourcing arrangements. CERCs and LSPs can use the findings to understand their customers’ needs and adapt service offerings.

To the best of the authors’ knowledge, this study is one of the first studies of how two companies within a corporation can work together to develop construction logistics service offerings.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

We consider a joint optimization problem of product platform design and scheduling on unrelated additive/subtractive hybrid machines, and seek to find efficient solution approaches to solve such problem.

We propose a mathematical formulation for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines, and develop a simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length to solve the problem.

The simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length (SAHH-osla) that we proposed can be quite efficient in solving the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

To the best of our knowledge, we are one of the first to consider both cost-related and time-related criteria for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

Supply chain disruptions have a significant impact on overall project delivery. This study aims to identify the supply chain disruptive factors and develop a framework to mitigate the disruptive effects on the supply chain. Covid-19 and Brexit disruption and their longevity effects in the short, medium and long term on the supply chain are relied upon to develop the framework.

The study adopted a mixed-method approach with a sequential explanatory design. The main disruptive factors were identified through a literature review, and key factors were selected through a focus group exercise. A questionnaire survey was carried out to sample opinions from the practitioners; 41 questionnaires were received and analysed using the relative importance index (RII) method for ranking the factors and percentage frequency distribution to determine the longevity effects. Five follow-up semi-structured interviews were conducted over the telephone and later transcribed.

The results of Covid-19 disruption indicate that material cost increase ranked first (RII: 0.863), logistics cost increase and supply chain interaction ranked second and third, respectively. They have long-term, medium-term and short-term longevity effects, respectively. The lowest-rated factors were communication (RII: 0.561), staff shortages (RII: 0.629) and impact on relationships (RII: 0.639). The three most ranked Brexit disruptive factors are supply chain interaction (RII: 0.775), material cost increase (RII: 0.766) and logistic and haulage delay (RII: 0.717). The first two factors have long-term effects, and the logistics and haulage delays have a medium-term impact. The mitigating solutions suggested in the framework are collaborative working, stronger resilience to external forces and better transparency and communication that will lead to good relationships among the supply chain members.

The scope of the study was limited to the UK construction industry; however, the pandemic effect on supply chain can serve as critical learning curve in other developed and developing countries.

The study will help the government and construction firms to understand the focal areas of importance in solving the supply chain disruption problems based on the effects of Brexit and Covid-19. The research would be useful in ensuring the proactive involvement of the government and contracting firms in their preparedness for similar events in the future. The results could be interpreted for critical learning in other developed/developing countries.

Identifying and ranking the supply chain disruptive factors affecting the small‐ and medium‐sized enterprises (SMEs) in the UK construction industry has been the focal point of this study. The study also proposes a simple but effective framework comprising the highly ranked factors, their longevity effects and mitigating measures. This will help the SMEs manage future/similar external events affecting the supply chain.

This paper proposes a new metric, named Operational Space Efficiency (OpSE), intended to diagnose and quantify the inefficient use of floor space for stocking materials in industrial workstations. OpSE presents a formulation analogous to the well-known Overall Equipment Effectiveness and can be obtained as the product of three distinct indicators: Standard Compliance Effectiveness, Standards Selection Effectiveness and Design Space-usage Effectiveness.

This indicator scrutinizes how usefully floor space in workstations is used to temporarily stock materials in the form of raw materials, semi-finished products, parts and components. It is suited for analyzing fixed-position layouts as well as product layouts typical of repetitive manufacturing settings, such as assembly lines in the automotive sector. The proposed indicator leverages an appropriate loss structure that features those factors affecting floor space utilization in workstations with regard to supplying and stocking materials.

An Italian manufacturer in the field of electro-technology was used as an industrial case study for the application of the methodology. The application shows how the three indicators work in practice, the effectiveness of OpSE and the methodology as a whole, in diagnosing floor space usage inefficiencies and in properly addressing improvement actions of the internal logistics in industrial settings.

The paper scrutinizes some important Key Performance Indicators (KPIs) dealing with space usage efficiency and identifies some significant drawbacks. Then it suggests a new, inclusive structure of losses and a KPI that not only measures efficiency but also allows to identify viable countermeasures.

Industry 4.0 has an inimitable potential to create competitive advantages for the apparel industry by enhancing productivity, production, profitability, efficiency and effectiveness. This study, thus, aims to assess the digitalisation level of the Tanzanian apparel industry through the Industry 4.0 perspectives.

A mixed-methods-based approach was deployed. This study deployed semi-structured interviews, document review and observation methods for the qualitative approach. For the quantitative approach, closed-ended questionnaires were used to ascertain the digitalisation levels and maturity level of the textiles and apparel (T&A) factories and small and medium-sized textile enterprises in Tanzania. The sample size was 110, with participants engaged through the purposive sampling technique.

Industry 4.0 frameworks evolved into practices mainly since 2011 in several service and manufacturing industries globally. For Tanzania, the findings indicate that the overall maturity level of the T&A industries is 2.5 out of 5.0, demonstrating a medium level of adoption. Thus, the apparel industries are not operating under the industry 4.0 framework; they are operating within the third industrial revolution – Industry 3.0 – framework. For such industries to operate within the fourth industrial revolution – Industry 4.0 – that is only possible if there is significantly well-developed industrial infrastructure, availability of engineering talent, stable commercial partnerships, demand from the marketplace and transactional relationship with customers.

This study’s limitations include: firstly, Industry 4.0 is an emerging area; this resulted in limited theoretical underpinnings in the Tanzanian perspectives. Secondly, the studied industries may not suffice the need to generalise the findings for the entire country, thus needing another study.

Although Industry 4.0 conceptual frameworks have been on trial in several industries since 2011, this is amongst the first empirical research on Industry 4.0 in the Tanzanian apparel industry that assesses the digitalisation levels.

The purpose of this study is to evaluate the efficiency of a Brazilian steelmaking company’s reheating process of the hot rolling mill.

The research method is a quantitative modeling. The main research techniques are data envelopment analysis, TOBIT regression and simulation supported by artificial neural networks. The model’s input and output variables consist of the average billet weight, number of billets processed in a batch, gas consumption, thermal efficiency, backlog and production yield within a specific period. The analysis spans 20 months.

The key findings include an average current efficiency of 81%, identification of influential variables (average billet weight, billet count and gas consumption) and simulated analysis. Among the simulated scenarios, the most promising achieved an average efficiency of 95% through increased equipment availability and billet size.

Additional favorable simulated scenarios entail the utilization of higher pre-reheating temperatures for cold billets, representing a large amount of savings in gas consumption and a reduction in CO2 emissions.

This study’s primary innovation lies in providing steelmaking practitioners with a systematic approach to evaluating and enhancing the efficiency of reheating processes.