Search results

This paper proposes an integrated value stream mapping (VSM) for a complex assembly line to improve the leanness of a complex automotive component manufacturing organization.

This study depicts the application of VSM at the case organization, where top management is concerned about the challenges of higher cycle time and lower productivity. Gemba walks were conducted to establish the concept of “walk the flow, create the flow” along the assembly line. The multi-hierarchical cross-functional team developed the current value stream map to know the “as-is” state. Then, the team analysed the current VSM and proposed the future VSM for the “to-be” state.

The integrated VSM shows different processes and work cells, various wastes, non-value-added activities, cycle time, uptime and the material and information flows for both products of the assembly line on the same map. The integrated VSM reduced cycle time, non-value-added activities, work in process inventory and improved line efficiency and production per labour hour for both the products, simultaneously.

The limitation of the study is that the study focussed only on the application of VSM for one complex assembly only. Future research may be conducted using the developed integrated VSM approach in other complex production environments.

Managers can identify and reduce system waste by incorporating the concept of integrated VSM in a complex production or assembly environment where two or more products are being manufactured/assembled with low similarity.

The application of VSM for assembly lines is highly challenging because of merging flows, a large number of child parts in the lines and assembly of more than one product on the same line.

Inventory models are quantitative ways of calculating low-cost operating systems. These models can be either deterministic or stochastic. A deterministic model hypothesizes variable quantities like demand and lead time, as certain. However, various types of research have revealed that the value of demand and lead time is still ambiguous and vary unanimously. The main purpose of this research piece is to reduce the uncertainties in such a dynamic environment of Industry 4.0.

The current study tackles the multiperiod single-item inventory lot-size problem with varying demands. The three lot sizing policies – Lot for Lot, Silver–Meal heuristic and Wagner–Whitin algorithm – are reviewed and analyzed. The suggested machine learning (ML)–based technique implies the criteria, when and which of these inventory models (with varying demands and safety stock) are best fit (or suitable) for economical production.

When demand surpasses a predicted value, variance in demand comes into the picture. So the current work considers these things and formulates the proper lot size, which can fix this dynamic situation. To deduce sufficient lot size, all three considered stochastic models are explored exclusively, as per respective protocols, and have been analyzed collectively through suitable regression analysis. Further, the ML-based Classification And Regression Tree (CART) algorithm is used strategically to predict which model would be economical (or have the least inventory cost) with continuously varying demand and other inventory attributes.

The ML-based CART algorithm has rarely been seen to provide logical assistance to inventory practitioners in making wise-decision, while selecting inventory control models in dynamic batch-type production systems.

The aim of this paper is to identify some specificities of production planning and control (PPC) activities in the one-of-a-kind-production (OKP) process through an extensive literature review. Relevant aspects related to systems and PPC activities in the context of OKP environment are discussed, and six opportunities for future research are highlighted.

The following research is based on a review of 53 articles published in peer-reviewed journals over the past three decades. After an initial descriptive analysis based on bibliometric indicators, a cluster analysis of 15 most cited articles was carried out using multivariate data analysis techniques and in-depth analysis.

The results reveal some specificities inherent to the clusters featured in the research, including aspects of planning, control and systems for OKP process. This cluster addresses information regarding next-generation manufacturing systems, scheduling and design science, computer simulation and project approach. On the other hand, the authors point out six topics for future research regarding contemporary issues associated with PPC in the context of OKP.

This paper fills an important gap regarding OKP production planning and control practices. The results provide a theoretical overview of different PPC practices suitable for the OKP environment. Furthermore, it can provide insights for scientific developments in order to manage the complexity inherent in the OKP process.

This editorial for the 6th World Conference on Production and Operations Management (P&OM) 2022 Special Issue delves into the transformative role of advanced artificial intelligence (AI)-driven chatbots in reshaping operations, supply chain management and logistics (OSCM). It aligns with the conference’s theme of exploring the intersection between P&OM and strategy during the Technological Revolution.

Utilizing a conceptual approach, this paper introduces the “ERI Framework,” a tool designed to evaluate the impact of AI-driven chatbots in three critical operational dimensions: efficiency (E), responsiveness (R) and intelligence (I). This framework is grounded in disruptive debottlenecking theory and real-world applications, offering a novel structure for analysis.

The conceptual analysis suggests immediate benefits of chatbots in enhancing decision-making and resource allocation, thereby alleviating operational bottlenecks. However, it sees challenges such as workforce adaptation and potential impacts on creativity and sustainability.

The paper suggests that while chatbots present opportunities for optimizing operational processes, organizations must thoughtfully address the emerging challenges to maintain productivity and foster innovation. Strategic implementation and employee training are highlighted as key factors for successful integration.

Bridging the gap between the burgeoning proliferation of chatbots and their practical implications in OSCM, this paper offers a first perspective on the role of AI chatbots in modern business environments. By providing insights into both the benefits and challenges of chatbot integration, it offers a preliminary view essential for academics and practitioners in the digital age.

This paper proposes the “fields and weapons of the competition model applied to business networks” – CAC-Redes (in Portuguese, Campos e Armas da Competição – Redes de negócio), an extension of the fields and weapons of the competition model (CAC) – to study the competition and competitiveness of companies operating in business networks in a competitive environment while integrating organizational competencies, interorganizational ties and company positioning to provide competitive advantage.

CAC-Redes is born from the cross-fertilization process of various theoretical perspectives, namely, industrial organization, traditional view of operational activities and resources, relational view, strategic alignment, transaction cost theory and social perspectives in networks, structured according to systems theory and under the mantle of competitive advantage theory. To discover the structure of existing models of competitiveness in networks, a bibliographic search was conducted in the Scopus database. Quali-quantitative empirical research was undertaken in companies from six different economic sectors through structured questionnaires and personal interviews to understand how companies competed and discover the determining factors of their competitive advantage.

Only seven models of competitiveness in network were found, and their structures and characteristics are quite different from those of CAC-Redes. Empirical research confirms all the hypotheses that support CAC-Redes, which, combined with those of CAC, indicate the CAC-Redes corroboration.

CAC-Redes does not apply to networks without intercompany competition, studies on network governance and corporate strategy formulation.

CAC-Redes is effective in studying complex competitiveness phenomena because it considers multiple influences; provides a process based on qualitative and quantitative variables that increase the probability of formulating successful competitive strategies; simplifies the differentiation of skills from core competencies and determines them; proposes a competitive advantage criterion to select suppliers; creates a unifying language to represent the different strategic specificities of companies, competitors, suppliers, customers and the company environment and provides a library containing 181 weapons (resources) and dozens of interorganizational ties that can be used in empirical studies with other methodologies.

CAC-Redes, due to its originality and peculiarities, theoretically contributes to theory of resources because it dispenses with the assumption, “unique resource, source of competitive advantage”; to relational view because it considers interorganizational relationships as a competence and treats it quali-quantitatively and to core competencies because if the strategy changes, different core competencies will be needed. Furthermore, it is an alternative to the dynamic capabilities perspective, and it transforms the five manufacturing performance objectives into nine for the entire company.

CAC-Redes is an original model because its structure and characteristics comparatively differ from those of existing models, and 14 singularities are detected.

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?

The purpose of this paper is to explore and disseminate knowledge about quantum-inspired computing technology's potential to solve complex challenges faced by the operational agility capability in Industry 4.0 manufacturing and logistics operations.

A multi-case study approach is used to determine the impact of quantum-inspired computing technology in manufacturing and logistics processes from the supplier perspective. A literature review provides the basis for a framework to identify a set of flexibility and agility operational capabilities enabled by Industry 4.0 Information and Digital Technologies. The use cases are analyzed in depth, first individually and then jointly.

Study results suggest that quantum-inspired computing technology has the potential to harness and boost companies' operational flexibility to enhance operational agility in manufacturing and logistics operations management, particularly in the Industry 4.0 context. An exploratory model is proposed to explain the relationships between quantum-inspired computing technology and the deployment of operational agility capabilities.

This is study explores the use of quantum-inspired computing technology in Industry 4.0 operations management and contributes to understanding its potential to enable operational agility capability in manufacturing and logistics operations.

The paper is a proof of concept (PoC) intervention study aimed for developing performance management (PM) practices in manufacturing small and medium-sized enterprises (SMEs) with the longer-term aim enabling the SMEs to improve their productivity. The intervention was designed and deployed by a collaborative quartet of academics, management consultants, accountancy firm and a commercial bank manager.

The paper firstly musters a set of initialising PM practices aligned to productivity improvement. These are utilised to design a knowledge transfer intervention for deployment with a set of manufacturing SMEs incorporating some associated productivity tools. The evaluation of the intervention utilised a case study approach founded on a logic model of the intervention to assess the development of the PM practices.

The intervention contributed to a partial development of the mustered practices and the productivity diagnostic based on the multi-factor productivity (MFP) abstraction and a data extraction protocol had the strongest impact. The study revealed the importance of the three interlaced factors: Depth of engagement, feedback opportunities and the intervention gradient (the increase of independent action from the participating SME's and the diminishment of the external intervention effort).

The case study is based on a limited number of individual SME's, and within just the manufacturing sector.

SME businesses will require a more sustained programme of interventions than this pilot to develop PM capability, and depth of engagement within the SME is critical. Professional stakeholders can be utilised in recruitment of firms for intervention programmes. Business can start developing PM capability prior to PMS implementation using the tools from this programme.

The productivity diagnostic tool, based on a synthesis of MFP and the performance pyramid, an array of potential initialising practices for PM capability and discovery of potential mechanisms for PM practice development.

An emerging body of literature has pinpointed the role of supply chain structure in influencing the extent to which supply chain members disclose information about their internal practices and performance. Nevertheless, empirical research investigating the effects of firm-level relational embeddedness on network-level transparency still lags. Drawing on social network analysis, this research examines the effect of relational embeddedness on supply chain transparency and the contingent role of digitalization in the context of environmental, social and governance (ESG) information disclosure.

In their empirical analysis, the authors collected secondary data from the Bloomberg database about 2,229 firms and 14,007 ties organized in 107 extended supply chains. The authors employed supplier and customer concentration metrics to measure relational embeddedness and performed multiple econometric models to test the hypothesis.

The authors found a positive effect of supplier concentration on supply chain transparency, but the effect of customer concentration was not significant. Additionally, the digitalization of focal firms reinforced the impact of supplier concentration on supply chain transparency.

The study findings contribute by underscoring the critical effect of relational embeddedness on supply chain transparency, extending prior literature on social network analysis, providing compelling evidence for the intersection of digitalization and supply chain management, and drawing important implications for practices.

This study is exploratory in nature and designed to address poorly documented issues in the literature. The dimensions of regional distribution or spatial organisation of Industry 4.0 (I4.0), including the potential role of clusters, have only recently been addressed, with most available studies focusing on advanced, mainly Western European countries. Although developing fast, the literature on I4.0 in other countries, such as the Central and Eastern European or post-transition economies like Poland, needs to pay more attention to the spatial distribution or geographical and organisational aspects. In response to the identified knowledge gap, this paper aims to identify the role of clusters in the transformation towards I4.0. This explains why clusters may matter for advancing the fourth digital transformation, how advanced in implementing I4.0 solutions are the residents of Polish clusters and how they perceive the advantages of cluster membership for such implementation. Finally, it seeks to formulate policy recommendations based on the evidence gathered.

The methodology used in this study combines quantitative analysis of secondary data from a cluster benchmarking survey with a case study approach. The benchmarking survey, conducted by the polish agency for enterprise development in 2021, gathered responses from 435 cluster members and 41 cluster managers, representing an estimated 57% of the current clusters in Poland. In addition to quantitative analysis, a case study approach was used, incorporating primary sources such as interview with cluster managers and surveys of cluster members, as well as secondary sources like company documents and information from cluster organisation websites. Statistical analysis involved assessing the relationship between technology implementation and the adoption of management systems, as well as exploring potential correlations between technology use and company characteristics such as revenue, export revenue share and number of employees using Pearson correlation coefficient.

In Poland, implementing I4.0 technologies by cluster companies is still modest. The cluster has influenced the use of I4.0 technologies in 23% of surveyed companies. Every second surveyed company declared a positive impact of a cluster on technological advancement. The use of I4.0 technologies is not correlated with the revenue of clustered companies. A rather bleak picture emerges from the results, revealing a need for more interest among cluster members in advancing I4.0 technologies. This may be due to a comfortable situation in which firms still enjoy alternative competitive advantages that do not force them to seek new advanced advantages brought about by I4.0. It also reflects the sober approach and awareness of associated high costs and necessary investments, which are paramount and prevent successful I4.0 implementation.

The limitations inherent in this study reflect the scarcity of the available data. This paper draws on the elementary survey administered centrally and is confined by the type of questions asked. The empirical section focuses on an important, though only one selected sector of the economy – the automotive industry. Nevertheless, the diagnosis of the Polish cluster’s role in advancing I4.0 should complement the existing literature.

The exploratory study concludes with policy recommendations and sets the stage for more detailed studies. Amidst the research’s limitations, this study pioneers a path for future comprehensive investigations, enabling a deeper understanding of Polish clusters’ maturity in I4.0 adoption. By comparing the authors’ analysis of the Polish Automotive Group (PGM) cluster with existing literature, the authors uncover a distinct disparity between the theoretical prominence of cluster catalysis and the current Polish reality. Future detailed dedicated enquiries will address these constraints and provide a more comprehensive map of Polish clusters’ I4.0 maturity.

This study identifies patterns of I4.0 implementation and diagnoses the role of clusters in the transformation towards I4.0. It investigates how advanced is the adoption of I4.0 solutions among the residents of Polish clusters and how they perceive the advantages of cluster membership for such transformation. Special attention was paid to the analysis of the automotive sector. Comparing the conclusions drawn from the analysis of the Polish PGM cluster in this case study to those from the literature on the subject, it becomes clear that the catalytic role of clusters in the implementation of I4.0 technologies by enterprises, as emphasised in the literature, is not yet fully reflected in the Polish reality.