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This article aims to map the expectations of manufacturing companies towards suppliers in terms of implementing improvement activities. The article poses two research questions: RQ1: What kind of improvement of activities do the surveyed producers expect from their suppliers? RQ2: Do factors such as size, capital or implemented systems influence different assessments of the analyzed requirements toward suppliers?

The Computer Assisted Telephone Interview (CATI) technique was used to collect data. The sample consists of 150 producers (employing over 50 people) who were suppliers for enterprises from the automotive, electromechanical and chemical sectors operating in the Polish business-to-business (B2B) market. We analyzed 11 improvement activities, while their correlation structure was examined by exploratory factor analysis.

We have identified three latent factors – risk reduction, product innovation and increasing efficiency – which summarize the main expectations of manufacturing companies towards suppliers. Expectations for these factors are independent of the implemented management system, although the analysis showed higher expectations for product innovation in organizations with the implementation of Kaizen.

The article fills the research gap in the literature. The research results presented in the literature so far have focused on the expectations of enterprises towards suppliers in terms of meeting the criteria for their initial and periodic assessment. The research gap in the article is the result of empirical research presenting the expectations of manufacturers towards suppliers in terms of improving their processes. Based on the findings of the presented study, development trends and implications for managers responsible for purchasing processes and relationships with suppliers can be determined.

This study aims to investigate the intention to use blockchain technology (BT) in the context of supply chain (SC) operations through an integrated technology adoption framework using two well-known models, the unified theory of acceptance and use of technology (UTAUT) and the technology acceptance model (TAM). Moreover, the study looked at the direct effect of TAM and UTAUT elements on attitude toward BT, as well as the role of attitude toward BT as a mediator between TAM and UTAUT elements and intention to use BT.

The study used a quantitative research method, and a structured questionnaire was used to gather primary data. The final sample, drawn using a convenience sampling that consisted of 273 managers from the Jordanian manufacturing sector. Structural equation modeling statistical method was conducted using the Smart PLS program to test hypotheses in the proposed study framework.

The study has provided intriguing results. It found that two UTAUT elements, namely performance expectancy and social influence and one TAM element, namely perceived usefulness, have a significant impact on the attitude toward BT. Besides that, the study found that attitude toward BT significantly mediated the relationship between UTAUT-TAM elements and intention to use BT. The findings revealed that three elements namely performance expectancy, social influence and perceived usefulness have statistical significance on intention to use BT through the mediation of attitude. Finally, there is a direct significant positive relationship between the attitude toward BT and intention to use it.

The study helps decision-makers, South Carolina practitioners and academics recognize the fundamental factors that increase manufacturing firms’ intentions to use blockchain in their SCs. This gives decision-makers a better understanding of why users accept or reject BT, as well as how to improve user acceptability through technological design. Future studies should seek for a bigger sample size and use random sampling techniques. Furthermore, the study should be replicated in other industries or developing countries to validate the findings.

There is a scarcity of studies identifying the factors that increase blockchain adoption intention in SCM and developing countries. This study differs in that it examines BT intention to use in the context of SC using an integrated technology adoption framework that uses two well-known models, UTAUT and TAM, whereas other studies typically use only one model/theory. Moreover, given the importance of attitude in behavior, this study also investigated the effect of TAM-UTAUT elements on attitude toward BT, as well as the role of attitude toward BT as a mediator between TAM and UTAUT elements and intention to use BT.

The set of 2,509 documents related to the human-centric aspect of manufacturing were retrieved from Scopus database and systmatically analyzed. Using an unsupervised machine learning approach based on Latent Dirichlet Allocation we were able to identify latent topics related to human-centric aspect of Industry 5.0.

This study aims to create a scientific map of the human-centric aspect of manufacturing and thus provide a systematic framework for further research development of Industry 5.0.

In this study a 140 unique research topics were identified, 19 of which had sufficient research impact and research interest so that we could mark them as the most significant. In addition to the most significant topics, this study contains a detailed analysis of their development and points out their connections.

Industry 5.0 has three pillars – human-centric, sustainable, and resilient. The sustainable and resilient aspect of manufacturing has been the subject of many studies in the past. The human-centric aspect of such a systematic description and deep analysis of latent topics is currently just passing through.

The purpose of this paper is to develop a theoretical model that explains the impact of big data analytics capabilities (BDAC) on company's supply chain innovation capabilities and sustainable supply chain performance. BDAC is represented through two dimensions of big data technological capabilities (BDTC) and big data personal capabilities (BDPC). Moreover, the relationships between BDTC and BDPC with sustainable supply chain performance through the mediation effect of supply chain innovation capabilities are examined.

The study used a quantitative research approach. A survey of 400 Jordanian manufacturing companies was carried out to conduct this research. However, the responses of 207 managers were valid to be used in the analysis. In this study, the SmartPLS software was used to perform structural equation modeling using a partial least squares approach (PLS-SEM) and to examine the measurement and structural model's validity and reliability.

According to the results of this study, BDPC has a significant positive impact on supply chain innovation capabilities. Furthermore, the findings indicate that supply chain innovation capabilities are the most influential predictor of sustainable supply chain performance and act as a positive significant mediator in the relationship between BDPC and firm sustainable performance. Surprisingly, the study found that BDTC had no significant effect on supply chain innovation capabilities. Besides that, no significant relationship exists between BDTC and firm sustainable performance via the mediation effect of supply chain innovation capabilities.

This study provides an integrated research model that incorporates BDAC, supply chain innovation capabilities, and sustainable supply chain performance in order to analyze supply chain innovation and sustainable supply chain performance. This suggests that the scope of the study is broader in terms of predicting sustainable supply chain performance. As a result, the study intends to fill a gap in the literature by explaining how BDAC affects supply chain innovation capabilities and firms sustainable performance. In addition, the role of supply chain innovation capabilities as a mediator between BDAC and sustainable supply chain performance is investigated.

This chapter of the book seeks to use famous mathematical functions (statistical distribution functions) in evaluating and analyzing supply chain network data related to supply chain management (SCM) elements in organizations. In other words, the main purpose of this chapter is to find the best-fitted statistical distribution functions for SCM data. Explaining how to best fit the statistical distribution function along with the explanation of all possible aspects of a function for selected components of SCM from this chapter will make a significant attraction for production and services experts who will lead their organization to the path of competitive excellence. The main core of the chapter is the reliability values related to the reliability function calculated by the relevant chart and extracting other information based on other aspects of statistical distribution functions such as probability density, cumulative distribution, and failure function. This chapter of the book will turn readers into professional users of statistical distribution functions in mathematics for analyzing supply chain element data.