Overcoming Barriers in Supply Chain Analytics—Investigating Measures in LSCM Organizations

While supply chain analytics shows promise regarding value, benefits, and increase in performance for logistics and supply chain management (LSCM) organizations, those organizations are often either reluctant to invest or unable to achieve the returns they aspire to. This article systematically explores the barriers LSCM organizations experience in employing supply chain analytics that contribute to such reluctance and unachieved returns and measures to overcome these barriers. This article therefore aims to systemize the barriers and measures and allocate measures to barriers in order to provide organizations with directions on how to cope with their individual barriers. By using Grounded Theory through 12 in-depth interviews and Q-Methodology to synthesize the intended results, this article derives core categories for the barriers and measures, and their impacts and relationships are mapped based on empirical evidence from various actors along the supply chain. Resultingly, the article presents the core categories of barriers and measures, including their effect on different phases of the analytics solutions life cycle, the explanation of these effects, and accompanying examples. Finally, to address the intended aim of providing directions to organizations, the article provides recommendations for overcoming the identified barriers in organizations

Data and Predictive Analytics Use for Logistics and Supply Chain Management

Purpose The purpose of this paper is to explore the social process of Big Data and predictive analytics (BDPA) use for logistics and supply chain management (LSCM), focusing on interactions among technology, human behavior and organizational context that occur at the technology’s post-adoption phases in retail supply chain (RSC) organizations. Design/methodology/approach The authors follow a grounded theory approach for theory building based on interviews with senior managers of 15 organizations positioned across multiple echelons in the RSC. Findings Findings reveal how user involvement shapes BDPA to fit organizational structures and how changes made to the technology retroactively affect its design and institutional properties. Findings also reveal previously unreported aspects of BDPA use for LSCM. These include the presence of temporal and spatial discontinuities in the technology use across RSC organizations. Practical implications This study unveils that it is impossible to design a BDPA technology ready for immediate use. The emergent process framework shows that institutional and social factors require BDPA use specific to the organization, as the technology comes to reflect the properties of the organization and the wider social environment for which its designers originally intended. BDPA is, thus, not easily transferrable among collaborating RSC organizations and requires managerial attention to the institutional context within which its usage takes place. Originality/value The literature describes why organizations will use BDPA but fails to provide adequate insight into how BDPA use occurs. The authors address the “how” and bring a social perspective into a technology-centric area

Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains. Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin. Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed. Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

A Proposal for Supply Chain Management Research That Matters: Sixteen High Priority Research Projects for the Future

On May 4th, 2016 in Milton, Ontario, the World Class Supply Chain 2016 Summit was held in partnership between CN Rail and Wilfrid Laurier University’s Lazaridis School of Business & Economics to realize an ambitious goal: raise knowledge of contemporary supply chain management (SCM) issues through genuine peer-­‐to-­‐peer dialogue among practitioners and scholars. A principal element of that knowledge is an answer to the question: to gain valid and reliable insights for attaining SCM excellence, what issues must be researched further? This White Paper—which is the second of the summit’s two White Papers—addresses the question by proposing a research agenda comprising 16 research projects. This research agenda covers the following: The current state of research knowledge on issues that are of the highest priority to today’s SCM professionals Important gaps in current research knowledge and, consequently, the major questions that should be answered in sixteen future research projects aimed at addressing those gaps Ways in which the research projects can be incorporated into student training and be supported by Canada’s major research funding agencies That content comes from using the summit’s deliberations to guide systematic reviews of both the SCM research literature and Canadian institutional mechanisms that are geared towards building knowledge through research. The major conclusions from those reviews can be summarized as follows: While the research literature to date has yielded useful insights to inform the pursuit of SCM excellence, several research questions of immense practical importance remain unanswered or, at best, inadequately answered The body of research required to answer those questions will have to focus on what the summit’s first White Paper presented as four highly impactful levers that SCM executives must expertly handle to attain excellence: collaboration; information; technology; and talent The proposed research agenda can be pursued in ways that achieve the two inter-­‐related goals of creating new actionable knowledge and building the capacity of today’s students to become tomorrow’s practitioners and contributors to ongoing knowledge growth in the SCM field This White Paper’s details underlying these conclusions build on the information presented in the summit’s first White Paper. That is, while the first White Paper (White Paper 1) identified general SCM themes for which the research needs are most urgent, this White Paper goes further along the path of industry-academia knowledge co-creation. It does so by examining and articulating those needs against the backdrop of available research findings, translating the needs into specific research projects that should be pursued, and providing guidelines for how those projects can be carried out

Digital maturity variables and their impact on the enterprise architecture layers

This study examines the variables of digital maturity of companies. The framework for enterprise architectures Archimate 3.0 is used to compare the variables. The variables are assigned to the six layers of architecture: Strategy, Business Environment, Applications, Technology, Physical and Implementation and Migration. On the basis of a literature overview, 15 “digital maturity models” with a total of 147 variables are analyzed. The databases Scopus, EBSCO – Business Source Premier and ProQuest are used for this purpose

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

Contextual impacts on industrial processes brought by the digital transformation of manufacturing: a systematic review

The digital transformation of manufacturing (a phenomenon also known as "Industry 4.0" or "Smart Manufacturing") is finding a growing interest both at practitioner and academic levels, but is still in its infancy and needs deeper investigation. Even though current and potential advantages of digital manufacturing are remarkable, in terms of improved efficiency, sustainability, customization, and flexibility, only a limited number of companies has already developed ad hoc strategies necessary to achieve a superior performance. Through a systematic review, this study aims at assessing the current state of the art of the academic literature regarding the paradigm shift occurring in the manufacturing settings, in order to provide definitions as well as point out recurring patterns and gaps to be addressed by future research. For the literature search, the most representative keywords, strict criteria, and classification schemes based on authoritative reference studies were used. The final sample of 156 primary publications was analyzed through a systematic coding process to identify theoretical and methodological approaches, together with other significant elements. This analysis allowed a mapping of the literature based on clusters of critical themes to synthesize the developments of different research streams and provide the most representative picture of its current state. Research areas, insights, and gaps resulting from this analysis contributed to create a schematic research agenda, which clearly indicates the space for future evolutions of the state of knowledge in this field

A maturity model for digital transformation

Various disruptive digital technologies such as IoT, Artificial Intelligence, Big Data and Analytics, 3D Printing, Augmented Reality, Cybersecurity, Clouding, Autonomous Robots, Simulation, Horizontal and Vertical Integration challenge the traditional means of doing business. Also, factors such as fierce price competition due to price transparency, the increased demand for customized product and services, and so on force organizations to adapt to the rapid change in digital technologies. However, many companies have doubts about where to start their transformation process. In order to develop a roadmap, the companies should first identify their weaknesses and strengths in the context of digital transformation. Maturity models have been used for years in order to identify companies’ weaknesses and strengths. In this work, we proposed one such maturity model to determine the current readiness level of the companies in the context of digital transformation. Overall, we defined 5 dimensions and developed/adopted 76 questions for assessing digital maturity. These dimensions include “Leadership”, “Strategy”, “People”, Partnership and Resources”, and “Product, Process, and Services”. The proposed maturity model is among the few models that are based on sound theoretical frameworks already validated in earlier studies and/or widely used in the literature and practic