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

From big data to big performance – exploring the potential of big data for enhancing public organizations’ performance : a systematic literature review

This article examines the possibilities for increasing organizational performance in the public sector using Big Data by conducting a systematic literature review. It includes the results of 36 scientific articles published between January 2012 and July 2019. The results show a tendency to explain the relationship between big data and organizational performance through the Resource-Based View of the Firm or the Dynamic Capabilities View, arguing that perfor-mance improvement in an organization stems from unique capabilities. In addition, the results show that Big Data performance improvement is influenced by better organizational decision making. Finally, it identifies three dimensions that seem to play a role in this process: the human dimension, the organizational dimension, and the data dimension. From these findings, implications for both practice and theory are derived

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

Critical business intelligence practices to create meta-knowledge

In order to successfully implement strategies and respond to business variations in real-time, business intelligence (BI) systems have been deployed by organisations that assist in focused analytical assessments for execution of critical decisions. Although businesses have realised the significance of BI, few studies have explored their analytical decision-enabling capabilities linked to organisational practices. This study investigates the BI practices critical in creating meta-knowledge successfully for strategy-focused analytical decision-making. First, key BI suppliers are interviewed to develop an understanding of their BI capabilities and current deployment practices. Subsequently, two large BI implementation case studies are conducted to examine their practices in data transformation process. Findings reveal that BI practices are highly context-specific in mapping decisions with data assets. Complimentary static and dynamic evaluations provide holistic intelligence in predicting and prescribing a more complete picture of the enterprise. These practices vary across firms in their effectiveness reflecting numerous challenges and improvement opportunities.Publishe

Big data-savvy teams’ skills, big data-driven actions and business performance

Prior studies on big data analytics have emphasized the importance of specific big data skills and capabilities for organizational success; however, they have largely neglected to investigate the use of cross-functional teams’ skills and its links to the role played by relevant data-driven actions and business performance. Drawing on the resource-based view (RBV) of the firm and on the data collected from big data experts working in global agrifood networks, we examine the links between the use of big data-savvy (BDS) teams’ skills, big data-driven (BDD) actions and business performance. BDS teams depend on multidisciplinary skills (e.g., computing, mathematics, statistics, machine learning, and business domain knowledge) that help them to turn their traditional business operations into modern data-driven insights (e.g., knowing real time price changes and customer preferences), leading to BDD actions that enhance business performance. Our results, raised from structural equation modelling, indicate that BDS teams' skills that produce valuable insights are the key determinants for BDD actions, which ultimately contribute to business performance. We further demonstrate that those organisations that emphasise BDD actions perform better compared to those that do not focus on such applications and relevant insights

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

Factors Influencing Willingness To Adopt Advanced Analytics In Small Businesses

Business analytics (BA) continues to be one of the top technology trends in recent years as well as one of the top priorities for CIO’s in many large enterprises. Business analytic tools can significantly help small businesses in quickly responding to changing market conditions and improving their organizational performance. However, prior studies report that the adoption rate of business analytics in small businesses is extremely low such that only 32 percent small businesses have adopted Business Intelligence (BI) and analytics solutions till now (SMB Group, 2018). As small businesses constitute a major force in the US economy, a slow rate of adoption of significant technological innovations, such as BA, may be a critical concern that can affect the economy in the longer run. Despite this, the extant small business literature as well as the information systems literature fails to provide an understanding of why small businesses are not receptive to current BA trends. Therefore, drawing upon the theoretical underpinnings of organizing vision theory, strategic orientation literature, and theory of upper echelon, this study investigates the willingness of small businesses to adopt newer innovations in BA. More specifically, this study investigates the impact of the reception of organizing vision of BA by owner managers, learning orientation of small businesses, analytics orientation of small businesses, and personal characteristics of owner-mangers on small businesses’ willingness to adopt BA. By drawing its motivation from prior strategic orientation and v BA literature, this study is also among the first one to propose, formally develop, and validate the measurement construct of analytics orientation

Carving A Wheel or Assembling A Widget? Insights Into the Management Of Advanced Analytics

Medieval guilds and assembly plants are unlikely metaphors in an information-based economy. My experience with advanced analytics suggests that such descriptions are nevertheless apt. This paper explores two distinct situations within a single firm. In one department, predictive models were generated through adopting a craft style approach. In another department, a production type of approach was deployed. The reasons for their adoption are explored, followed by their consequences for job satisfaction, performance, staffing, change-management, and more. Craft and production approaches have implications not just for modeling analysts and their managers but also for senior leaders, business partners, and human resources staff. Finally, I describe the pressure to adopt a production approach, and attempt to unravel the extent to which this reflects broader cultural and technological influences or firmspecific traits. This reflection ends with a call for professionals to share their encounters with advanced analytics