Supply Chain Intelligence

This chapter provides on overall picture of business intelligence (BI) and supply chain analytics (SCA) as a means to support supply chain management (SCM) and decision-making. Based on the literature review, we clarify the needs of BI and performance measurement in the SCM sphere, and discuss its potential to enhance decision-making in strategic, tactical and operational levels. We also make a closer look in to SCA in different areas and functions of SCM. Our findings indicate that the main challenge for harnessing the full potential of SCA is the lack of holistic and integrated BI approaches that originates from the fact that each functional area is using its own IT applications without necessary integration in to the company’s overall BI system. Following this examination, we construct a holistic framework that illustrates how an integrated, managerially planned BI system can be developed. Finally, we discuss the main competency requirements, as well as the challenges still prohibiting the great majority of firms from building smart and comprehensive BI systems for SCM.fi=vertaisarvioitu|en=peerReviewed

Business analytics in industry 4.0: a systematic review

Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions

Exploring the Future Shape of Business Intelligence: Mapping Dynamic Capabilities of Information Systems to Business Intelligence Agility

A major challenge in today’s turbulent environments is to make appropriate decisions to sustainably steer an organization. Business intelligence (BI) systems are often used as a basis for decision making. But achieving agility in BI and cope with dynamic environments is no trivial endeavor as the classical, data-warehouse (DWH)-based BI is primarily used to retrospectively reflect an organization’s performance. Using an exploratory approach, this paper investigates how current trends affect the concept of BI and thus their ability to support adequate decision making. The key focus is to understand dynamic capabilities in the field of information systems (IS) and how they are connected to BI agility. We therefore map dynamic capabilities from the IS literature to agility dimensions of BI. Additionally, we propose a structural model that focusses on DWH-based BI and analyze how current BI-related trends and environmental turbulence affect the way that BI is shaped in the future

Smart supply chain management in Industry 4.0

The emerging information and communication technologies (ICT) related to Industry 4.0 play a critical role to enhance supply chain performance. Employing the smart technologies has led to so-called smart supply chains. Understanding how Industry 4.0 and related ICT affect smart supply chains and how smart supply chains evolve with the support of the advanced technologies are vital to practical and academic communities. Existing review works on smart supply chains with ICT mainly rely on the academic literature alone. This paper presents an integrated approach to explore the effects of Industry 4.0 and related ICT on smart supply chains, by combining introduction of the current national strategies in North America, the research status analysis on ICT assisted supply chains from the major North American national research councils, and a systematic literature review of the subject. Besides, we introduce a smart supply chain hierarchical framework with multi-level intelligence. Furthermore, the challenges faced by supply chains under Industry 4.0 and future research directions are discussed as well

Managing Distribution Logistics Using Enterprise Systems

To meet the shipping deadlines of dynamic product demands efficiently, the operations and shipping systems in an enterprise must be nimble and responsive enough to delight customers. Organizations have implemented enterprise systems (ESs) to integrate their supply chain processes such as customer order receipt, logistics planning, manufacturing, and dispatch of products. This paper explores the distribution logistics function of manufacturing organizations utilizing ES technology to investigate the goods dispatch process. Three case studies are conducted in manufacturing companies that have implemented ESs to examine how these systems support the management practices and strategies in shipping out operations. Findings reveal that ES tools aid information flow for tracking shipment orders, optimization of product packaging, and achieving on-time deliveries. Though firms are sometimes constrained in materials and availability of physical products for dispatch, the underlying ES technology provides the analytical and knowledge-leveraging support to spur the distribution logistics processes efficiently

Determining Learning Outcomes Relevant for Logistics Higher Education on Sustainability and Industry 4.0

The ambitious goals of the European Union require companies to transform themselves into sustainable and smart. To do so, for example, in the logistics field, employees who will know how to implement green transport and logistics and how to establish smart systems based on a higher level of digital maturity are needed. This article lists 51 essential topics from Sustainability, Industry 4.0, Logistics 4.0, and Digitalisation areas, further supported by 127 unique learning outcomes gathered through a multi-stage international Delphi study including practitioners and academics. Experts from 6 countries participating in the study first identified the most important topics from the field and the connected learning outcomes in the following rounds, and consequently achieved consensus on the most important learning outcomes that the future workforce in the logistics sector should have. Supporting the logistics sector with a workforce educated based on this framework should help it reach the European Union’s strategic goals

IoT Value Creation Through Supply Chain Analytics Capability

Business Intelligence and Analytics (BI&A) systems form the key information processing artifact that enables firms to process, store, and use the data generated by the Internet of Things (IoT) in the supply chain context. We empirically investigate how firms create value from IoT through a ‘capability creation’ path model for Supply Chain Analytics Capability. Partial least square analysis of primary survey data collected from 127 firms in India provides two key findings: 1) a modular system architecture and decentralized governance across supply chain partners are important precursors to build a robust Supply Chain Analytics Capability which can utilize IoT based data 2) Supply Chain Analytics Capability influences Firm Performance in two ways - directly, through Supply Chain Integration, and interactively with Supply Chain Integration. Overall, this study establishes the antecedents and consequences of Supply Chain Analytics Capability, which is an important precursor to value creation through IoT