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

Artificial intelligence in logistics and supply chain management: A primer and roadmap for research.

International audienceThe dawn of generative artificial intelligence (AI) has the potential to transform logistics and supply chain management radically. However, this promising innovation is met with a scholarly discourse grappling with an interplay between the promising capabilities and potential drawbacks. This conversation frequently includes dystopian forecasts of mass unemployment and detrimental repercussions concerning academic research integrity. Despite the current hype, existing research exploring the intersection between AI and the logistics and supply chain management (L&SCM) sector remains limited. Therefore, this editorial seeks to fill this void, synthesizing the potential applications of AI within the L&SCM domain alongside an analysis of the implementation challenges. In doing so, we propose a robust research framework as a primer and roadmap for future research. This will give researchers and organizations comprehensive insights and strategies to navigate the complex yet promising landscape of AI integration within the L&SCM domain

Editorial Commentary: Addressing Confusion in the Diffusion of Archival Data Research

Supply chain management researchers are increasingly using archival data to push boundaries of existing knowledge. Archival data provide opportunities to address new research questions and offer fresh perspectives on old questions. This editorial seeks to establish a common ground regarding research design, measurement validity, and endogeneity to help both authors and reviewers fully utilize archival data to advance supply chain management knowledge

A multi-objective differentiated service model for pricing and due date setting in the handmade wood product industry.

[[abstract]]Since various service needs for pricing and setting due dates in the handmade wood product industry are increasing dramatically, a differentiated service based on the maximal profit criterion becomes an important marketing strategy for customer relationship management (CRM). However, other important objectives of customer satisfaction maximization are often ignored. In this paper, a multiobjective differentiated service model based on customer preference information on pricing and due date setting into consideration in the handmade wood product industry is proposed. Since resolving the differentiated service model is an NP problem, a multiobjective hybrid heuristic method is proposed. The hybrid heuristic method integrates large neighborhood search (LNS) into particle swarm optimization (PSO). The results revealed that the proposed method is better than existing methods and other heuristic methods for the single or differentiated service model in terms of both profit and customer satisfaction criterion. Some sensitivity analysis for different customer segments and different arrival rates is also considered.[[notice]]補正完