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

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

Modeling the Colombian Swine Supply Chain from a Knowledge Management Perspective

The Colombian swine supply chain (CSSC) has a low level of national competitiveness compared to other supply chains such as coffee and fruit. While consumption of pork has raised in Colombia, most dealers are importing it from The United States and Canada, since farmers in those countries have received agricultural incentives to breed and commercialize pigs. Additionally, agribusiness have received technological developments to share information and develop the swine sector. This article aims to state theoretical Knowledge Management (KM) dimensions for CSSC that were built under authors’ assumptions on the literature. These were proposed to identify the competitiveness level in CSSC, because only two different kinds of measuring for swine competitiveness were found, but on the other hand, no model about Swine Supply Chain (SSC) was found. Perspectives of researching KM in CSSC would integrate stakeholders using a technological web platform which allows interchange of information among them.La cadena de suministro porcina colombiana (CSSC) tiene un bajo nivel de competitividad nacional en comparación con otras cadenas de suministro como café y frutas. Si bien el consumo de carne de cerdo ha aumentado en Colombia, la mayoría de los comerciantes la importan de Estados Unidos y Canadá, ya que los ganaderos de esos países han recibido incentivos agrícolas para criar y comercializar cerdos. Adicionalmente, la agroindustria ha recibido desarrollos tecnológicos para compartir información y desarrollar el sector porcino. Este artículo tiene como objetivo establecer las dimensiones teóricas de la Gestión del conocimiento (KM) para CSSC que se construyeron bajo los supuestos de los autores en la literatura. Estos fueron propuestos para identificar el nivel de competitividad en CSSC, debido a que solo se encontraron dos tipos diferentes de medición para la competitividad porcina, pero por otro lado, no se encontró un modelo sobre Cadena de suministro porcina (SSC). Las perspectivas de investigar KM en CSSC integrarían a las partes interesadas utilizando una plataforma web tecnológica que permita el intercambio de información entre ellos