Artificial Intelligent Enabled Supply Chains as a Competitive Advantage

The focus of this paper is on the topics of artificial intelligence and supply chain management and how artificial intelligence-enabled supply chains provide organizations with competitive advantages. The supply chain’s adoption of data collection technologies as part of digital transformation and movements of industry 4.0 creates a strong foundation for artificial intelligence analytics. Artificial intelligence has three branches sensing and interacting, decision-making, and learning. Each branch uses its algorithms and serves a different purpose for the business. Artificial intelligence-enabled supply chains create unique, inimitable competitive advantages that fit Michael Porter’s five forces

Recommendation systems and other analytic methods

Trabalho de projeto de mestrado, Matemática Aplicada à Economia e Gestão, Universidade de Lisboa, Faculdade de Ciências, 2019Neste relatório de projeto vamos explicar os fundamentos de Sistemas de Recomendações e outros métodos analíticos dando uma visão mais direcionada para a parte analítica. O objetivo é dar ao leitor um contexto geral sobre o tópico, de forma a que esteja à vontade com a linguagem associada. Para começar, vamos dar uma visão geral sobre o Ciclo Analítico, pois é necessário entender quais são os passos a dar para podermos avançar neste. Seguidamente, é também escrito um capítulo sobre um dos tópicos do momento, Machine Learning, portanto será descrito com algum cuidado para o leitor ficar familiarizado com as metodologias principais ligadas a esta abordagem. Mais direcionado para o ponto principal do projeto, falaremos sobre Sistemas de Recomendação e quais as abordagens utilizadas para o sucesso destes, quais os métodos. Visto que os modelos necessitam de dados para serem significantes, abordamos também como vamos realmente reunir os dados necessários para utilizar nestes. Uma vez tendo o modelo, é necessário avaliar se este é bom ou não. Existe sempre uma métrica para tal avaliação, uma forma de compreender se o modelo está a prever bem ou com a precisão suficiente. Mais tarde, falamos sobre enviesamento estatístico. Este tópico é bastante importante na medida em que é algo muitas vezes não é discutido e que por essa mesma razão, por vezes, pode levar a resultados errados, que não correspondem à realidade. Sendo este projeto baseado em algoritmos de Machine Learning, outros modelos importantes são também explicados: Árvores de Decisão e Clustering. No SAS, é usual dizermos que quando a curiosidade se junta à capacidade, o progresso é inevitável. Trabalhar com dados pode ser difícil, desde a limpeza destes até ao deployment, vai um longo caminho. É a nossa missão fornecer ferramentas para manipulação de dados, de forma acessível a todos.In this report we will explain the fundamentals of Recommendation Systems and other analytical methods giving a more insightful vision on the analytical part. The goal is to give the reader an overall context on the topic, so it becomes comfortable to talk about it and the surrounding context. To start, we are giving an overview on the Analytics Life Cycle, because we need to understand what the steps are, to be able to move forward. After, there is also an insightful chapter about one of the hot topics nowadays is Machine Learning, therefore we will detail it a way that you can be familiarized with the basic approaches it takes. More to the core of the project, we talk about Recommendation Systems and which are the approaches taken to achieve them, which are the methods. Since models need data to be significant, we give a view on how we gather information for most recommendation systems, since it is a more specific case. Once we have the model, how do we know if it is good or not? There is always an evaluation metric, a way of knowing if an analytic model is performing well and accurately enough. Later, we talk about statistical bias. This is particularly important in the sense that, even though have a lot of data, if it is not independent, it will not provide clear and truthful insights. Giving that this is a report based on machine learning algorithms, other models are addressed as well: Decision Trees and Clustering. Here at SAS, we say that when curiosity meets capability, progress is inevitable. Working with data can be difficult, from data cleansing to data model deployment, goes a long way. It is our mission to provide tools for data manipulation, that are easy for all to handle

Mastering the digital transformation of sales

Managerial and academic literature provide only limited guidance on how to drive the digital transformation of sales. This article presents a model for in-depth analysis of sales processes, goals for each process in terms of effectiveness and efficiency, and a structured set of digital responses. For managers, it provides actionable guidelines on how to drive the digital transformation of sales, a large set of inspiring examples, and an international benchmarking opportunity

Big Data and the Transformation of Operations Models: A Framework and A New Research Agenda

Big Data has been hailed as the ‘next big thing’ to drive business value, transform organisations and industries, and “reveal secrets to those with the humility, willingness and tools to listen” (Mayer-Schönberger and Cukier, 2013: 5). However, despite growing interest from organisations across industry sectors, Big Data applications appear to have concentrated on delivering incremental change and operational efficiency improvements, with little evidence on using Big Data to facilitate strategic, transformational change. In this paper, we explore how Big Data is actually being can be used across different sectors in leading organisations and examine the ways in which it is fostering change in the core operations models of organisations. A definition of ‘operations model’ is developed and the core components dimensions of an operations model are then examined, namely capacity, supply network, process and technology, and people development and organisation. Through a series of case studies, we examine the role of Big Data in affecting some, or all, of these components dimensions in order to generate value for the organisation by optimising, adapting or radically transforming the operations model. Following our analysis, we develop a tentative framework which can be used both for understanding how Big Data affects operations models, and for planning changes in operations models through Big Data. We set out a new research agenda to systematically understand the full potential of Big Data in transforming operations models

Consumer-driven e-commerce: A literature review, design framework, and research agenda on last-mile logistics models

Purpose The purpose of this paper is to re-examine the extant research on last-mile logistics (LML) models and consider LML’s diverse roots in city logistics, home delivery and business-to-consumer distribution, and more recent developments within the e-commerce digital supply chain context. The review offers a structured approach to what is currently a disparate and fractured field in logistics. Design/methodology/approach The systematic literature review examines the interface between e-commerce and LML. Following a protocol-driven methodology, combined with a “snowballing” technique, a total of 47 articles form the basis of the review. Findings The literature analysis conceptualises the relationship between a broad set of contingency variables and operational characteristics of LML configuration (push-centric, pull-centric, and hybrid system) via a set of structural variables, which are captured in the form of a design framework. The authors propose four future research areas reflecting likely digital supply chain evolutions. Research limitations/implications To circumvent subjective selection of articles for inclusion, all papers were assessed independently by two researchers and counterchecked with two independent logistics experts. Resulting classifications inform the development of future LML models. Practical implications The design framework of this study provides practitioners insights on key contingency and structural variables and their interrelationships, as well as viable configuration options within given boundary conditions. The reformulated knowledge allows these prescriptive models to inform practitioners in their design of last-mile distribution. Social implications Improved LML performance would have positive societal impacts in terms of service and resource efficiency. Originality/value This paper provides the first comprehensive review on LML models in the modern e-commerce context. It synthesises knowledge of LML models and provides insights on current trends and future research directions. </jats:sec

Analysis Into Artificial Intelligence And Its Developing Dynamic And Relationship In Agricultural Supply Chains

The thesis explores artificial intelligence (AI) in agricultural (Ag) supply chains (SCs) and presents a new typology to understand artificial intelligence-based solutions in agricultural SCs. The thesis was performed utilizing a research-based review to investigate the current uses of artificial intelligence-based solutions in agricultural SCs. The AI-based solutions were found in case studies that reviewed AI operations in different areas internationally. The typology was formed on the foundation of two dynamics, the location of AI applications in Ag SCs and the driving values to integrate the AI applications. In order to develop the typology, the AI applications were studied in a series of different analyses. The analyses helped to critique and scrutinize the AI applications to gain new perspectives. The series of analyses consists of exploring the AI applications’ location within the supply chain, the value additions to the supply chain from integrating the AI applications, and the resulting depth of the effect of AI application has on the supply chain. Each additional evaluation of the AI applications examining another parameter further exposed more insight and started to build a structured ideology of AI. The proposed typology aims to create a tool of measurement to infer AI technology’s relation in the SCs and create a new viewpoint that will lead investigation and provide insight for predictions of AI’s future in agricultural SCs. In addition, the new typology should aid agriculture firms in understanding and capturing the potential synergies stemming from the driving values of innovation. The study found that AI applications with a strong relationship in the supply chain provide the greatest beneficiary relationship between technology value creation and supply chain logistics. Furthermore, AI applications will have the strongest relationship and implementation when operating in collaboration with other supply chain locations and AI integrated firms. Concluding the thesis, relevant policy and business practice recommendations are proposed