Supply Chain Analysis Overview

Principles

Definition

Supply chain analysis is a systematic methodological approach involving the investigation of the structure, processes, and performance of interconnected entities and activities that create and deliver a product or service to the end customer. This analysis aims to identify inefficiencies, risks, and opportunities within the network to inform strategic and operational decisions for optimization, resilience, and value creation.

Ontological type

Key Performance Metrics

Analytical Frameworks

Core Objectives

Key developments

Key Figures

Information Coordination Era era

Hau L. Lee [1] is a distinguished scholar associated with Stanford University [3] during the Information Coordination Era. His key contribution in this era is the exposition of Information Distortion in a Supply Chain: The Bullwhip Effect [6], which showed how demand signals distort through the network and underscored the role of governance and data sharing to dampen oscillations. D.R. Towill [2] is affiliated with Cardiff University [4] and the University of Wales [5] during this period. Towill's contributions in Industrial dynamics modelling of supply chains [7], Time compression and supply chain management ‐ a guided tour [8], and Supply Chain Dynamics—The Change Engineering Challenge of the Mid 1990s [9] provided analytic frameworks and guidance that improved coordination and responsiveness in the mid-1990s.

Resilience and Sustainability Governance era

Craig R. Carter [1] is a leading figure in the Resilience and Sustainability Governance era, with affiliations at the Massachusetts Institute of Technology [3] and the University of Maryland, College Park [4]. His contributions to sustainable supply chain management are epitomized by the 2008 framework paper 'A framework of sustainable supply chain management: moving toward new theory' [7] and the 2011 synthesis 'Sustainable supply chain management: evolution and future directions' [8], which reframed theory and guided procurement, inventory, and sourcing decisions toward resilience and sustainability. Dale S. Rogers [2], affiliated with Rutgers, The State University of New Jersey [5] and Michigan State University [6], co-authored the 2008 framework of sustainable supply chain management [7], contributing a formal, integrative lens for resilience and sustainability in supply chain analysis. This foundational framework helped drive cross-functional coordination, network visibility, and standardized metrics that translate resilience and sustainability into procurement and inventory practices, reinforcing the era's governance aims [7].

Digital Resilience Analytics era

Dmitry Ivanov [1] is a leading author in the Digital Resilience Analytics era, collaborating with institutions such as University of Padua [3] and Universität Hamburg [4]. His contributions include 'Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case' [7], a 'Review of quantitative methods for supply chain resilience analysis' [8], and a 'Coronavirus (COVID-19/SARS-CoV-2) and supply chain resilience: a research note' [9], which collectively advance data-driven, scenario-based resilience analytics across disrupted global networks. Alexandre Dolgui [2] builds on this era through Nantes Université [5] and IMT Atlantique [6], shaping resilience analytics via synthesis of quantitative methods and network reconfigurability. His key contributions include the review of quantitative methods for supply chain resilience analysis [8] and the proposal of the Reconfigurable supply chain: the X-network [10], which demonstrates how flexible network structures enable rapid reconfiguration in response to disruptions—critical for multi-tier governance in Digital Resilience Analytics.