Establishing a framework for the effective design of resilient supply chains with inherent non-linearities

Purpose of this paper: Previous control theory research on supply chain dynamics has predominantly taken a linear perspective of the real world, whereas nonlinearities have usually been studied via a simulation approach. Nonlinearities can naturally occur in supply chains through the existence of physical and economic constraints, for example, capacity limitations. Since the ability to flex capacity is an important aspect of supply chain resilience, there is a need to rigorously study such nonlinearities. Hence, the purpose of this paper is to propose a framework for the dynamic design of supply chains so that they are resilient to nonlinear system structures

Applications of lean thinking: a briefing document

This report has been put together by the Health and Care Infrastructure Research and Innovation Centre (HaCIRIC) at the University of Salford for the Department of Health. The need for the report grew out of two main simple questions, o Is Lean applicable in sectors other than manufacturing? o Can the service delivery sector learn from the success of lean in manufacturing and realise the benefits of its implementation?The aim of the report is to list together examples of lean thinking as it is evidenced in the public and private service sector. Following a review of various sources a catalogue of evidence is put together in an organised manner which demonstrates that Lean principles and techniques, when applied rigorously and throughout an entire organization/unit, they can have a positive impact on productivity, cost, quality, and timely delivery of services

Supply chain network design models for a circular economy: a review and a case study assessment

Global supply chains are getting increasingly dispersed, and hence, more complex. This has also made them more vulnerable to disruptions and risks. As a result, there is a constant need to reconfigure/redesign them to ensure competitiveness. However, the relevant aspects/facets for doing so are fragmented and scattered across the literature. This study reviews the literature to develop a holistic understanding of the key considerations (environment, cost, efficiency, and risks) in designing/redesigning global supply chains. This understanding is then applied to assess the global supply chain network of a leading multinational tire manufacturing firm; also to provide recommendations on redesigning it. The study has significant practical and research implications for global supply chain management

Performance measurement and its impact on sustainable and resilient supply chain management practices in the Thai electronic industry

Paper originally presented at the 20th Annual Logistics Research Network (LRN) Conference, 9th to 11th Sept 2015, Derby

Performance measurement and its impact on sustainable and resilient supply chain management practices in the Thai electronic industry

Paper originally presented at the 20th Annual Logistics Research Network (LRN) Conference, 9th to 11th Sept 2015, Derby

Framework For Effective Resilience Managmenet Of Complex Supply Networks

In today\u27s environment with high global and complex supply chains for engineered products, the ability to assess and manage the resilience of supply chains is not a luxury but a fundamental prerequisite for business continuity and success. This is particularly true for firms with deep-tier supply chains, such as the automotive original equipment manufacturers (OEMs) and their suppliers. Automotive supply networks are particularly facing growing challenges due to their complexity, globalization, economic volatility, rapidly changing technologies, regulations, and environmental/political shocks. These risks and challenges can disrupt and halt operations in any section of the supply network. Given that supply chains have become quite lean in the 21st century with relatively little slack, the COVID-19 pandemic has fully exposed these vulnerabilities. According to Allianz\u27s Business Risk Report from 2014, half of all supply chain disruptions stemming from tier-2 and tier-3 suppliers. However, the industry\u27s supply network assessment practice is primarily limited to immediate (i.e., tier-1 ) suppliers with no real consideration for the deep-tiers. The added complication due to poor supplier relations is that there is no visibility to the upstream deeper-tiers of the supply network, which could lead to severe vulnerabilities and impose massive disruption costs. Our research goal is to enhance the resilience of deep-tier automotive supply networks through improved resilience assessment and management mechanisms. In this collaborative study with a global automotive OEM (Ford Motor Company), we seek to develop methods to assess and manage the resilience of deep-tier supply networks. This research considers the multi-dimensional nature of resilience management, focusing on metrics around cost efficiency, effective inventory management, demand fulfillment, capacity management, and delivery performance. We develop and evaluate our proposed resilience assessment and management framework with a real case study supply network for an automotive climate control system. The supply network contains 20 firms (nodes) located in various global regions and 21 connections (edges) between firms. The network includes three tiers of suppliers with different transportation modes, making the network a rich illustrative example for proposed resilience assessment and management methods and analysis. All inventory and shipping policies with related parameters have been defined and set for each supplier and their connections. The proposed resilience assessment framework relies on discrete-event simulation for effectiveness; computational efficiency is maintained by relying on modern open-source packages for modeling, optimization, and analysis. The framework starts by generating a digital supply network model that includes the focal firm and its suppliers and deeper-tiers based on the available visibility. Disruption scenarios, including disruption sources, frequency, and severity, are then efficiently generated using private and public regional risk sources. For illustrative purposes, we primarily relied on public secondary data sources. The secondary regional risk indices that we relied upon aggregate political, economic, legal, operational, and security risks for the given region. Finally, the digital supply network is simulated with an adequate number of replications for reliable assessment. In this research, discrete-event simulation is implemented using NetworkX and SimPy Python packages. We employ the network analysis techniques combined with discrete-event simulation informed by secondary data sources for improving the assessment framework. Our resilience assessment results confirm that visibility into the deeper-tiers of the supply network (through primary or secondary data sources) leads to a more accurate network resilience assessment. Finally, we offer a global sensitivity analysis procedure to determine the supply network players, parameters, and policies that most influence the network performance. We also propose an effective resilience management framework that efficiently leverages simulation-based optimization. For illustrative purposes, we considered the mitigation strategies typical in the automotive industry, such as dual sourcing, reserve capacities (at primary or secondary suppliers), and contracts with backup suppliers besides carrying safety stock. Sourcing and transportation mode decisions can be easily incorporated into the framework. The method seeks to minimize the cost of risk mitigation strategies while attaining the target resilience. The framework is flexible and can entertain other objectives and constraints. Given that simulation-based optimization methods can be computationally expensive, we employ surrogate models that relate supply network resilience performance to network design parameters within our mathematical programming formulation. Without loss of generality, the surrogate models are based on linear regression models that define the relationship between the focal firm and tier-1 suppliers\u27 resilience levels and network design decision variables. The imperfections of the regression models are accounted for in the formulation through constraints with slack (function of the RMSE of the regression model). We demonstrate that optimal resilience management would stem from jointly allocating safety buffers (e.g., capacity, inventory levels) across the network and not by independently applying a simplistic/static set of rules for all nodes/arcs. Our validation experiments with a real-world case study informed by secondary data from public data sources confirm the effectiveness and efficiency of the proposed supply network resilience management method

MARE: Semantic Supply Chain Disruption Management and Resilience Evaluation Framework

Supply Chains (SCs) are subject to disruptive events that potentially hinder the operational performance. Disruption Management Process (DMP) relies on the analysis of integrated heterogeneous data sources such as production scheduling, order management and logistics to evaluate the impact of disruptions on the SC. Existing approaches are limited as they address DMP process steps and corresponding data sources in a rather isolated manner which hurdles the systematic handling of a disruption originating anywhere in the SC. Thus, we propose MARE a semantic disruption management and resilience evaluation framework for integration of data sources included in all DMP steps, i.e. Monitor/Model, Assess, Recover and Evaluate. MARE, leverages semantic technologies i.e. ontologies, knowledge graphs and SPARQL queries to model and reproduce SC behavior under disruptive scenarios. Also, MARE includes an evaluation framework to examine the restoration performance of a SC applying various recovery strategies. Semantic SC DMP, put forward by MARE, allows stakeholders to potentially identify the measures to enhance SC integration, increase the resilience of supply networks and ultimately facilitate digitalization

A computer simulation-based analysis of supply chains resilience in industrial environment

The article presents a computer simulation-based model for analysis of supply chain resilience, which allows determining and verifying the generally valid principles, capabilities and ways for building long-term resilience of global supply chains against serious disruptions. The model is created on the basis of a supply chain from automotive industry and contains the main logistics flows used by present automotive producers. Any real automotive supply chain can be modelled as a combination of these logistics flows. The model was created in DOSIMIS-3® and verified using experimental data. The performed simulation demonstrates a significant decrease of the supply chain performance in case of serious disruption occurrence.Web of Science54470670