Supply Chain Digital Twin Framework Design:An Approach of Supply Chain Operations Reference Model and System of Systems

Digital twin technology has been regarded as a beneficial approach in supply chain development. Different from traditional digital twin (temporal dynamic), supply chain digital twin is a spatio-temporal dynamic system. This paper explains what is 'twined' in supply chain digital twin and how to 'twin' them to handle the spatio-temporal dynamic issue. A supply chain digital twin framework is developed based on the theories of system of systems and supply chain operations reference model. This framework is universal and can be applied in various types of supply chain systems. We firstly decompose the supply chain system into unified standard blocks preparing for the adoption of digital twin. Next, the idea of supply chain operations reference model is adopted to digitise basic supply chain activities within each block and explain how to use existing information system. Then, individual sub-digital twin is established for each member in supply chain system. After that, we apply the concept of system of systems to integrate and coordinate sub-digital twin into supply chain digital twin from the views of supply chain business integration and information system integration. At last, one simple supply chain system is applied to illustrate the application of the proposed model

An Integrated Retail Supply Chain Risk Management Framework: A System Thinking Approach

It is often taken for granted that the right products will be available to buy in retail outlets seven days a week, 52 weeks a year. Consumer perception is that of a simple service requirement, but the reality is a complex, time sensitive system - the retail supply chain (RSC). Due to short product life-cycles with uncertain supply and demand behaviour, the RSC faces many challenges and is very vulnerable to disruptions. In addition, external risk events such as BREXIT, extreme weather, the financial crisis, and terror attacks mean there is a need for effective RSC risk management (RSCRM) processes within organisations. Literature shows that although there is an increasing amount of research in RSCRM, it is highly theoretical with limited empirical evidence or applied methodologies. With an active enthusiasm coming from industry practitioners for RSCRM methodologies and support solutions, the RSCRM research community have acknowledged that the main issue for future research is not tools and techniques, but collaborative RSC system wide implementation. The implementation of a cross-organisational initiative such as RSCRM is a very complex task that requires real-world frameworks for real-world practitioners. Therefore, this research study attempts to explore the business requirements for developing a three-stage integrated RSCRM framework that will encourage extended RSC collaboration. While focusing on the practitioner requirements of RSCRM projects and inspired by the laws of Thermodynamics and the philosophy of System Thinking, in stage one a conceptual reference model, The �6 Coefficient, was developed building on the formative work of supply chain excellence and business process management. The �6 Coefficient reference model has been intricately designed to bridge the theoretical gap between practitioner and researcher with the aim of ensuring practitioner confidence in partaking in a complex business process project. Stage two focused on a need for a standardised vocabulary, and through the SCOR11 reference guide, acts as a calibration point for the integrated framework, ensuring easy transfer and application within supply chain industries. In their design, stages one and two are perfect complements to the final stage of the integrated framework, a risk assessment toolbox based on a Hybrid Simulation Study capable of monitoring the disruptive behaviour of a multi-echelon RSC from both a macro and micro level using the techniques of System Dynamics (SD) and Discrete Event Simulation (DES) modelling respectively. Empirically validated through an embedded mixed methods case study, results of the integrated framework application are very encouraging. The first phase, the secondary exploratory study, gained valuable empirical evidence of the barriers to successfully implementing a complex business project and also validated using simulation as an effective risk assessment tool. Results showed certain high-risk order policy decisions could potentially reduce total costs (TC) by over 55% and reduce delivery times by 3 days. The use of the �6 Coefficient as the communication/consultation phase of the primary RSCRM case study was hugely influential on the success of the overall hybrid simulation study development and application, with significant increase in both practitioner and researcher confidence in running an RSCRM project. This was evident in the results of the hybrid model’s macro and micro assessment of the RSC. SD results effectively monitored the behaviour of the RSC under important disruptive risks, showing delayed effects to promotions and knowledge loss resulted in a bullwhip effect pattern upstream with the FMCG manufacturer’s TC increasing by as much as €50m. The DES analysis, focusing on the NDC function of the RSC also showed results of TC sensitivity to order behaviour from retailers, although an optimisation based risk treatment has reduced TC by 30%. Future research includes a global empirical validation of the �6 Coefficient and enhancement of the application of thermodynamic laws in business process management. The industry calibration capabilities of the integrated framework application of the integrated framework will also be extensively tested

Measuring agri-food supply chain performance and risk through a new analytical framework: a case study of New Zealand dairy

Many researchers and practitioners have long recognized the significance of measuring performance. Although general guidelines for measuring business performance are widely available, no appropriate measurement frameworks have been developed for measuring agri-food supply chain performance. Particularly, food quality and risk-related indicators have not been well integrated into existing performance measurement systems. Our research, therefore, addresses this knowledge gap by first providing an in-depth review of extant performance measurement systems and frameworks. It then develops an analytical framework by extending the Supply Chain Operations Reference (SCOR) model which has been extensively implemented across non-food industries. The analytical framework is further validated by utilizing a case study of 50 farmers and 10 dairy companies, operating in the New Zealand dairy industry. Our pilot testing and subsequent findings show that the individual metrics interlocked with the analytical framework are in-line with the key industrial practices adapted by the New Zealand dairy industry. In addition, the framework is flexible and scalable to evaluate and benchmark other agri-food supply chains–ranging from fresh products such as fruits and vegetables to processed foods such as canned fruits. The findings further show that the detailed information required for measuring the level-3 SCOR metrics is not easily available in the industry, as researchers need to access specific company records that may be confidential. Consequently, this study provides how agri-food supply chain managers can employ our new analytical framework in-conjunction with the SCOR model for a deeper understanding of the complicated performance measurement indicators applied in their agri-food production systems and relevant supply chains

A Representation of Tactical and Strategic Precursors of Supply Network Resilience Using Simulation Based Experiments

Modern supply chains are becoming increasingly complex and are exposed to higher levels of risk. Globalization, market uncertainty, mass customization, technological and innovation forces, among other factors, make supply networks more susceptible to disruptions (both those that are man-made and/or ones associated with natural events) that leave suppliers unavailable, shut-down facilities and entail lost capacity. Whereas several models for disruption management exist, there is a need for operational representations of concepts such as resilience that expand the practitioners’ understanding of the behavior of their supply chains. These representations must include not only specific characteristics of the firm’s supply network but also its tactical and strategic decisions (such as sourcing and product design). Furthermore, the representations should capture the impact those characteristics have on the performance of the network facing disruptions, thus providing operations managers with insights on what tactical and strategic decisions are most suitable for their specific supply networks (and product types) in the event of a disruption. This research uses Agent-Based Modeling and Simulation (ABMS) and an experimental set-up to develop a representation of the relationships between tactical and strategic decisions and their impact on the performance of multi-echelon networks under supply uncertainty. Two main questions are answered: 1) How do different tactical and strategic decisions give rise to resilience in a multi-echelon system?, and 2) What is the nature of the interactions between those factors, the network’s structure and its performance in the event of a disruption? Product design was found to have the most significant impact on the reliability (Perfect Order Fulfillment) for products with high degrees of componentization when dual sourcing is the chosen strategy. However, when it comes to network responsiveness (Order Fulfillment Cycle Time), this effect was attenuated. Generally, it was found that the expected individual impact these factors have on the network performance is affected by the interactions between them

Optimizing The Global Performance Of Build-to-order Supply Chains

Build-to-order supply chains (BOSCs) have recently received increasing attention due to the shifting focus of manufacturing companies from mass production to mass customization. This shift has generated a growing need for efficient methods to design BOSCs. This research proposes an approach for BOSC design that simultaneously considers multiple performance measures at three stages of a BOSC Tier I suppliers, the focal manufacturing company and Tier I customers (product delivery couriers). We present a heuristic solution approach that constructs the best BOSC configuration through the selection of suppliers, manufacturing resources at the focal company and delivery couriers. The resulting configuration is the one that yields the best global performance relative to five deterministic performance measures simultaneously, some of which are nonlinear. We compare the heuristic results to those from an exact method, and the results show that the proposed approach yields BOSC configurations with near-optimal performance. The absolute deviation in mean performance across all experiments is consistently less than 4%, with a variance less than 0.5%. We propose a second heuristic approach for the stochastic BOSC environment. Compared to the deterministic BOSC performance, experimental results show that optimizing BOSC performance according to stochastic local performance measures can yield a significantly different supply chain configuration. Local optimization means optimizing according to one performance measure independently of the other four. Using Monte Carlo simulation, we test the impact of local performance variability on the global performance of the BOSC. Experimental results show that, as variability of the local performance increases, the mean global performance decreases, while variation in the global performance increases at steeper levels

Mitigating Space Industry Supply Chain Risk Thru Risk-Based Analysis

Using risk-based analysis to consider supply chain disruptions and uncertainty along with potential mitigation strategies in the early stages of space industry projects can be used avoid schedule delays, cost overruns, and lead to successful project outcomes. Space industry projects, especially launch vehicles, are complicated assemblies of high-technology and specialized components. Components are engineered, procured, manufactured, and assembled for specific missions or projects, unlike make-to-stock manufacturing where assemblies are produced at a mass production rate for customers to choose off the shelf or lot, like automobiles. The supply chain for a space industry project is a large, complicated web where one disruption, especially for sole-sourced components, could ripple through the project causing delays at multiple project milestones. This ripple effect can even cause the delay or cancelation of the entire project unless project managers develop and employ risk mitigations strategies against supply chain disruption and uncertainty. The unpredictability of when delays and disruptions may occur makes managing these projects extremely difficult. By using risk-based analysis, project managers can better plan for and mitigate supply chain risk and uncertainty for space industry projects to better manage project success. Space industry project supply chain risk and uncertainty can be evaluated through risk assessments at major project milestones and during the procurement process. Mitigations for identified risks can be evaluated and implemented to better manage project success. One mitigation strategy to supply chain risk and uncertainty is implementing a dual or multi-supplier sourcing procurement strategy. This research explores using a risk-based analysis to identify where this mitigation strategy can be beneficial for space industry projects and how its implementation affects project success. First a supply chain risk assessment and mitigation decision tool will be used at major project milestones to show where a multi-sourcing strategy may be beneficial. Next, updated supplier quote evaluation tools will confirm the usage of multiple suppliers for procurement. Modeling and simulation are then used to show the impact of that strategy on the project success metrics of cost and schedule

A Diagnostic Framework for Demand Amplification Problems in Supply Chains

This dissertation delivers a framework to diagnose the Bull-Whip Effect (BWE) in supply chains and then identify methods to minimize it. Such a framework is needed because in spite of the significant amount of literature discussing the bull-whip effect, many companies continue to experience the wide variations in demand that are indicative of the bull-whip effect. While the theory and knowledge of the bull-whip effect is well established, there still is the lack of an engineering framework and method to systematically identify the problem, diagnose its causes, and identify remedies. The present work seeks to fill this gap by providing a holistic, systems perspective to bull-whip identification and diagnosis. The framework employs the SCOR reference model to examine the supply chain processes with a baseline measure of demand amplification. Then, research of the supply chain structural and behavioral features is conducted by means of the system dynamics modeling method. The contribution of the diagnostic framework, is called Demand Amplification Protocol (DAMP), relies not only on the improvement of existent methods but also contributes with original developments introduced to accomplish successful diagnosis. DAMP contributes a comprehensive methodology that captures the dynamic complexities of supply chain processes. The method also contributes a BWE measurement method that is suitable for actual supply chains because of its low data requirements, and introduces a BWE scorecard for relating established causes to a central BWE metric. In addition, the dissertation makes a methodological contribution to the analysis of system dynamic models with a technique for statistical screening called SS-Opt, which determines the inputs with the greatest impact on the bull-whip effect by means of perturbation analysis and subsequent multivariate optimization. The dissertation describes the implementation of the DAMP framework in an actual case study that exposes the approach, analysis, results and conclusions. The case study suggests a balanced solution between costs and demand amplification can better serve both firms and supply chain interests. Insights pinpoint to supplier network redesign, postponement in manufacturing operations and collaborative forecasting agreements with main distributors

An integrated framework for improving supply chain performance

In 2009, Roland Berger Strategy Consultants [Roland Berger Strategy Consultants, (2009). Global SCM excellence study., p.5.] reported that 40% of 234 companies had the wrong priorities in regard to efficiency vs. responsiveness. In 2014, PricewaterhouseCoopers (PwC) and American Production and Inventory Control Society (APICS) [PwC and APICS, Sustainable supply chains: Making value the priority 2014] found that 76% of 500 supply chain executives identified sustainability as an important aspect of their supply chain. The results highlight the importance of achieving consistency between customer expectations, in terms of cost and service level, and supply chain performance in today’s competitive business environment. Despite this, however, no integrated supply chain design framework exists to control majority of the important functions related to supply chain strategy, structure, process and performance. The literature review showed that simulation is rarely considered at the strategic level, but the research experiments highlighted a number of ways in which simulation tools might be useful at this level, such as exploring the impact of strategic fit and decoupling points, and assessing different supply chain network configurations and policies. This research contributes to knowledge by designing and developing a framework that integrates strategy, process and resources, and allows the use of simulation tools to consider the three dimensions of efficiency, responsiveness and sustainability concurrently during the design process. The proposed framework is validated using a hypothetical supply chain network. Simulation allows performance to be assessed under a range of scenarios. The simulation experiments showed that under the suggested policies, efficiency improved from 25.38% to 30.58% and responsiveness rose from 18.37% to 32.78%. However, they also indicated that while policies oriented towards improving responsiveness had a positive impact on sustainability, those oriented towards improving efficiency had a negative impact. The significance of the research lies in its development of a supply chain design framework that could assist companies in achieving the optimum configuration of supply chain resources, thereby helping them reduce inventory, lower costs, enhance responsiveness and improve strategic focus in terms of design, execution and capital investments

An Investigation into Factors Affecting the Chilled Food Industry

With the advent of Industry 4.0, many new approaches towards process monitoring, benchmarking and traceability are becoming available, and these techniques have the potential to radically transform the agri-food sector. In particular, the chilled food supply chain (CFSC) contains a number of unique challenges by virtue of it being thought of as a temperature controlled supply chain. Therefore, once the key issues affecting the CFSC have been identified, algorithms can be proposed, which would allow realistic thresholds to be established for managing these problems on the micro, meso and macro scales. Hence, a study is required into factors affecting the CFSC within the scope of Industry 4.0. The study itself has been broken down into four main topics: identifying the key issues within the CFSC; implementing a philosophy of continuous improvement within the CFSC; identifying uncertainty within the CFSC; improving and measuring the performance of the supply chain. However, as a consequence of this study two further topics were added: a discussion of some of the issues surrounding information sharing between retailers and suppliers; some of the wider issues affecting food losses and wastage (FLW) on the micro, meso and macro scales. A hybrid algorithm is developed, which incorporates the analytic hierarchical process (AHP) for qualitative issues and data envelopment analysis (DEA) for quantitative issues. The hybrid algorithm itself is a development of the internal auditing algorithm proposed by Sueyoshi et al (2009), which in turn was developed following corporate scandals such as Tyco, Enron, and WorldCom, which have led to a decline in public trust. However, the advantage of the proposed solution is that all of the key issues within the CFSC identified can be managed from a single computer terminal, whilst the risk of food contamination such as the 2013 horsemeat scandal can be avoided via improved traceability