The power of purpose – lessons in agility from the Ventilator Challenge

Purpose: COVID-19 has shaken views of what is normal and what is possible, raising questions about conventional norms, ways of working and our understanding of agility. This paper aims to respond to calls for empirical research of supply chain capacities in times of crisis and offer a unique perspective on agile procurement and supply chain management from a case study of the Ventilator Challenge. Design/methodology/approach: A descriptive case study was undertaken, adopting an inductive approach. Interviews were conducted with the major stakeholders tasked with the design, sourcing and assembly of ventilators. Findings: Findings are delivered across four key areas: context; procurement and supply chain management; technology and culture; and environment. Key challenges and enablers are discussed, highlighting the critical roles of trust, empowerment and enabling technologies in the construction of an entirely new ventilator supply chain, from scratch, in five weeks. Originality/value: This paper delivers contributions for both academic research and practice. The case study offers rich new insights relating to procurement in times of crisis, contributing to efforts to advance beyond outdated approaches for resilience in literature. Practical contributions arise in highlighting the significance of adapted sourcing and recruitment, technology, collaboration, people and power of purpose in enabling agility and achieving the impossible

A portfolio approach to managing procurement risk using multi-stage stochastic programming

Procurement is a critical supply chain management function that is susceptible to risk, due mainly to uncertain customer demand and purchase price volatility. A procurement approach in the form of a portfolio that incorporates the common procurement means is proposed. Such means include long-term contracts, spot procurements and option-based supply contracts. The objective is to explore possible synergies among the various procurement means, and so be able to produce optimal or near optimal results in profit while mitigating risk. The implementation of the portfolio approach is based on a multi-stage stochastic programming model in which replenishment decisions are made at various stages along a time horizon, with replenishment quantities being determined by simultaneously considering the stochastic demand and the price volatility of the spot market. The model attempts to minimise the risk exposure of procurement decisions measured as conditional value-at-risk. Numerical experiments to test the effectiveness of the proposed model are performed using demand data from a large air conditioner manufacturer in China and price volatility data from the Shanghai steel market. The results indicate that the proposed model can fairly reliably outperform other approaches, especially when either the demand and/or prices exhibit significant variability. © 2011 Operational Research Society Ltd. All rights reserved.link_to_subscribed_fulltex

A portfolio approach to managing procurement risk using multi-stage stochastic programming

Procurement is a critical supply chain management function that is susceptible to risk, due mainly to uncertain customer demand and purchase price volatility. A procurement approach in the form of a portfolio that incorporates the common procurement means is proposed. Such means include long-term contracts, spot procurements and option-based supply contracts. The objective is to explore possible synergies among the various procurement means, and so be able to produce optimal or near optimal results in profit while mitigating risk. The implementation of the portfolio approach is based on a multi-stage stochastic programming model in which replenishment decisions are made at various stages along a time horizon, with replenishment quantities being determined by simultaneously considering the stochastic demand and the price volatility of the spot market. The model attempts to minimise the risk exposure of procurement decisions measured as conditional value-at-risk. Numerical experiments to test the effectiveness of the proposed model are performed using demand data from a large air conditioner manufacturer in China and price volatility data from the Shanghai steel market. The results indicate that the proposed model can fairly reliably outperform other approaches, especially when either the demand and/or prices exhibit significant variability.

Multi-level, Multi-stage and Stochastic Optimization Models for Energy Conservation in Buildings for Federal, State and Local Agencies

Energy Conservation Measure (ECM) project selection is made difficult given real-world constraints, limited resources to implement savings retrofits, various suppliers in the market and project financing alternatives. Many of these energy efficient retrofit projects should be viewed as a series of investments with annual returns for these traditionally risk-averse agencies. Given a list of ECMs available, federal, state and local agencies must determine how to implement projects at lowest costs. The most common methods of implementation planning are suboptimal relative to cost. Federal, state and local agencies can obtain greater returns on their energy conservation investment over traditional methods, regardless of the implementing organization. This dissertation outlines several approaches to improve the traditional energy conservations models. Any public buildings in regions with similar energy conservation goals in the United States or internationally can also benefit greatly from this research. Additionally, many private owners of buildings are under mandates to conserve energy e.g., Local Law 85 of the New York City Energy Conservation Code requires any building, public or private, to meet the most current energy code for any alteration or renovation. Thus, both public and private stakeholders can benefit from this research. The research in this dissertation advances and presents models that decision-makers can use to optimize the selection of ECM projects with respect to the total cost of implementation. A practical application of a two-level mathematical program with equilibrium constraints (MPEC) improves the current best practice for agencies concerned with making the most cost-effective selection leveraging energy services companies or utilities. The two-level model maximizes savings to the agency and profit to the energy services companies (Chapter 2). An additional model presented leverages a single congressional appropriation to implement ECM projects (Chapter 3). Returns from implemented ECM projects are used to fund additional ECM projects. In these cases, fluctuations in energy costs and uncertainty in the estimated savings severely influence ECM project selection and the amount of the appropriation requested. A risk aversion method proposed imposes a minimum on the number of “of projects completed in each stage. A comparative method using Conditional Value at Risk is analyzed. Time consistency was addressed in this chapter. This work demonstrates how a risk-based, stochastic, multi-stage model with binary decision variables at each stage provides a much more accurate estimate for planning than the agency’s traditional approach and deterministic models. Finally, in Chapter 4, a rolling-horizon model allows for subadditivity and superadditivity of the energy savings to simulate interactive effects between ECM projects. The approach makes use of inequalities (McCormick, 1976) to re-express constraints that involve the product of binary variables with an exact linearization (related to the convex hull of those constraints). This model additionally shows the benefits of learning between stages while remaining consistent with the single congressional appropriations framework

The impact of commodity price volatility on stock prices: a case study from the exhaust gas treatment industry within the stainless steel value chain

Commodity price volatility (CPV), its impacts and potential price mitigation strategies along the stainless steel value chain are the subject of this research. The phenomena of price fluctuation attract increasing attention in literature, academia, manufacturing, and none manufacturing in-dustries like the banking industry. CPV has the potential to influence the prospects and the prosperity of companies, expressed in the share price. The share price is deployed as it con-denses these business prospects; hence, it is a gauge of the economic state of the company and future business expectations of the industry. The manufacturing industry faces an increasingly unstable business environment and a rising complexity. The recent pandemic outbreak (COVID-19) illustrates the vulnerability of the industry and the necessity of mitigation scenari-os. The term Volatility-Uncertainty-Complexity & Ambiguity (VUCA) describes this combina-tion in literature. The stainless steel value chain experiences price fluctuations and its impacts from the mining industry to the customers, like the exhaust gas treatment system producer. The competitiveness of the industry is determined by a high level of fixed costs, which is evident in steel production sites; and among others, is affected by raw material price fluctuations. The raw material may account for up to 70% of the product price. This commercial and hence financial situation challenges the stainless steel business. This research sheds light on the particularities of the industry with the means of statistics (i.e., Generalised Autoregressive Conditional Heter-oscedasticity (GARCH) and an Autoregressive Distributed Lag (ARDL) modelling). These statistic models help to gauge the time varying impact of the price variations and study the im-pact of CPV on share prices. These findings contribute to the risk management in the stainless steel industry by offering a forecast method and a selection of mitigation approaches. This re-search deploys times series models with multiple variables and hypotheses testing. These find-ings are transferrable to other industries.The investigation centred on an industry survey (questionnaire) and five in-depth interviews with stainless steel producers’ executives. This research will carve out the differences between the stainless steel producers while coping with commodity price volatility. Also, it addresses the existence of price mitigation strategies and the ability of companies to mitigate commodity price fluctuations. The experience and knowledge of commodity price volatility determines the selection of the mitigation scenarios to defend the financial stability of the manufacturing indus-try (e.g., automotive). The goal of this research is to study and measure the commodity price volatility, to help compa-nies to discover new opportunities and competition. However, this thesis will although assist companies in deploying mitigation strategies in case of disruptive phenomena and understand the risks associated to political and financial instability

An investigation into risk and vulnerability in the UK food supply network

PURPOSE: The aim of the thesis was to investigate the constructs of risk and vulnerability at a network level for the UK food supply system. Through a deeper re-examination of data collected for the Chatham House project, the objectives of the thesis were to understand actors’ perceptions of threats within UK food networks and how these relate to the constructs of risk and vulnerability. METHOD: Using a grounded analysis approach, the research re-examined data from case studies in the UK dairy and wheat supply networks, from a supply chain risk management (SCRM) and supply chain vulnerability (SCV) perspective. While not in the tradition of a true grounded theory method, the study looked to support theory building through comparison of findings to key literature in the SCRM and SCV fields. FINDINGS: The study revealed that risk, vulnerability and resilience are highly interrelated. How actors perceived risk, along with their willingness or capability to act, were core dynamics of SCV. Innovation was also identified as a major influence on resilience and adaptive capacity. At a network level, vulnerability can be characterised as system change. Thus the research highlights convergences between the fields of ecological resilience, system transition, SCV and supply chain resilience (SCRES) for supply networks. RESEARCH IMPLICATIONS: There has been very little research into SCRM, SCV and SCRES at a network level. This thesis presents a conceptualisation of these constructs for the UK food supply network, along with their interconnections, and therefore provides a contribution to these fields. PRACTICAL IMPLICATIONS: Wider socio-economic and environmental outcomes of the UK food network are at risk and there needs to be more cohesive, network-based policies and approaches to support greater resilience. This will require a stronger lead from government and collaborative approaches from policy makers and supply actors