How to Make the Most Productive Intervention in a Complex Economic System

Information about supply and demand propagates through supply chains in a queueing network with people and computers as batch information processors. As each batch processor delays propagation of information whilst pursuing optimal local decisions, the effect is delay and distortion of the information that is used to commit resources to actions in the supply chain. This thesis investigates the effect of delay and imperfect information as a source of error, to establish the case for change in research focus from optimal exploitation of physical constraints to optimal exploitation of information. In the context of real world supply chains, the thesis asks "How does one make the most productive intervention in a complex economic system?" and pursues a meta-intervention which perpetually minimises the discovered error-term. Evidence from literature indicates that agent-based modelling permits real-time peer-to-peer communication and distributed optimisation. Based on the literature the research project designs and develops an agent-based model which operates in real-time without batch-processes and can perform incremental multi-objective optimisation under realistic (chronologically progressive) conditions for decision making. The agent based model is then used to investigate two real-world supply chains, as case studies, which reveals a significant improvement of profitability and order-fulfilment. The thesis concludes that agent-based modelling is a very promising direction for "making the most productive intervention" as it reduces delay to a minimum. Finally it recommends that continuous improvement of decision making methods is a role better suited for humans, rather than operational decision making where computers cope much better with the high amount of detailed information

Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information

In marketing analytics applications in OR, the modeler often faces the problem of selecting key variables from a large number of possibilities. For example, SKU level retail store sales are affected by inter and intra category effects which potentially need to be considered when deciding on promotional strategy and producing operational forecasts. But no research has yet put this well accepted concept into forecasting practice: an obvious obstacle is the ultra-high dimensionality of the variable space. This paper develops a four steps methodological framework to overcome the problem. It is illustrated by investigating the value of both intra- and inter-category SKU level promotional information in improving forecast accuracy. The method consists of the identification of potentially influential categories, the building of the explanatory variable space, variable selection and model estimation by a multistage LASSO regression, and the use of a rolling scheme to generate forecasts. The success of this new method for dealing with high dimensionality is demonstrated by improvements in forecasting accuracy compared to alternative methods of simplifying the variable space. The empirical results show that models integrating more information perform significantly better than the baseline model when using the proposed methodology framework. In general, we can improve the forecasting accuracy by 12.6 percent over the model using only the SKU's own predictors. But of the improvements achieved, 95 percent of it comes from the intra-category information, and only 5 percent from the inter-category information. The substantive marketing results also have implications for promotional category management

Digital twin-driven real-time planning, monitoring, and controlling in food supply chains

There needs to be more clarity about when and how the digital twin approach could benefit the food supply chains. In this study, we develop and solve an integrated problem of procurement, production, and distribution strategies (PPDs) in a medium-scale food processing company. Using the digital twin approach, the model considers the industrial symbiosis opportunities between the supplier, manufacturer, and customer using interval and sequence variables operating in a constrained environment using mixed-integer linear programming (MILP) and agent-based simulation (ABS) methodology. The study optimizes the make-span and lead time, simultaneously achieving a higher level of digitalization. The analysis demonstrates how digital twin accelerates supply chain productivity by improving makespan time, data redundancy (DR), optimal scheduling plan (OSP), overall operations effectiveness (OOE), overall equipment effectiveness (OEE), and capacity utilization. Our findings provide compelling evidence that the seamless integration PPDs enormously enhance production flexibility, resulting in an excellent service level of 94 %. Managers leverage real-time simulation to accurately estimate the replenishment point with minimal lead time, ensuring optimized operations. Furthermore, our results demonstrate that implementing PPDs has yielded considerable benefits. Specifically, we observed a remarkable 65 % utilization of the pasteurizer and aging vessel and an impressive 97 % utilization of the freezer. Moreover, by applying the DT model, the present model found a notable 6 % reduction in backlog, further streamlining operations and enhancing efficiency

Forecasting: theory and practice

Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

Modelling an End-to-End Supply Chain System Using Simulation

Supply chains (SCs) are an important part of today’s world. Many businesses operate in the global marketplace where individual companies are no longer treated as separate entities, but as a vital part of an end-to-end supply chain (E2E-SC) system. Key challenges and issues in managing E2E-SCs are duly attributed to their extended, complex and systemic nature. In the era of uncertainty, risks and market volatility, decision makers are searching for modelling techniques to be able to understand, to control, design or evaluate their E2E-SC. This research aims to support academics and decision makers by defining a generic simulation modelling approach that can be used for any E2E-SC. This study considers the challenges and issues associated with modelling complex E2E-SC systems using simulation and underlines the key requirements for modelling an E2E-SC. The systematic literature review approach is applied to provide a twofold theoretical contribution [a] an insightful review of various contributions to knowledge surrounding simulation methods within the literature on end-to-end supply chains and [b] to propose a conceptual framework that suggests generic elements required for modelling such systems using simulation. The research adopts a simulation methodology and develops a generic guide to an E2E-SC simulation model creation process. It is a mindful inquiry into the implications relative to a simulation model development process in presence of generic elements from the proposed conceptual framework. The conceptual framework is validated with industry experts and insightful remarks are drawn. In conclusion, it is acknowledged that modelling an E2E-SC system using simulation is a challenge, and this area is not fully exploited by the business. A guide to an E2E-SC simulation model development is a theoretical and practical contribution of this research, immensely sought by businesses, which are continuously tackling day to day issues and challenges, hence often lacking resources and time to focus on modelling. The conceptual framework captures generic elements of the E2E-SC system; however, it also highlights multiple challenges around simulation model development process such as technical constraints and almost impracticability of a true reflection of an E2E-SC system simulation model. The significant contribution of this thesis is the evaluation of the proposed generic guide to E2E-SC simulate model development, which provides the architecture for better strategic supply and demand balancing as new products, price fluctuations, and options for physical network changes can be dynamically incorporated into the model. The research provides an insightful journey through key challenges and issues when modelling E2E-SC systems and contributes with key recommendations for mindful inquiries into E2E-SC simulation models

Exploratory research into supply chain voids within Welsh priority business sectors

The paper reports the findings resulting from the initial stages of an exploratory investigation into Supply Chain Voids (SCV) in Wales. The research forms the foundations of a PhD thesis which is framed within the sectors designated as important by the Welsh Assembly Government (WAG) and indicates local supplier capability voids within their supply chains. This paper covers the stages of initial data gathering, analysis and results identified between June 2006 and April 2007, whilst addressing the first of four research questions. Finally, the approach to address future research is identified in order to explain how the PhD is to progress

Forecasting: theory and practice

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.info:eu-repo/semantics/publishedVersio