Sustainable Inventory Management Model for High-Volume Material with Limited Storage Space under Stochastic Demand and Supply

Inventory management and control has become an important management function, which is vital in ensuring the efficiency and profitability of a company’s operations. Hence, several research studies attempted to develop models to be used to minimise the quantities of excess inventory, in order to reduce their associated costs without compromising both operational efficiency and customers’ needs. The Economic Order Quantity (EOQ) model is one of the most used of these models; however, this model has a number of limiting assumptions, which led to the development of a number of extensions for this model to increase its applicability to the modern-day business environment. Therefore, in this research study, a sustainable inventory management model is developed based on the EOQ concept to optimise the ordering and storage of large-volume inventory, which deteriorates over time, with limited storage space, such as steel, under stochastic demand, supply and backorders. Two control systems were developed and tested in this research study in order to select the most robust system: an open-loop system, based on direct control through which five different time series for each stochastic variable were generated, before an attempt to optimise the average profit was conducted; and a closed-loop system, which uses a neural network, depicting the different business and economic conditions associated with the steel manufacturing industry, to generate the optimal control parameters for each week across the entire planning horizon. A sensitivity analysis proved that the closed-loop neural network control system was more accurate in depicting real-life business conditions, and more robust in optimising the inventory management process for a large-volume, deteriorating item. Moreover, due to its advantages over other techniques, a meta-heuristic Particle Swarm Optimisation (PSO) algorithm was used to solve this model. This model is implemented throughout the research in the case of a steel manufacturing factory under different operational and extreme economic scenarios. As a result of the case study, the developed model proved its robustness and accuracy in managing the inventory of such a unique industry

Decision Making in Supply Chains with Waste Considerations

As global population and income levels have increased, so has the waste generated as a byproduct of our production and consumption processes. Approximately two billion tons of municipal solid waste are generated globally every year – that is, more than half a kilogram per person each day. This waste, which is generated at various stages of the supply chain, has negative environmental effects and often represents an inefficient use or allocation of limited resources. With the growing concern about waste, many governments are implementing regulations to reduce waste. Waste is a often consequence of the inventory decisions of different players in a supply chain. As such, these regulations aim to reduce waste by influencing inventory decisions. However, determining the inventory decisions of players in a supply chain is not trivial. Modern supply chains often consist of numerous players, who may each differ in their objectives and in the factors they consider when making decisions such as how much product to buy and when. While each player makes unilateral inventory decisions, these decisions may also affect the decisions of other players. This complexity makes it difficult to predict how a policy will affect profit and waste outcomes for individual players and the supply chain as a whole. This dissertation studies the inventory decisions of players in a supply chain when faced with policy interventions to reduce waste. In particular, the focus is on food supply chains, where food waste and packaging waste are the largest waste components. Chapter 2 studies a two-period inventory game between a seller (e.g., a wholesaler) and a buyer (e.g., a retailer) in a supply chain for a perishable food product with uncertain demand from a downstream market. The buyer can differ in whether he considers factors affecting future periods or the seller’s supply availability in his period purchase decisions – that is, in his degree of strategic behavior. The focus is on understanding how the buyer’s degree of strategic behavior affects inventory outcomes. Chapter 3 builds on this understanding by investigating waste outcomes and how policies that penalize waste affect individual and supply chain profits and waste. Chapter 4 studies the setting of a restaurant that uses reusable containers instead of single-use ones to serve its delivery and take-away orders. With policy-makers discouraging the use of single-use containers through surcharges or bans, reusable containers have emerged as an alternative. Managing inventories of reusable containers is challenging for a restaurant as both demand and returns of containers are uncertain and the restaurant faces various customers types. This chapter investigates how the proportion of each customer type affects the restaurant’s inventory decisions and costs

Market Transformation for Value-Retention Processes as a Strategy for Circular Economy

There is increasing global interest in the application of circular economy as a tool for enabling the decoupling of economic growth from environmental degradation. Despite this growing interest, there is a lack of in-depth insight about the quantified potential benefits that value-retention processes (VRPs) – direct reuse, repair, refurbishment and remanufacturing – can contribute to circular economy and improved resource efficiency. In this assessment, product-level production impacts are bridged with economy-level insights about market, regulatory, technological and infrastructure conditions, to demonstrate and quantify the essential role of value-retention processes within circular economies. Three representative products were selected from each of three industrial sectors known to engage in VRPs (Industrial digital printers, vehicle parts, and heavy-duty and off-road equipment), and select environmental and economic impacts were assessed at the material- and product-levels. Results indicate that, where appropriately employed, the adoption of VRPs can lead to significant reduction of negative environmental impact and positive economic opportunity at the product- and process-levels. Further, these insights were assessed in the context of diverse sample industrial economies around the world (Brazil, China, Germany, and United States of America) to better understand the significance of varied systemic conditions and barriers to VRPs in the realization of circular economy objectives. In aggregate, this work highlights the need for policy-makers and decision-makers to incorporate systems-perspectives and integrated environmental and technology policy approaches into their circular economy strategies. Industry must embrace a product-system design approach that considers both forward- and reverse-logistics, as well as a new value-proposition based in maximized customer utility, multiple product service lives, and results-oriented business models. In parallel, governments of both industrialized and non-industrialized economies must look for opportunities to further enable, enhance, optimize, and improve the efficiency of accepted value-retention practices if they are to optimize their potential for value-retention and the pursuit of circular economy

Sustainable Operations Management: An Enduring Stream or a Passing Fancy?

Paul Kleindorfer was among the first to weigh in on and nurture the stream of Sustainable Operations Management. The thoughts laid out here are based on conversations we had with Paul relating to the drivers underlying sustainability as a management issue: population and per capita consumption growth, the limited nature of resources and sinks, and the responsibility and exposure of firms to ensuing ecological risks and costs. We then discuss how an operations management lens contributes to the issue and criteria to help the Sustainable Operations Management perspective endure. This article relates to a presentation delivered by Morris Cohen for Paul's Manufacturing and Service Operations Management Distinguished Fellows Award, given at Columbia University, June 18, 2012. We wrote this article at Paul's request

Integrated optimisation for production capacity, raw material ordering and production planning under time and quantity uncertainties based on two case studies

Abstract This paper develops a supply chain (SC) model by integrating raw material ordering and production planning, and production capacity decisions based upon two case studies in manufacturing firms. Multiple types of uncertainties are considered; including: time-related uncertainty (that exists in lead-time and delay) and quantity-related uncertainty (that exists in information and material flows). The SC model consists of several sub-models, which are first formulated mathematically. Simulation (simulation-based stochastic approximation) and genetic algorithm tools are then developed to evaluate several non-parameterised strategies and optimise two parameterised strategies. Experiments are conducted to contrast these strategies, quantify their relative performance, and illustrate the value of information and the impact of uncertainties. These case studies provide useful insights into understanding to what degree the integrated planning model including production capacity decisions could benefit economically in different scenarios, which types of data should be shared, and how these data could be utilised to achieve a better SC system. This study provides insights for small and middle-sized firm management to make better decisions regarding production capacity issues with respect to external uncertainty and/or disruptions; e.g. trade wars and pandemics.</jats:p

Impact of RFID information-sharing coordination over a supply chain with reverse logistics

Companies have adopted environmental practices such as reverse logistics over the past few decades. However, studies show that aligning partners inside the green supply chain can be a substantial problem. This lack of coordination can increase overall supply chain cost. Information technology such as Radio Frequency Identification (RFID) has the potential to enable decentralized supply chain coordinate their information. Even though there are research that address RFID on traditional supply chain, few researches address how to coordinate RFID information sharing in a green supply chain. We study, through simulation experiments, two types of RFID information-sharing coordination under different configurations related with their inventory policies: basic and advanced. Statistical analyses show that better results can be presented in advanced RFID configuration given new coordination and inventory policy decisions presented. In addition, these findings shows what are the RFID information-sharing coordination that can provide better system improvement depending on the supply chain scenarios and factors

Application of Optimization in Production, Logistics, Inventory, Supply Chain Management and Block Chain

The evolution of industrial development since the 18th century is now experiencing the fourth industrial revolution. The effect of the development has propagated into almost every sector of the industry. From inventory to the circular economy, the effectiveness of technology has been fruitful for industry. The recent trends in research, with new ideas and methodologies, are included in this book. Several new ideas and business strategies are developed in the area of the supply chain management, logistics, optimization, and forecasting for the improvement of the economy of the society and the environment. The proposed technologies and ideas are either novel or help modify several other new ideas. Different real life problems with different dimensions are discussed in the book so that readers may connect with the recent issues in society and industry. The collection of the articles provides a glimpse into the new research trends in technology, business, and the environment

Improving green supply chain performance with Operations Research

Due to increasing greenhouse gas emission as a consequence of the production activities in various industries, managing the supply chain has been a big concern between both scholars and practitioners. Green supplier selection and order allocation is among important topics that managers should pay attention to as the majority of the supply chain costs and emission level during production process depends on the procured material by suppliers. Also, investigating the emission abatement regulations, and interactions between regulator and manufacturers is one of the main concerns of supply chain managers that should be figured out. In the present study, green supply chain problems are taken into account for more investigations. First, a green supplier selection and order allocation model in a closed-loop supply chain considering both environmental and economical criteria, is studied. In this study, one of the carbon emission abatement schemes, cap-and-trade mechanism is proposed. The described problem is modeled as a multi-objective robust optimization (RO) model. Second, the cap-and-trade (C\&T) mechanism is further investigated. The goal of this investigation is to find the best strategy for supply chain parties to maximize their utility as well as minimize the carbon emission. To model the described problem, a stochastic three-player game theoretical model is developed. The results show that the developed models can effectively help decision makers select the most appropriate suppliers, allocate the proper amount of order to each selected supplier, and find optimal strategy of C\&T players. Also, the results show that the uncertainty control approaches used in the presented models are capable of handling the model uncertainties from different sources. Furthermore, this study shows that C\&T outperforms the penalty based systems in terms of the total utility of the supply chain. Moreover, the robustness of the results is proved by sensitivity analyses. Another area that is investigated in this study is the disruption effects on supply chain. Disasters and pandemics like COVID-19 can destroy industries by causing huge disruptions in their supply chains. To control these disruptions, decision-makers need to design resilient supply chains. This study proposes a multi-stage, multi-period resilient green supply chain design model considering six resilient strategies. Disruptions are taken into account in both downstream and upstream directions, causing the ripple effect and bullwhip effect, respectively. To control the mentioned disruptions, and handle uncertainties of parameter estimations, a two-stage stochastic optimization approach is applied. The objectives are to minimize the total cost of disruption and $CO_{2}$ emission considering the cap-and-trade mechanism as a government-issued emission regulation. The proposed decision-making framework and solution approach are validated using a numerical experiment followed by a sensitivity analysis. The results show the optimal structure of the supply chain and the best resilient strategies to mitigate the ripple effect. Moreover, the effect of a decrease in capacity of facilities on the optimal solution and the applied resilient strategies is investigated. This study provides managerial insights to help governments set the proper amount of cap and supply chain managers to predict the demand behaviour of essential and non-essential products in the event of disruptions

Green Technologies for Production Processes

This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies