Supply Chain

Traditionally supply chain management has meant factories, assembly lines, warehouses, transportation vehicles, and time sheets. Modern supply chain management is a highly complex, multidimensional problem set with virtually endless number of variables for optimization. An Internet enabled supply chain may have just-in-time delivery, precise inventory visibility, and up-to-the-minute distribution-tracking capabilities. Technology advances have enabled supply chains to become strategic weapons that can help avoid disasters, lower costs, and make money. From internal enterprise processes to external business transactions with suppliers, transporters, channels and end-users marks the wide range of challenges researchers have to handle. The aim of this book is at revealing and illustrating this diversity in terms of scientific and theoretical fundamentals, prevailing concepts as well as current practical applications

Use of Artificial Intelligence (AI) in Managing Inventory of Medicine in Pharmaceutical Industry

 Inventory is one of the vital components of current assets. Excess holdings of inventory may increase cost as well as wastage. As such, effective and efficient management of inventory is an integral part of supply chain. Especially, in the field of management of pharmaceutical products and medicine it bears more importance. Improper use of pharmaceutical products or shortage of medicine would not only cause financial loss but also may affect the patients adversely. Rather than using the traditional techniques of managing inventory use of Artificial Intelligence (AI) can make the process more effective and efficient. AI is the application of computer program that demonstrates action like a human being, learns from experience, gets new input and processes big data by reasoning. It can acquire large amount of data and create rules for turning the data into actionable information. This study has been conducted based mainly on secondary sources of data. It is a qualitative study that gives a conceptual idea regarding how the functions of AI can support managing inventory of medicine in pharmaceutical industry

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

Stochastic modeling of responsiveness, schedule risk and obsolescence of space systems, and implications for design choices

The U.S Department of Defense and the National Aeronautics and Space Administration continue to face common challenges in the development and acquisition of their space systems. In particular, space programs repeatedly experience significant schedule slippages, and spacecraft are often delivered on-orbit several months, sometimes years, after the initially planned delivery date. The repeated pattern of these schedule slippages suggests deep-seated flaws in managing spacecraft delivery and schedule risk, and an inadequate understanding of the drivers of schedule slippages. Furthermore, due to their long development time and physical inaccessibility after launch, space systems are exposed to a particular and acute risk of obsolescence, resulting in loss of value or competitive advantage over time. The perception of this particular risk has driven some government agencies to promote design choices that may ultimately be contributing to these schedule slippages, and jeopardizing what is increasingly recognized as critical, namely space responsiveness. The overall research objective of this work is twofold: (1) to identify and develop a thorough understanding of the fundamental causes of the risk of schedule slippage and obsolescence of space systems; and in so doing, (2) to guide spacecraft design choices that would result in better control of spacecraft delivery schedule and mitigate the impact of these "temporal risks" (schedule and obsolescence risks). To lay the groundwork for this thesis, first, the levers of responsiveness, or means to influence schedule slippage and impact space responsiveness are identified and analyzed, including design, organizational, and launch levers. Second, a multidisciplinary review of obsolescence is conducted, and main drivers of system obsolescence are identified. This thesis then adapts the concept of a technology portfolio from the macro- or company level to the micro-level of a single complex engineering system, and it analyzes a space system as a portfolio of technologies and instruments, each technology with its distinct stochastic maturation path and exposure to obsolescence. The selection of the spacecraft portfolio is captured by parameters such as the number of instruments, the initial technology maturity of each technology/instrument, the resulting heterogeneity of the technology maturity of the whole system, and the spacecraft design lifetime. Building on the abstraction of a spacecraft as a portfolio of technologies, this thesis then develops a stochastic framework that provides a powerful capability to simultaneously explore the impact of design decisions on spacecraft schedule, on-orbit obsolescence, and cumulative utility delivered by the spacecraft. Specifically, this thesis shows how the choice of the portfolio size and the instruments Technology Readiness Levels (TRLs) impact the Mean-Time-To-Delivery (MTTD) of the spacecraft and mitigate (or exacerbate) schedule risk. This work also demonstrates that specific combinations/choices of the spacecraft design lifetime and the TRLs can reduce the risk of on-orbit obsolescence. This thesis then advocates for a paradigm shift towards a calendar-based design mindset, in which the delivery time of the spacecraft is accounted for, as opposed to the traditional clock-based design mindset. The calendar-based paradigm is shown to lead to different design choices, which are more likely to prevent schedule slippage and/or enhance responsiveness and ultimately result in a larger cumulative utility delivered. Finally, missions scenarios are presented to illustrate how the framework and analyses here proposed can help identify system design choices that satisfy various mission objectives and constraints (temporal as well as utility-based).PhDCommittee Chair: Saleh Joseph; Committee Member: Brown Owen; Committee Member: Erwin R. Scott; Committee Member: Feron Eric; Committee Member: Mavris Dimitr

Production planning with parameters on the basis of dynamic predictive models : interconnection and the inertness of their interaction

The research is related to the increasing role of prognostic models in production systems management, which is associated with an increase in the requirements for managerial efficiency, the need to consider external factors affecting the system, the determination of the features of the systems in question, the examination of the processes in progress and the relationship between the chain of managerial decisions and the values of the selected control parameters. The purpose of the article is to consider and evaluate the consequences of decisions made as a chain of interrelated events in time with regard to the dynamics of the environment in which production systems operate and the variability of control parameters. The leading approach of the research considers the production system as one that is open "in terms of environment" and "in terms of the ultimate goal". The proprietary results demonstrate that the solutions obtained are of a probabilistic nature, the solutions should be set by ranges of possible values, the decision ranges can be arranged in such a way as to introduce variability into the decisions made, the choice of which will be based on factors not taken into account in the proposed method of analyzing production systems. The practical and theoretical significance of the research is that the described methodology allows to obtain optimal values of control parameters based on the objectives of the production system under consideration on the basis of its integrated assessment, taking into account the interaction and the mutual influence of the system’s parameters, their inertness and probabilistic nature, which makes it possible to increase the validity of managerial decisions and to consider the inertness of the processes taking place in the system during planning.peer-reviewe