From supply chains to demand networks. Agents in retailing: the electrical bazaar

A paradigm shift is taking place in logistics. The focus is changing from operational effectiveness to adaptation. Supply Chains will develop into networks that will adapt to consumer demand in almost real time. Time to market, capacity of adaptation and enrichment of customer experience seem to be the key elements of this new paradigm. In this environment emerging technologies like RFID (Radio Frequency ID), Intelligent Products and the Internet, are triggering a reconsideration of methods, procedures and goals. We present a Multiagent System framework specialized in retail that addresses these changes with the use of rational agents and takes advantages of the new market opportunities. Like in an old bazaar, agents able to learn, cooperate, take advantage of gossip and distinguish between collaborators and competitors, have the ability to adapt, learn and react to a changing environment better than any other structure. Keywords: Supply Chains, Distributed Artificial Intelligence, Multiagent System.Postprint (published version

Supply chain business modelling

The developed work is motivated by the hypothesis that the presented Supply Chain Business Model is a practical and comprehensive approach to support not only operational day-to-day business decisions, but most importantly strategic and long term decisions that may define the success and the longevity of a business. Conceptually, the Business Supply Chain Model developed in this thesis replicates the behaviour and decision making of the different agents in a supply chain, and an Optimisation Module determines the optimised parameters that maximise the overall business profit, whatever scenario it may be. In the optimisation module, a Genetic Algorithm was used to determine the best equation parameters for each individual agent that optimise the overall supply chain profit. Furthermore, several business case-scenarios are presented and the findings highlighted. These case-scenarios prove that: the HC model is robust when subjected to predictable or unpredictable causes of variability; the bullwhip effect can be reduced significantly by applying GA as the optimisation tool; the improvement of profits needs to be evaluated at a global scale, independently of the individual agents’ profit; impact of supply shortages in the SC ; retail expansion analysis; delivery patterns change impact in profitability; impact of sourcing decisions in the SC profitability; model suitability for seasonal vs. non-seasonal products. The SC Modelling framework generic and globalising approach means that is easily applied and transposed to any other business realities and it can be easily changed to reflect other SC scenarios. The costing model associated means that, at any point in the network, all costs and profits can be easily measured. For the first time the shelf-life of a product captured and losses of product due to BBE dates, quantified. In this model the optimisation methodology runs parallel to the developed simulation tool, so the optimisation should be only run for new scenarios

Supply chain uncertainty:a review and theoretical foundation for future research

Supply-chain uncertainty is an issue with which every practising manager wrestles, deriving from the increasing complexity of global supply networks. Taking a broad view of supply-chain uncertainty (incorporating supply-chain risk), this paper seeks to review the literature in this area and develop a theoretical foundation for future research. The literature review identifies a comprehensive list of 14 sources of uncertainty, including those that have received much research attention, such as the bullwhip effect, and those more recently described, such as parallel interaction. Approaches to managing these sources of uncertainty are classified into: 10 approaches that seek to reduce uncertainty at its source; and, 11 approaches that seek to cope with it, thereby minimising its impact on performance. Manufacturing strategy theory, including the concepts of alignment and contingency, is then used to develop a model of supply-chain uncertainty, which is populated using the literature review to show alignment between uncertainty sources and management strategies. Future research proposed includes more empirical research in order to further investigate: which uncertainties occur in particular industrial contexts; the impact of appropriate sources/management strategy alignment on performance; and the complex interplay between management strategies and multiple sources of uncertainty (positive or negative)

Supply Chain Management: Reducing the Bullwhip Effect in SME’s

Effective supply chain management is critical for mitigating the Bullwhip Ejfect (BWE) in SME’s. Thus, this study examines how order batching, lead time, rationing, demand forecasting errors, information sharing and sale promotions afiect the Bullwhip ejfect. Primary data was collected through questionnaires from 150 respondents belonging to business organizations operating in Karachi. The study finds that demand forecasting, order batching, rationing, lead-time, sales promotions and information sharing are major factors afiecting the bullwhip ejfect. ln view of these findings, organizations should seek to manage the bullwhip effect through sharing real time information and maintaining a centralized supply chain network.Keywords: Bull Whip Ejfect, Supply Chain Management

Two Studies on The Use of Information Technology in Collaborative Planning, Forecasting & Replenishment (CPFR)

In the 1st study, I seek to determine whether there are trends in the coverage of the use of Information Technology in CPFR in support of Supply Chain Management. I look at the way technology is studied along two dimensions. The first dimension is the function within CPFR—Planning, Forecasting or Replenishment. The second dimension is level at which the study addresses use of the technology, whether at the Operational, Tactical or Strategic level. Within this 3x3 matrix, I seek to prove that studies would primarily fall along a line where the higher the level functions should be served by systems which have a longer-term orientation. This was broadly true, along with an emphasis on studies at the strategic level. Additionally, I find an underrepresentation of Forecasting, especially at the strategic level. The 2nd study seeks to determine the factors affecting IT system use for CPFR, in the real world. I examine the factors affecting system use along two dimensions. The first is along the company-level dimension. There are 3 points along the company-level dimension, defined as follows. Strategic use is defined as use by upper level management who are interested in the long term view of the organization and its processes and products. The Tactical use of IT for CPFR includes use by middle managers at a departmental level for medium term decision making. Operational level IT use covers functions which directly affect individual customers and keep the business running day to day. The second dimension along which system use is examined, is the functional-dimension. There are 3 points along this dimension and they are defined as follows. Use of IT for Planning, based on the VICS standard, is usually, but not exclusively under the purview of senior managers to determine what products to manufacture and the features they should have. Forecasting is done mainly by middle-managers in order to move enough products at the right time, to the right paces, while avoiding over-stocking each product. The Replenishment function is the actual process of moving items to the customer as they are ordered on-line or bought from the shelf. This is typically the job of operational logistics personnel such as purchasing and, shipping and delivery, as well as front-line staff such as customer service, shop-floor attendants or cashiers who interface directly with customers. In examining real world IT use for CPFR, I build on Simmonds, Haines & Li (2013) which looks at the trends and gaps in the IT literature as far as use of IT in CPFR was concerned. The aim is to determine whether the literature lines up with reality, or whether researchers are inherently biased when studying how Information Technology is used to support CPFR. A survey instrument was sent to 4000 senior managers in manufacturing and distribution companies. IT use along the STO dimension (Haines, Hough, & Haines, 2010) and its relationship with Industry characteristics (clock-speed of the industry and technological orientation) will be investigated in the context of the Technology Acceptance Model (TAM) (Fred D. Davis, 1989). Product factors (such as demand variability & luxury nature of the product) which drive IT use (Attaran & Attaran, 2007) along the PFR dimension will be investigated in the context of Technology Task Fit Theory (Goodhue & Thompson, 1995). Intra-firm trust (Frazier, Johnson, Gavin, Gooty, & Bradley Snow, 2010) and its effect on use on the PFR dimension, will be looked at with managerial influence within Innovation Diffusion theory (Rogers, 2010) as a basis. Trust issues including confidence of management in competence of workers and confidence of employees in dependability of IT

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research

Operational research IO 2021—analytics for a better world. XXI Congress of APDIO, Figueira da Foz, Portugal, November 7–8, 2021

This book provides the current status of research on the application of OR methods to solve emerging and relevant operations management problems. Each chapter is a selected contribution of the IO2021 - XXI Congress of APDIO, the Portuguese Association of Operational Research, held in Figueira da Foz from 7 to 8 November 2021. Under the theme of analytics for a better world, the book presents interesting results and applications of OR cutting-edge methods and techniques to various real-world problems. Of particular importance are works applying nonlinear, multi-objective optimization, hybrid heuristics, multicriteria decision analysis, data envelopment analysis, simulation, clustering techniques and decision support systems, in different areas such as supply chain management, production planning and scheduling, logistics, energy, telecommunications, finance and health. All chapters were carefully reviewed by the members of the scientific program committee.info:eu-repo/semantics/publishedVersio

Forecasting Demand in the Pharmaceutical Industry Using Machine Learning

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Driven Marketing, specialization in Data Science for MarketingThis study delves into the exploitation of three machine learning models, namely the Extreme Gradient Boosting (XGBoost), the Long Short-Term Memory (LSTM), and the novel Prophet algorithm, to surpass the challenge of demand forecast within the pharmaceutical industry. Following the CRISP-DM framework, we enabled accurate sales forecasting by studying, treating, transforming, and training a dataset containing historical sales data from a major Portuguese pharmaceutical company. Our findings align with the literature, underlying the robustness of the XGBoost and the inefficacy of the LSTM for the delineated task, considering the singularities of the provided data. Furthermore, this research highlights the potential of the Prophet for both its effectiveness and efficiency. This endeavor allowed us to reinforce the literature’s conviction of the need for product-specific forecasting, showcasing that no single model achieves the best accuracy for all drugs