Modelling and managing systemic risks in supply chains

A structured review of the supply chain and risk management literature supports an analysis of the sources and types of risks anticipated in supply chains and networks. We discuss alternative modelling approaches, such as Bayesian Belief Nets (BBN), System Dynamics, Fault and Event Trees, which are evaluated against the criteria characterizing systemic risks that emerge from the literature review. Finally, we briefly present an empirical pilot case study is conducted with a public sector organization in charge of a pharmaceutical distribution network to explore the feasibility of a BBN modelling approach

Modelling, simulation, and analysis of supply chain systems using discrete-event simulation.

Many approaches have been developed which support the construction of detailed supply chain models useful for analysis and simulation. However, most of these approaches lack the ability to model the supply chain in a single model, and usually produce solutions that lead to conflicting strategies between the companies. Simulation using a discrete-event simulation (DES) is an effective tool for the dynamically changing supply chain variables, thus allowing the system to be modelled more realistically. Considering the complexities of the supply chain system and the interrelations between its various systems, the task of developing such a model is challenging. The aim of this thesis is to develop a simulation model of a fast moving consumer goods (FMCG) supply chain with a DES tool. This model would be utilised as a decision-support system (DSS) for the investigation of the effectiveness of several inventory policies towards effective coordination and control of production inventory system, in various situations. This thesis discusses fundamental issues in the development of a simulation model for a supply chain using the DES tool, ARENA. A modelling procedure for the development of a supply chain simulation model is presented. The overall structure of the model is constructed by incorporating the well documented concept of modelling materials flowing downstream with an approach of modelling orders flowing upstream (modelling of feedback information). The model has an easily adaptable structure where rules (inventory policies) and model variables can be modified. The flexibility in the model's structure allows devising appropriate experimental designs, for several tests to be performed to imitate some realistic situations or scenarios (including the presence of disturbances). A new control theory oriented inventory policy, called the pseudo PID, is proposed. Detailed evaluations of five inventory policies for a production-inventory control under dynamic and stochastic conditions is presented. The findings demonstrate the ability of the approach to provide a wealth of potential solutions to the decision-maker, and confirm the qualitative behaviour of a supply chain in response to the different policies

The application of discrete event simulation and system dynamics in the logistics and supply chain context

Discrete event simulation (DES) and system dynamics (SD) are two modelling approaches widely used as decision support tools in logistics and supply chain management (LSCM). A widely held belief exists that SD is mostly used to model problems at a strategic level, whereas DES is used at an operational/tactical level. This paper explores the application of DES and SD as decision support systems (DSS) for LSCM by looking at the nature and level of issues modelled. Peer reviewed journal papers that use these modelling approaches to study supply chains, published between 1996 and 2006 are reviewed. A total of 127 journal articles are analysed to identify the frequency with which the two simulation approaches are used as modelling tools for DSS in LSCM. Our findings suggest that DES has been used more frequently to model supply chains, with the exception of the bullwhip effect, which is mostly modelled using SD. Based on the most commonly used modelling approach, issues in LSCM are categorised into four groups: the DES domain, the SD domain, the common domain and the less common domain. The study furthermore suggests that in terms of the level of decision making involved, strategic or operational/tactical, there is no difference in the use of either DES or SD. The results of this study inform the existing literature about the use of DES and SD as DSS tools in LSCM

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

Exploring the influence of big data on city transport operations: a Markovian approach

© 2017, © Emerald Publishing Limited.Purpose: The purpose of this paper is to advance knowledge of the transformative potential of big data on city-based transport models. The central question guiding this paper is: how could big data transform smart city transport operations? In answering this question the authors present initial results from a Markov study. However the authors also suggest caution in the transformation potential of big data and highlight the risks of city and organizational adoption. A theoretical framework is presented together with an associated scenario which guides the development of a Markov model. Design/methodology/approach: A model with several scenarios is developed to explore a theoretical framework focussed on matching the transport demands (of people and freight mobility) with city transport service provision using big data. This model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services. Findings: This modelling study is an initial preliminary stage of the investigation in how big data could be used to redefine and enable new operational models. The study provides new understanding about load sharing and optimization in a smart city context. Basically the authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city. Further how improvement could take place by having a car free city environment, autonomous vehicles and shared resource capacity among providers. Research limitations/implications: The research relied on a Markov model and the numerical solution of its steady state probabilities vector to illustrate the transformation of transport operations management (OM) in the future city context. More in depth analysis and more discrete modelling are clearly needed to assist in the implementation of big data initiatives and facilitate new innovations in OM. The work complements and extends that of Setia and Patel (2013), who theoretically link together information system design to operation absorptive capacity capabilities. Practical implications: The study implies that transport operations would actually need to be re-organized so as to deal with lowering CO2 footprint. The logistic aspects could be seen as a move from individual firms optimizing their own transportation supply to a shared collaborative load and resourced system. Such ideas are radical changes driven by, or leading to more decentralized rather than having centralized transport solutions (Caplice, 2013). Social implications: The growth of cities and urban areas in the twenty-first century has put more pressure on resources and conditions of urban life. This paper is an initial first step in building theory, knowledge and critical understanding of the social implications being posed by the growth in cities and the role that big data and smart cities could play in developing a resilient and sustainable transport city system. Originality/value: Despite the importance of OM to big data implementation, for both practitioners and researchers, we have yet to see a systematic analysis of its implementation and its absorptive capacity contribution to building capabilities, at either city system or organizational levels. As such the Markov model makes a preliminary contribution to the literature integrating big data capabilities with OM capabilities and the resulting improvements in system absorptive capacity

An exploration of integrality in project productions and its final outcome : the mobile production cells

Tese de doutoramento. Engenharia Industrial e Gestão. Faculdade de Engenharia. Universidade do Porto. 200

Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems

Digital data, dynamic capability and financial performance: an empirical investigation in the era of Big Data

Firms automatically and continuously capture a high amount of digital data through social media, RFID tags, clickstreams, smart meters, manufacturing sensors, equipment logs, and vehicle tracking systems. However, empirical evidence on the effects of the generation of these digital data on firm performance remains scarce in the Information Systems and Management literature. Therefore, from a dynamic capability perspective, this paper examines whether companies’ ability to leverage digital data, which we call their Digital Data dynamic capability, leads to better financial performance, and whether there are moderating effects on this relationship. In order to achieve these goals, the following research questions are addressed: 1) To what extent do firms that develop Digital Data dynamic capabilities achieve better financial performance? 2) To what extent do organisational and industry-related environmental conditions moderate the relationship between a firm’s Digital Data dynamic capability and financial performance? We empirically test our hypotheses through partial least square modelling using a financial database and a survey of sales managers from 125 firms. We find that the development of Digital Data dynamic capability provides value in terms of firm financial performance and that the moderating effects are influential: under high levels of dynamism and munificence in younger firms, the relationship is stronger. Overall, this study evaluates the potential business value of firm digital data use and addresses a lack of empirical evidence on this issue in the Information Systems literature. We discuss two managerial implications. First, managers should pay more attention to digital data phenomena and to ways of leveraging value creation opportunities. Second, managers must evaluate their environment and organisational characteristics when business opportunities from digital data are taken into account