Showing papers by "Boris Sokolov published in 2019"

The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics

Abstract: The impact of digitalisation and Industry 4.0 on the ripple effect and disruption risk control analytics in the supply chain (SC) is studied. The research framework combines the results from two is...

Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-the-art and applications

Abstract: This paper presents a survey on the applications of optimal control to scheduling in production, supply chain and Industry 4.0 systems with a focus on the deterministic maximum principle. The first objective is to derive major contributions, application areas, limitations, as well as research and application recommendations for the future research. The second objective is to explain control engineering models in terms of industrial engineering and production management. To achieve these objectives, optimal control models, qualitative methods of performance analysis and computational methods for optimal control are considered. We provide a brief historic overview and clarify major mathematical fundamentals whereby the control engineering terms are brought into correspondence with industrial engineering and management. The survey allows the grouping of models with only terminal constraints with application to master production scheduling, models with hybrid terminal–logical constraints with applications to ...

Digital Supply Chain Twins: Managing the Ripple Effect, Resilience, and Disruption Risks by Data-Driven Optimization, Simulation, and Visibility

Abstract: The quality of model-based decision-making support strongly depends on the data, its completeness, fullness, validity, consistency, and timely availability. These requirements on data are of a special importance in supply chain (SC) risk management for predicting disruptions and reacting to them. Digital technology, Industry 4.0, Blockchain, and real-time data analytics have a potential to achieve a new quality in decision-making support when managing severe disruptions, resilience, and the Ripple effect. A combination of simulation, optimization, and data analytics constitutes a digital twin: a new data-driven vision of managing the disruption risks in SC. A digital SC twin is a model that can represent the network state for any given moment in time and allow for complete end-to-end SC visibility to improve resilience and test contingency plans. This chapter proposes an SC risk analytics framework and explains the concept of digital SC twins. It analyses perspectives and future transformations to be expected in transition toward cyber-physical SCs. It demonstrates a vision of how digital technologies and smart operations can help integrate resilience and lean thinking into a resileanness framework “Low-Certainty-Need” (LCN) SC.

Integrated detection of disruption scenarios, the ripple effect dispersal and recovery paths in supply chains

Abstract: The studies on supply chain (SC) disruption management frequently assume the existence of some negative scenarios and suggest ways to proactively protect and reactively recover the SC operations and performance if such scenarios occur. Though, there is a paucity of research on how to support methodologically the detection of realistic disruption scenarios, ideally of different risk aversion degrees. The contribution of our study lies in a conceptualization of a new methodical approach to the detection of disruption scenarios, ripple effect dispersal and recovery paths in supply chains on the basis of structural genomes. The objective is to integrate and expand the existing knowledge gained isolated in robustness analysis and recovery planning into a comprehensive framework for building a theory as well as for managerial purposes. The outcomes of this research constitute a useful decision-making support tool that allows detecting disruption scenarios at different risk-aversion levels based on the quantification of the structural robustness with the use of the genome method and observing the scope of disruption propagation, i.e., the ripple effect. The advantage of using a robustness computation by the genome method is that this allows detecting both the disruption scenarios of different severity, the ripple effect dispersal, and the corresponding recovery paths. Our results can be of value for decision-makers to compare different supply chain structural designs regarding the robustness and to identify disruption scenarios that interrupt the supply chain operations to different extents. The scenario detection can be further used for identifying optimal reconfiguration paths to deploy proactive contingency and reactive recovery policies. We show a correlation between the risk aversion degree of disruption scenarios and the outcomes of the reconfiguration policies.

Simultaneous structural–operational control of supply chain dynamics and resilience

Abstract: This study develops a resilience control model and computational algorithm for simultaneous structural–operational design of supply chain (SC) structural dynamics and recovery policy control. Our model integrates both structural recovery control in the SC as a whole and the corresponding functional recovery control at individual firms in the SC. Such a comprehensive combination is unique in literature and affords more realistic application to SC resilience control decisions. The focus of our study is to advance insights into feedback-driven understanding of resilience within open system control context. We construct a model that allows theorizing the notion of SC resilience within a disruption dynamics profile as a product of degradation and recovery control loops and examine the conditions for changes of disruption profile states. We show that the deviations from the resilient trajectory are associated with structural and performance degradation, and the recovery operations in structural adaptation yield the performance recovery. We contribute to existing works by comprehensively modelling structural dynamics and operational dynamics within an integrated feedback-driven framework to enable proactive SC resilience control. Our approach conceptualizes a new perspective as compared to the more common closed system view where SC resilience is treated from the performance equilibrium point of view. The proposed approach can help explain and improve the firms’ operations in multiple ways. First, the combination of structural and functional dynamics can help revealing the latent supply–demand allocations which would be disrupted in case of particular changes in the SC design and suggest re-allocations of supply and demand Second, the model can be used to perform the dynamic analysis of SC disruption and recovery and to explain the reasons of SC performance degradation and restoration. This analysis can be further used to improve SC risk mitigation policies and recovery plans.

Ripple Effect in the Supply Chain: Definitions, Frameworks and Future Research Perspectives

Abstract: This chapter aims at delineating major features of the ripple effect and methodologies to mitigate the supply chain disruptions and recover in case of severe disruptions. It observes the reasons and mitigation strategies for the ripple effect in the supply chain and presents the ripple effect control framework that is comprised of redundancy, flexibility and resilience. Even though a variety of valuable insights has been developed in the given area in recent years, new research avenues and ripple effect taxonomies are identified for the near future. Two special directions are highlighted. The first direction is the supply chain risk analytics for disruption risks and the data-driven ripple effect control in supply chains. The second direction is the concept of low-certainty-need (LCN) supply chains.

A Model-Oriented System for Operational Forecasting of River Floods

Abstract: This article gives the results of developing and testing a system for operational river flood forecasting, which is based on a system of hydrological and hydrodynamic models, as well as ground-observation and satellite data. This system is implemented on the basis of service-oriented architecture. A specific feature of the system is fully automated implementation of the entire modeling cycle—from loading input data to interpreting and visualizing the results and alerting the interested parties. The theoretical basis for coherent functioning of all system components is the qualimetry of models and polymodel complexes developed by the authors. The practical implementation is based on open codes and freeware. The results of testing demonstrate the potential for a wide introduction of such systems in the activities of territorial authorities and emergency services.

Informative and Formal Description of Structure Dynamics Control Task of Cyber-Physical Systems

Abstract: The authors propose the informative and formal description of Structure Dynamics Control Task of Cyber-physical Systems (CPS). The main advantage of proposed poly-model multicriterion approach for the structure dynamics control tasks decision is that, in comparison with previously developed control models oriented on CPS simulation, proposed models allow to increase the effi ciency, validity and, in general, the quality of the SD control CPS due to the original combination of their static and dynamic models.

Methodology of Complex Objects Structural Dynamics Proactive Management and Control Theory and Its Application

Abstract: Methodological and methodical fundamentals of the complex objects (CO) proactive management and control theory based on the fundamental results obtained in the interdisciplinary field of system knowledge are proposed. The paper provides information on the developed innovative multiple-model complexes, combined methods, algorithms and techniques for solving various classes of problems of operational, structural and functional synthesis and management of the development of the regarded classes of CO. The tasks of controlling the structural dynamics of CO belong to the structural and functional synthesis class of problems and the formation of appropriate programs for managing and control of their development. The main difficulty and a special feature of the solution of the regarded problems is as follows. Determination of optimal control programs for the basic elements and subsystems of CO can be performed only after all functions and algorithms of information processing and control that should be implemented in these elements and subsystems are known. In its turn, the distribution of functions and algorithms by the elements and subsystems of CO depends on the structure and parameters of the control laws of these elements and subsystems. The difficulty of resolving this controversial situation is ex-acerbated by the fact that under the influence of various reasons, the composition and structure of the CO at different stages of their lifecycle changes over time. The given examples of solving practical problems for such subject areas as spacecrafts, logistics, and industrial production.

Performance Impact Analysis of Disruption Propagations in the Supply Chain

Abstract: Despite a wealth of literature on disruption considerations in the supply chain (SC), a method for quantification of the ripple effect that describes disruption propagation in the SC has not yet been developed. In addition, there are only a few studies that incorporate recovery into the performance impact assessment. This chapter develops a method to quantify the ripple effect in the SC with recovery policy considerations. We study a four-stage SC over time and consider both performance impact assessment and recovery decisions. The performance impact index developed is used to compare sales (revenue) in different SC designs to measure the estimated annual magnitude of the ripple effect. First, we compute optimal SC replanning for two disruption scenarios. Second, we estimate the performance impact of disruptions for six proactive SC designs. Finally, we compare the performance impact index of different SC designs and draw conclusions about the ripple effect in these SC designs along with recommendations for the selection of a proactive strategy. The performance impact index developed can be used to analyze how different markets are exposed to the ripple effect and how to compare different SC designs according to their resilience to severe disruptions.

A Model of an Integrated Analytics Decision Support System for Situational Proactive Control of Recovery Processes in Service-Modularized Supply Chain

Abstract: In the supply chain (SC) recovery process, a disruptive event, planning of the recovery control policy and implementation of this policy are distributed in time and subject to SC structural and parametrical dynamics. In other words, environment, SC structure and its operational parameters may change in the period between the planning of the recovery control policy and its implementation. As such, situational proactive control with combined use of simulation-optimization and analytics is proposed in the paper to improve processes of transition between a disrupted and a restored SC state. Implementation of situational proactive control can reduce investments in robustness and increase resilience by obviating the time traps in transition process control problems. This chapter develops a model of a decision support system for situational proactive control of SC recovery processes based on a combination of optimization and analytics techniques. More specifically, three dynamic models are developed and integrated with each other, i.e. a model of SC material flow control, a model of SC recovery control and a model of SC recovery control adjustment. The given models are developed within a cyber-physical SC framework based on the service modularization approach.

Model-driven system for operational forecasting of river flooding

Abstract: This article presents the results of the development and testing of a system for operational forecasting of river flooding. This system is based on the use of a complex of hydrological and hydrodynamic models and in-situ and satellite data. It is implemented on the basis of service-oriented architecture. A distinctive feature of the system is its full automation of the entire modeling cycle, from the initial data loading to the results of interpretation, visualization, and notification of stakeholders. The theoretical background for ensuring the coordinated functioning of system components is provided by the qualimetry of the models and polymodel complexes that have been developed by the authors. The implementation of the system’s software was performed using open source and free tools. The results of testing indicate the possibility of the widespread introduction of such systems for authorities and emergency services.

Model-Algorithmic Support for Abilities Calculating of Control System Based on Projection Operators

Abstract: One of the important problems in moving objects control system is the calculating of goal abilities, i.e., potential of the system to perform its missions in different situations. Thus, the preliminary analysis of information and technological and goal abilities of moving objects control system is very important in practice and can be used to obtain reasonable means of the moving objects exploitation under different conditions. In the paper model-algorithmic support for abilities calculating of control system based on projection operators are proposed.

Scheduling in Production, Supply Chain and Industry 4.0 Systems by Optimal Control: Fundamentals, State-of-the-Art, and Applications

Abstract: Specific scheduling problems with complex hybrid logical and terminal constraints, non-stationarity in process execution as well as complex interrelations between dynamics in process design, capacity utilization, and machine setups require further investigation and the application of a broad range of methodical approaches. One of these approaches is optimal control. The objectives of this survey are twofold. The first objective is to derive major contributions, application areas, limitations, as well as research and application recommendations for the future regarding optimal control applications to scheduling. The second objective is to explain control engineering models in terms of industrial engineering and production management. In this paper, we provided a survey on the applications of optimal control to scheduling in production, supply chain, and Industry 4.0 systems with a focus on the deterministic maximum principle. Optimal control approaches take a different perspective as mathematical programming methods which represent schedules as trajectories. We consider optimal control models, performance analysis qualitative methods, and computational methods for optimal control. We provide a brief historic overview and clarify major mathematical fundamentals whereby the control engineering terms are brought into correspondence with industrial engineering and management. The survey allowed the group-ing of models with only terminal constraints with application to master production scheduling, models with hybrid terminal-logical constraints with applications to short term job and flow shop scheduling, and hybrid structural-terminal-logical constraints with applications to customized assembly systems such as Industry 4.0. Computational algorithms in state, control, and conjunctive variable spaces are discussed. Finally, we derive major contributions, application areas of different control methods, and their limitations. This paper also delineates research and application recommendations for future research.

The Power of Doubt

Abstract: In this article an attempt is made to suggest an explanation of some internal reasons for two interconnected tendencies in European culture. The first tendency is connected with a special way of creating frontiers in the European Union where they have now acquired a largely nominal character. The second tendency is a compulsive expansionism of European culture which, for long periods of time in the past, used to be realized in the shape of colonialism, and has now taken the form of globalization. Both these tendencies are connected with what the author defines as a “doubt imperative”, which is not only a constant “efficient cause” of scientific discourse, but also a means of constituting everyday reality. The article discusses both the thematization of this “doubt imperative” in the history of ideas of the modern philosophical tradition from Descartes to Husserl, and its current understanding as a model for business and everyday life. The compulsive reproduction of the “doubt imperative” makes it possible to draw the conclusion that the “doubt imperative” belongs to the basic spheres of modern European culture and consciousness, i.e., that it is connected with the basic structure and style of the process of constituting European reality.

Application of Cyber-Physical System and Real-Time Control Construction Algorithm in Supply Chain Management Problem

Abstract: Various classes of cyber-physical systems are the basis of digital and computer-integrated production and the digital economy as a whole. They include measuring, telecommunication and control subsystems with embedded software in various types of hierarchical structures. At the present paper supply chains (SC) are considered as highly dynamic systems. To improve the efficiency of incoming information about the current state of the SC and timeliness of the data we suggest to use cyber-physical systems. Here we consider the SC execution problem in the case of unforeseen events, it is one of the important problems in SC management. The advantage of this approach is that cyber-physical systems have been applied and the classical methods of optimal program and position control have been transferred and modified for supply chain management.