Optimising the preparedness capacity of enterprise resilience using mathematical programming

This article belongs to the Special Issue Mathematical Methods and Analysis for the Industrial Management and Business.In today's volatile business arena, companies need to be resilient to deal with the unexpected. One of the main pillars of enterprise resilience is the capacity to anticipate, prevent and prepare in advance for disruptions. From this perspective, the paper proposes a mixed-integer linear programming (MILP) model for optimising preparedness capacity. Based on the proposed reference framework for enterprise resilience enhancement, the MILP optimises the activation of preventive actions to reduce proneness to disruption. To do so, the objective function minimizes the sum of the annual expected cost of disruptive events after implementing preventive actions and the annual cost of such actions. Moreover, the algorithm includes a constraint capping the investment in preventive actions and an attenuation formula to deal with the joint savings produced by the activation of two or more preventive actions on the same disruptive event. The management and business rationale for proposing the MILP approach is to keep it as simple and comprehensible as possible so that it does not require highly mathematically skilled personnel, thus allowing top managers at enterprises of any size to apply it effortlessly. Finally, a real pilot case study was performed to validate the mathematical formulation.This work was supported by the Spanish State Research Agency (Agencia Estatal de Investigación) under the Reference No. RTI2018-101344-B-I00-AR

Modeling and Simulation of Supply Chains with AnyLogic

[ES] En este artículo se presenta una herramienta de software AnyLogic, para el modelado y simulación de la Cadena de Suministro (CS) proporcionando ayuda a la toma de decisiones a través de la simulación de escenarios de un mismo modelo de CS. AnyLogic da soporte a las metodologías de simulación más conocidas: sistemas de eventos discretos, dinámica de sistemas y modelado de agentes. Este artículo se centra en el contexto de dinámica de sistemas permitiendo la simulación de dos tipos de CS: colaborativa y no-colaborativa. Finalmente, se presenta un ejemplo ilustrativo en AnyLogic que permite comparar los dos modelos de CS (i) no-colaborativa vs. (ii) colaborativa, caracterizada por un modelo de Inventario Administrado por el Proveedor (en inglés Supplier Managed Inventory, SMI), en la que existe colaboración entre el proveedor y el fabricante de la CS.[EN] This paper proposes a simulation tool named AnyLogic for modelling and simulating of the Supply Chain(SC) in order to support the decision-making process through simulating scenarios of the same SC model.AnyLogic supports the most popular simulation methodologies: discrete events, system dynamics andmodelling agents. This article focuses on the context of system dynamics allowing carrying out thesimulation of two types of SC: collaborative and non-collaborative. Finally, an illustrative example isproposed in AnyLogic in order to compare two SC models (i) non-collaborative one vs. (ii) collaborativeone, characterised by applying the Supplier Managed Inventory (SMI), where there is collaboration betweenthe supplier and the manufacturer of the SC.Andres, B.; Sanchis, R.; Poler, R. (2016). Modelado y simulación de la cadena de suministro con AnyLogic®. Modelling in Science Education and Learning. 9(1):57-72. doi:10.4995/msel.2016.3520.SWORD577291Campuzano, F., & Mula, J. (2011). Supply Chain Simulation. doi:10.1007/978-0-85729-719-8Forrester, J. (1961). Dinámica Industrial. Buenos Aires, Argentina: Editorial El Ateneo.Sterman, J.D. (2000) Business Dynamics: Systems Thinking and Modelling for a Complex World. McGraw-Hill Higher Education, New York. ISBN: 978-0-07-231135-

A Cloud Platform to support Collaboration in Supply Networks

[EN] Collaboration is a trend in supply networks management, based on the jointly planning, coordination and integration of processes, participating all network entities. Due to the current characteristics of uncertainty in the markets and economic crisis, there is a need to encourage collaboration tools to reduce costs and increase trust and accountability to market requirements. This study presents an overview of the research carried out in the H2020 European Project: Cloud Collaborative Manufacturing Networks (C2NET), which is directed towards the development a cloud platform that consist of, optimization tools, collaboration tools to support and agile management of the network. The collaborative cloud platform allows to collect real time information coming from real-world resources and considering all the actors involved in the process. The collaborative cloud provides real time data gathered from the entire network partners in order to improve their decision-making processes.The research leading to these results has received funding from European Community’s H2020 Programme (H2020/2014-2020) under grant agreement n°636909, “Cloud Collaborative Manufacturing Networks (C2NET)”.Andrés Navarro, B.; Sanchis, R.; Poler, R. (2016). A Cloud Platform to support Collaboration in Supply Networks. International Journal of Production Management and Engineering. 4(1):5-13. https://doi.org/10.4995/ijpme.2016.4418SWORD5134

A reference architecture for the collaborative planning modelling process in multi-tier supply chain networks: a Zachman-based approach

A prominent and contemporary challenge for supply chain (SC) managers concerns the coordination of the efforts of the nodes of the SC in order to mitigate unpredictable market behaviour and satisfy variable customer demand. A productive response to this challenge is to share pertinent market-related information, on a timely basis, in order to effectively manage the decision-making associated with the SC production and transportation planning processes. This paper analyses the most well-known reference modelling languages and frameworks in the collaborative SC field and proposes a novel reference architecture, based upon the Zachman Framework (ZF), for supporting collaborative plan- ning (CP) in multi-level, SC networks. The architecture is applied to an automotive supply chain configuration, where, under a collaborative and decentralised approach, improvements in the service levels for each node were observed. The architecture was shown to provide the base discipline for the organisation of the processes required to manage the CP activity.The authors thanks the support from the project 'Operations Design and Management in Global Supply Chains (GLOBOP)' (Ref. DPI2012-38061-C02-01), funded by the Ministry of Science and Education of Spain, for the supply chain environment research contribution.Hernández Hormazábal, JE.; Lyons, AC.; Poler, R.; Mula, J.; Goncalves, R. (2014). A reference architecture for the collaborative planning modelling process in multi-tier supply chain networks: a Zachman-based approach. Production Planning and Control. 25(13-14):1118-1134. https://doi.org/10.1080/09537287.2013.808842S111811342513-14Al-Mutawah, K., Lee, V., & Cheung, Y. (2008). A new multi-agent system framework for tacit knowledge management in manufacturing supply chains. Journal of Intelligent Manufacturing, 20(5), 593-610. doi:10.1007/s10845-008-0142-0Baïna, S., Panetto, H., & Morel, G. (2009). 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Group Decision and Negotiation, 20(1), 79-114. doi:10.1007/s10726-010-9205-7Hernández, J. E., J. Mula, R. Poler, and A. C. Lyons. 2013. “Collaborative Planning in Multi-Tier Supply Chains Supported by a Negotiation-Based Mechanism and Multi-Agent System.”Group Decision and Negotiation Journal. doi:10.1007/s10726-013-9358-2.Jardim-Goncalves, R., Grilo, A., Agostinho, C., Lampathaki, F., & Charalabidis, Y. (2013). Systematisation of Interoperability Body of Knowledge: the foundation for Enterprise Interoperability as a science. Enterprise Information Systems, 7(1), 7-32. doi:10.1080/17517575.2012.684401Kampstra, R. P., Ashayeri, J., & Gattorna, J. L. (2006). Realities of supply chain collaboration. The International Journal of Logistics Management, 17(3), 312-330. doi:10.1108/09574090610717509Kim, W., Chung, M. J., Qureshi, K., & Choi, Y. K. (2006). WSCPC: An architecture using semantic web services for collaborative product commerce. 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Manufacturing Data Analytics for Manufacturing Quality Assurance

The authors acknowledge the European Commission for the support and funding under the scope of Horizon2020 i4Q Innovation Project (Agreement Number 958205) and the remaining partners of the i4Q Project Consortium.Nowadays, manufacturing companies are eager to access insights from advanced analytics, without requiring them to have specialized IT workforce or data science advanced skills. Most of current solutions lack of easy-to-use advanced data preparation, production reporting and advanced analytics and prediction. Thanks to the increase in the use of sensors, actuators and instruments, European manufacturing lines collect a huge amount of data during the manufacturing process, which is very valuable for the improvement of quality in manufacturing, but analyzing huge amounts of data on a daily basis, requires heavy statistical and technology training and support, making them not accessible for SMEs. The European i4Q Project, aims at providing an IoT-based Reliable Industrial Data Services (RIDS), a complete suite consisting of 22 i4Q Solutions, able to manage the huge amount of industrial data coming from cheap cost-effective, smart, and small size interconnected factory devices for supporting manufacturing online monitoring and control. This paper will present a set of i4Q services, for data integration and fusion, data analytics and data distribution. Such services, will be responsible for the execution of AI workloads (including at the edge), enabling the dynamic deployment industrial scenarios based on a cloud/edge architecture. Monitoring at various levels is provided in i4Q through scalable tools and the collected data, is used for a variety of activities including resource monitoring and management, workload assignment, smart alerting, predictive failure and model (re)training.publishersversionpublishe

Dealing with the Alignment of Strategies Within the Collaborative Networked Partners

Part 2: Collaborative NetworksInternational audienceOver the last years, enterprises awareness towards establishing collaborative processes has significantly grown due to the benefits obtained when participating in a collaborative network (CN). This paper is a continuation of the work developed by Andres and Poler [1] in which a set of collaborative processes are identified. This paper particularly focuses on the strategies alignment (SA) process, highlighting its influence on the success of a CN. A preliminary framework is outlined to address the collaborative process of SA, consisting of a model, a method and a methodology. Highlighting the importance of the use of cloud-based systems to support small and medium enterprises (SMEs) to deal with the SA process. The implementation of the proposed framework allows identifying what are the strategies that are aligned and how its activation increases the network performance, resulting in more stable and sustainable collaborative relationships, and assuring the proper operation of the CNs

An Expert System for Inventory Replenishment Optimization

Abstract: Companies survive in saturated markets trying to be more productive and more efficient. In this context, to manage more accurately the finished goods inventories becomes critical for make to stock production systems companies. In this paper an inventory replenishment expert system with the objectives of improving quality service and reducing holding costs is proposed. The Inventory Replenishment Expert System (IRES) is based on a periodic review inventory control and time series forecasting techniques. IRES propose the most effective replenishment strategy for each supply classed derived of an ABC-XYZ Analysis

A Novel MILP Model for the Production, Lot Sizing, and Scheduling of Automotive Plastic Components on Parallel Flexible Injection Machines with Setup Common Operators

In this article, a mixed integer linear program (MILP) model is proposed for the production, lot sizing, and scheduling of automotive plastic components to minimize the setup, inventory, stockout, and backorder costs, by taking into account injection molds as the main index to schedule on parallel flexible injection machines. The proposed MILP considers the minimum and maximum inventory capacities and penalizes stockout. A relevant characteristic of the modeled problem is the dependence between mold setups to produce plastic components. The lot sizing and scheduling problem solution results in the assignment of molds to machines during a specific time period and in the calculation of the number of components to be produced, which is often called lot size, following a sequence-dependent setup time. Depending on the machine on which the mold is setup, the number of units to be produced will be distinct because machines differ from one another. The stock coverage, defined in demand days, is also included in the MILP to avoid backorders, which is highly penalized in the automotive supply chain. Added to this, the proposed model is extended by considering setup common operators to respond to and fulfill the constraints that appear in automotive plastic enterprises. In this regard, the MILP presented solves a lot-sizing and scheduling problem, emerged in a second-tier supplier of a real automotive supply chain. Finally, this article validates the MILP by performing experiments with different sized instances, including small, medium, and large. The large-sized dataset is characterized by replicating the amount of data used in the real enterprise, which is the object of this study. The goodness of the model is evaluated with the computational time and the deviation of the obtained results as regards to the optimal solution

Matheuristic Algorithm for Job-Shop Scheduling Problem Using a Disjunctive Mathematical Model

This paper focuses on the investigation of a new efficient method for solving machine scheduling and sequencing problems. The complexity of production systems significantly affects companies, especially small- and medium-sized enterprises (SMEs), which need to reduce costs and, at the same time, become more competitive and increase their productivity by optimizing their production processes to make manufacturing processes more efficient. From a mathematical point of view, most real-world machine scheduling and sequencing problems are classified as NP-hard problems. Different algorithms have been developed to solve scheduling and sequencing problems in the last few decades. Thus, heuristic and metaheuristic techniques are widely used, as are commercial solvers. In this paper, we propose a matheuristic algorithm to optimize the job-shop problem which combines a genetic algorithm with a disjunctive mathematical model, and the Coin-OR Branch & Cut open-source solver is employed. The matheuristic algorithm allows efficient solutions to be found, and cuts computational times by using an open-source solver combined with a genetic algorithm. This provides companies with an easy-to-use tool and does not incur costs associated with expensive commercial software licenses

A Decision-Making Tool for Algorithm Selection Based on a Fuzzy TOPSIS Approach to Solve Replenishment, Production and Distribution Planning Problems

A wide variety of methods and techniques with multiple characteristics are used in solving replenishment, production and distribution planning problems. Selecting a solution method (either a solver or an algorithm) when attempting to solve an optimization problem involves considerable difficulty. Identifying the best solution method among the many available ones is a complex activity that depends partly on human experts or a random trial-and-error procedure. This paper addresses the challenge of recommending a solution method for replenishment, production and distribution planning problems by proposing a decision-making tool for algorithm selection based on the fuzzy TOPSIS approach. This approach considers a collection of the different most commonly used solution methods in the literature, including distinct types of algorithms and solvers. To evaluate a solution method, 13 criteria were defined that all address several important dimensions when solving a planning problem, such as the computational difficulty, scheduling knowledge, mathematical knowledge, algorithm knowledge, mathematical modeling software knowledge and expected computational performance of the solution methods. An illustrative example is provided to demonstrate how planners apply the approach to select a solution method. A sensitivity analysis is also performed to examine the effect of decision maker biases on criteria ratings and how it may affect the final selection. The outcome of the approach provides planners with an effective and systematic decision support tool to follow the process of selecting a solution method