Industrial cost modelling and multi-objective optimisation for decision support in production systems development

Recent developments in cost modelling, simulation-based multi-objective optimisation, and post-optimality analysis have enabled the integration of costing data and cost estimation into a new methodology for supporting economically sound decision-making in manufacturing enterprises. Within this methodology, the combination of production engineering and financial data with multi-objective optimisation and post-optimality analysis has been proven to provide the essential information to facilitate knowledge-driven decision-making in real-world production systems development. The focus of this paper is to present the incremental cost modelling technique specifically designed for the integration with discrete-event simulation models and multi-objective optimisation within this methodology. A complete example, using the simulation model and data modified from a previous real-world case study, is provided in this paper to illustrate how the methodology and cost modelling are applied for the optimal investment decision support.     

Relevancy and role of whole-farm models in supporting smallholder farmers in planning their agricultural season

The way a model is designed to assist farmers in their decision-making may influence how it is understood and perceived by farmers and shape interactions between farmers and model users (researcher, advisor). This study compared the strengths and weaknesses of three types of whole farm models used by researchers to assist 18 crop-livestock farmers in Burkina Faso in planning the next agricultural season. Due to its simplicity, the static simulation tool of annual farm stocks and flows led to superior changes in the farmers' knowledge and practices. The rule-based dynamic simulation tool helped the researchers grasp farmers' decision-making processes but was difficult for farmers to understand due to the discrepancy between its multi-annual time step and the farmers' short-term planning horizon. The optimisation tool stimulated more strategic discussions regarding paths to improve farm income despite a design that was distant from the farmers' reality.     

A geospatial framework to support integrated biogeochemical modelling in the United Kingdom

Anthropogenic impacts on the aquatic environment, especially in the context of nutrients, provide a major challenge for water resource management. The heterogeneous nature of policy relevant management units (e.g. catchments), in terms of environmental controls on nutrient source and transport, leads to the need for holistic management. However, current strategies are limited by current understanding and knowledge that is transferable between spatial scales and landscape typologies. This study presents a spatially-explicit framework to support the modelling of nutrients from land to water, encompassing environmental and spatial complexities. The framework recognises nine homogeneous landscape units, distinct in terms of sensitivity of nutrient losses to waterbodies. The functionality of the framework is demonstrated by supporting an exemplar nutrient model, applied within the Environmental Virtual Observatory pilot (EVOp) cloud cyber-infrastructure. We demonstrate scope for the use of the framework as a management decision support tool and for further development of integrated biogeochemical modelling.     

A framework for systems analysis for decision support systems

Comprehensive and elaborate systems analysis techniques have been developed in the past of routine and operational information systems. Developing support systems for organizational decision-making requires new tools and methodologies. We present a new framework for data collection and decision analysis which is useful for developing decision support systems. This task analysis methodology encompasses (1) event analysis, (2) participant analysis, and (3) decision content analysis. With a proper coding manual, it provides a framework for collecting relevant and detailed information required for decision support design and implementation. Further research is suggested for application and evaluation of the methodology in real-life DSS environments.     

A framework for decision support systems evaluation

In this paper, we present a framework for the Decision Support Systems evaluation problem. Using the Gorry-Scott Morton's framework for information systems, we develop several evaluation methods that structure the evaluation process. The framework determines the best methods of evaluation that are suitable to the characteristics of the Decision Support System concerned. Finally, we use the framework to evaluate two widely used Decision Support Systems.     

A knowledge-based tool for designing cyber physical production systems

Changing production systems and product requirements can trace their origin in volatile customer behaviour and evolving product requirements. This dynamic nature of customer requirements has been described as a constantly moving target, thus presenting a significant challenge for several aspects of product development. To deal with this constant and sometimes unpredictable product evolution, cyber physical production systems (CPPS) that employ condition monitoring, self-awareness and reconfigurability principles, have to be designed and implemented. This research contributes a CPPS design approach that proactively provides the required CPPS design knowledge. This approach aims to minimise or avoids future consequences and disruptions on the CPPS. This knowledge needs to be provided at the right time whilst not being intrusive to the production system designer’s cognitive activity. To effectively deal with the complexity of the cyber physical production system design activity with a manual method would lead to a time consuming, and complex support tool which is hard to implement, and difficult to use. The CPPS design approach has therefore been implemented in a prototype digital factory tool. This paper describes in detail the system requirements and system architecture for this tool. In order to establish the effectiveness of the proposed approach for designing cyber physical production systems, the prototype digital factory tool has been evaluated with a case study and a number of semi-structured interviews with both industrial and scientific stakeholders. The encouraging results obtained from this research evaluation have shown that such an approach for supporting the CPPS design activity makes stakeholders aware of their decision consequences and is useful in practice. This result can lead the way for the development and integration of such knowledge-based decision-making approaches within state-of-the-art digital factory and Computer Aided Engineering Design (CAED) tools.     

A modelling framework for complementary design of production planning and control systems

In a business era characterized by a dazzling rate of change, the improvement of production planning and control begins to be a main objective for manufacturing industries. This paper postulates four main statements to be considered for the design of production planning and control systems (PPC-systems) comprising human and technical sub-systems. The first is that production models required for the design of PPC-systems (i.e. design models) cannot be identical to production models required for planning and control of production systems (i.e. regulatory models). The design of PPC-systems must primarily focus on the quality of interaction between the regulatory models. This insight supports the second statement, which postulates that the design of PPC-systems requires a complementary design approach. Complementary design means to take explicitly into account that human and technical sub-systems- based on the differences in strengths and weaknesses of both- can achieve through their interaction a new quality, possible neither to human nor technical sub-systems alone. The third statement is that a complementary design of PPC-systems will only be possible if a fundamental change of mind from a static to a dynamic as well as from a technical to a socio-technical perception (i.e. a complete perception) of production systems takes place. Without a complete perception of production systems, designed PPC-systems will not be sufficiently reliable, maintainable and flexible, will be difficult to comprehend, and their elements will not be re-usable for further applications. The fourth statement is that the integral support of the design process requires a dual modelling framework comprising a meta- and an object-model. Considering these fundamental insights that were confirmed by a practical case study, a dual modelling framework for the design of PPC-systems which incorporates criteria for complementary design is outlined.     

A visualization tool to support decision making in environmental and biological planning

Large-scale ecosystem management involves consideration of many factors for informed decision making. The EverVIEW Data Viewer is a cross-platform desktop decision support tool to help decision makers compare simulation model outputs from competing plans for restoring Florida's Greater Everglades. The integration of NetCDF metadata conventions into EverVIEW allows end-users from multiple institutions within and beyond the Everglades restoration community to share information and tools. Our development process incorporates continuous interaction with targeted end-users for increased likelihood of adoption. One of EverVIEW's signature features is side-by-side map panels, which can be used to simultaneously compare species or habitat impacts from alternative restoration plans. Other features include examination of potential restoration plan impacts across multiple geographic or tabular displays, and animation through time. As a result of an iterative, standards-driven approach, EverVIEW is relevant to large-scale planning beyond Florida, and is used in multiple biological planning efforts in the United States.     

A refinement framework to support validation and maintenance of knowledge-based systems

In this article, we argue that a refinement tool capable of assisting the maintainer of a Knowledge-Based System (KBS) with updating and upgrading of its knowledge base can substantially expand the scope of his activities and prolong the life of the system. We show that refinement tasks involved in KBS maintenance are similar to those taking place at the development stage, and thus a uniform refinement framework can be designed to support both activities. After defining refinement tasks expected to take place at different stages of a KBS's life cycle, we present a refinement framework capable of supporting them. It utilizes a small number of domain-independent heuristics to search for possible rule revisions which are expected to eliminate errors detected during KBS validation. An extended example is given to illustrate how different refinement tasks are carried out.     

A framework to identify primitives that represent usability within Model-Driven Development methods

ContextNowadays, there are sound methods and tools which implement the Model-Driven Development approach (MDD) satisfactorily. However, MDD approaches focus on representing and generating code that represents functionality, behaviour and persistence, putting the interaction, and more specifically the usability, in a second place. If we aim to include usability features in a system developed with a MDD tool, we need to extend manually the generated code.ObjectiveThis paper tackles how to include functional usability features (usability recommendations strongly related to system functionality) in MDD through conceptual primitives.MethodThe approach consists of studying usability guidelines to identify usability properties that can be represented in a conceptual model. Next, these new primitives are the input for a model compiler that generates the code according to the characteristics expressed in them. An empirical study with 66 subjects was conducted to study the effect of including functional usability features regarding end users’ satisfaction and time to complete tasks. Moreover, we have compared the workload of two MDD analysts including usability features by hand in the generated code versus including them through conceptual primitives according to our approach.ResultsResults of the empirical study shows that after including usability features, end users’ satisfaction improves while spent time does not change significantly. This justifies the use of usability features in the software development process. Results of the comparison show that the workload required to adapt the MDD method to support usability features through conceptual primitives is heavy. However, once MDD supports these features, MDD analysts working with primitives are more efficient than MDD analysts implementing these features manually.ConclusionThis approach brings us a step closer to conceptual models where models represent not only functionality, behaviour or persistence, but also usability features.     

A proposal for modelling usability in a holistic MDD method

Holistic methods for Model-Driven Development (MDD) aim to model all the system features in a conceptual model. This conceptual model is the input for a model compiler that can generate software systems by means of automatic transformations. However, in general, MDD methods focus on modelling the structure and functionality of systems, relegating the interaction and usability features to manual implementations at the last steps of the software development process. Some usability features are strongly related to the functionality of the system and their inclusion is not so easy. In order to facilitate the inclusion of functional usability features from the first steps of the development process and bring closer MDD methods to the holistic perspective, we propose a Usability Model. The Usability Model gathers conceptual primitives that represent functional usability features in a sufficiently abstract way so that the model can be used with different holistic MDD methods. This paper defines all the primitives that can be used to represent functional usability features. Moreover, we have defined a process to include the Usability Model in any MDD method without affecting its existing conceptual model. The proposal is based on model-to-model and model-to-code transformations. As proof of concept, we have applied our proposal to an existing MDD method called the OO-method and we have measured its efficiency.     

A simulation-based risk network model for decision support in project risk management

This paper presents a decision support system (DSS) for the modeling and management of project risks and risk interactions. This is a crucial activity in project management, as projects are facing a growing complexity with higher uncertainties and tighter constraints. Existing classical methods have limitations for modeling the complexity of project risks. For example, some phenomena like chain reactions and loops are not properly taken into account. This will influence the effectiveness of decisions for risk response planning and will lead to unexpected and undesired behavior in the project. Based on the concepts of DSS and the classical steps of project risk management, we develop an integrated DSS framework including the identification, assessment and analysis of the risk network. In the network, the nodes are the risks and the edges represent the cause and effect potential interactions between risks. The proposed simulation-based model makes it possible to re-evaluate risks and their priorities, to suggest and test mitigation actions, and then to support project manager in making decisions regarding risk response actions. An example of application is provided to illustrate the utility of the model.     

Climate-Agriculture-Modeling and Decision Tool (CAMDT): A software framework for climate risk management in agriculture

Seasonal climate forecasts (SCFs) have received a lot of attention for climate risk management in agriculture. The question is, how can we use SCFs for informing decisions in agriculture? SCFs are provided in formats not so conducive for decision-making. The commonly issued tercile probabilities of most likely rainfall categories i.e., below normal (BN), near normal (NN) and above normal (AN), are not easy to translate into metrics useful for decision support. Linking SCF with crop models is one way that can produce useful information for supporting strategic and tactical decisions in crop production e.g., crop choices, management practices, insurance, etc. Here, we developed a decision support system (DSS) tool, Climate-Agriculture-Modeling and Decision Tool (CAMDT), that aims to facilitate translations of probabilistic SCFs to crop responses that can help decision makers adjust crop and water management practices that may improve outcomes given the expected climatic condition of the growing season.     

A simulation cloud monitoring framework and its evaluation model

Simulation Cloud can help users to carry out the simulation tasks in various stages quickly and easily by renting instead of buying all the needed resources, such as the computing hardware, simulation devices, software, and models. A monitoring system is necessary, which can dynamically collect information about the characteristics and status of resources in real time. In this paper, we design a Simulation Cloud Monitoring Framework (SCMF), which is a Monitoring Framework based on Simulation Cloud. The main functions of SCMF include: 1. Collecting performance information of Simulation Cloud (including physical resources and virtual resources). 2. Processing the collected performance information, providing ranking information about resource consumption as the customized service to service layer. 3. Detecting abnormal behaviors on Simulation Cloud in real time.The SCMF is based on hierarchical design. It consists of Root Monitoring Node (RMN), Federation Monitoring Node (RMN), and Main Monitoring Node (MMN). There is only one RMN in SCMF. It is responsible for collecting metadata about Simulation Cloud. For robustness, there are several FMNs in a federation. One is primary FMN and others are backup FMNs. MMN is implementing on every host in Simulation Cloud., MMN is responsible for collecting performance information about the host and virtual nodes. In the paper, it designs Sequence-Bucket strategy, which supports quick response for ranking information about resource consumption. It also designs two strategies: Rank-FMN (Federation Monitor Node) strategy and Huffman-Like Strategy. Huffman-Like Strategy combines small federations to reduce total consumption of SCMF, while Rank-FMN strategy is a load balancing strategy, which relieves the bottleneck of FMNs and spreads the loads equally among FMNs. The characteristics of SCMF are real-time, scalability, robustness, light weight, manageability, and archivability. Meanwhile, we design evaluation models for SCMF, which can provide quantitative results of monitoring accuracy and monitoring cost. The simulation results show that SCMF is accurate, low cost and can response in real-time.     

A novel data-driven approach to support decision-making during production scale-up of assembly systems

In today's manufacturing settings, a sudden increase in the customer demand may enforce manufacturers to alter their manufacturing systems either by adding new resources or changing the layout within a restricted time frame. Without an appropriate strategy to handle this transition to higher volume, manufacturers risk losing their market competitiveness. The subjective experience-based ad-hoc procedures existing in the industrial domain are insufficient to support the transition to a higher volume, thereby necessitating a new approach where the scale-up can be realised in a timely, systematic manner. This research study aims to fulfill this gap by proposing a novel Data-Driven Scale-up Model, known as DDSM, that builds upon kinematic and Discrete-Event Simulation (DES) models. These models are further enhanced by historical production data and knowledge representation techniques. The DDSM approach identifies the near-optimal production system configurations that meet the new customer demand using an iterative design process across two distinct levels, namely the workstation and system levels. At the workstation level, a set of potential workstation configurations are identified by utilising the knowledge mapping between product, process, resource and resource attribute domains. Workstation design data of selected configurations are streamlined into a common data model that is accessed at the system level where DES software and a multi-objective Genetic Algorithm (GA) are used to support decision-making activities by identifying potential system configurations that provide optimum scale-up Key Performance Indicators (KPIs). For the optimisation study, two conflicting objectives: scale-up cost and production throughput are considered. The approach is employed in a battery module assembly pilot line that requires structural modifications to meet the surge in the demand of electric vehicle powertrains. The pilot line is located at the Warwick Manufacturing Group, University of Warwick, where the production data is captured to initiate and validate the workstation models. Conclusively, it is ascertained by experts that the approach is found useful to support the selection of suitable system configuration and design with significant savings in time, cost and effort.     

A new generation of decision support systems for advanced manufacturing enterprises

The characteristics and environment of advanced manufacturing enterprises are analysed. A process model is briefly proposed to formulate the structured, semistructured, and unstructured decision problems. A framework for a decision support system is proposed that is capable of coordinating with the hybrid models and targeted to build an open system which can be self-organized to respond to a changing and unpredictable environment. Belief network and object-oriented technology are employed to help the system reason and reconfigure itself.     

A decision support system for design of mass production machining lines composed of stations with rotary or mobile table

This paper describes a decision support system (DSS) developed in order to offer to machining line designers a cognitive aid for early design stages. The aim of DSS is to assist the decision makers in finding the configuration of a new line that will meet quality and productivity requirements and minimize the investment costs. The current version of DSS is oriented to design of mass production machining lines composed of machines with rotary or mobile tables. This decision support system is based on mathematical models and methods which were devised to provide the designers with the optimal parameters of new line configuration including the required number of working stations of different types, the number of working positions at each station and spindle heads at each working position. The system is implemented under Autodesk Inventor and includes the modules for part modeling, process planning and machining system configuration. Its modular character and open architecture make upgrading with new mathematical tools suitable for other machining systems easy and fast. Moreover, it can be employed either as a separate software or integrated in a Product Life-cycle Management (PLM) tool.     

Modelling socio-ecological systems with MAIA: A biogas infrastructure simulation

Similar to other renewable energy technologies, the development of a biogas infrastructure in the Netherlands is going through social, institutional and ecological evolution. To study this complex evolutionary process, we built a comprehensive agent-based model of this infrastructure. We used an agent-based modelling framework called MAIA to build this model with the initial motivation that it facilitates modelling complex institutional structures. The modelling experience however proved that MAIA can also act as an integrated solution to address other major modelling challenges identified in the literature for modelling evolving socio-ecological systems. Building on comprehensive reviews, we reflect on our modelling experience and address four key challenges of modelling evolving socio-ecological systems using agents: (1) design and parameterization of models of agent behaviour and decision-making, (2) system representation in the social and spatial dimension, (3) integration of socio-demographic, ecological, and biophysical models, (4) verification, validation and sensitivity analysis of such ABMs.     

Key factors for operational performance in manufacturing systems: Conceptual model,systematic literature review and implications

PurposeThe performance of discrete items manufacturing systems (MS) is a primary concern of industrial firms. However, the understanding of the interrelations between performance and its key factors requires further advancements. Thus, several questions remain unanswered in the Operations and Production Management (OPM) field to understand and manage the relationship between these key factors. To address these challenges, this paper conceptualizes and examines the relevant antecedents and essential elements for the design and optimization of competitive MS.Design/methodology/approachFirst, drawing on the consolidated OPM literature, a novel conceptual model was developed incorporating the conceptual relationships essential to MS performance. Second, we conducted a systematic literature review based on the PRISMA protocol to analyze and validate the proposed conceptual model and to indicate the field’s current knowledge gaps and future research directions.FindingsFindings validated the proposed conceptual model by establishing the complex causal interrelations among key factors that influence discrete MS performance. Moreover, we found that the operational performance of discrete MS is multidimensional and directly dependent on the variables and mechanisms associated with the production flow. Findings also demonstrated that the degree of importance of the antecedents of MS performance vary and are temporally interrelated. Lastly, the paper advances the understanding of MS by revealing the predominance of quantitative approaches (e.g., discrete events simulation and closed mathematical models) in the literature as well as an emphasis on describing these approaches rather than characterizing MS appropriately.Research and Practical implicationsThis paper extends our knowledge on the operational performance challenges in discrete MS by proposing a visual, direct, and intuitive conceptual model that enables firms to better comprehend these complex challenges. This research also answers ongoing calls for investigations of the antecedents and elements of competitive MS design and optimization. Our findings show that decision-making in discrete MS is established temporally based on strategic, operational, and control definitions, influencing firms’ operational performance. Finally, since it draws on seminal OPM literature specializing in MS, this study informs scholars, industrial managers, and aid decision-making about discrete MS.Originality/valueThe first original aspect of this study lies in bridging the gaps identified in the OPM literature by providing a robust conceptual framework that highlights the key factors of operational performance in discrete MS. Its second original aspect is that it adopts different information sources in an independent and complementary way to achieve greater generalizability and robustness of the contributions.     

The human immune system as an information systems security reference model

The controls an organization places in its information systems are largely determined by its employee's thinking. Employee awareness of system vulnerabilities and the recognition that information is a strategically important organizational resource are two central ideas critical to effective information systems security thinking. For many years a military physical security environment has been the reference model (or a way of thinking) to which people refer when attempting to organize their thoughts about the complex systems security environment. While certainly still of use, this reference model has severely limited the thinking of those of us in the systems security field. This article defines both a new reference model with which people can view information systems security and several reasons why this new reference model should be adopted.