Workflow simulation for operational decision support using YAWL and ProM

Simulation is widely used as a tool for analyzing business processes but is mostly focused on examining rather abstract steady-state situations. Such analyses are helpful for the initial design of a business process but are less suitable for operational decision making and continuous improvement. Here we describe a simulation system for operational decision support in the context of work ow management. To do this we exploit not only the work ow's design, but also logged data describing the system's observed historic behavior, and information extracted about the current state of the work ow. Making use of actual data capturing the current state and historic information allows our simulations to accurately predict potential near-future behaviors for dierent scenarios. The approach is supported by a practical toolset which combines and extends the work ow management system YAWL and the process mining framework ProM. This technical report contains a detailed description of how a simulation model including operational decision support can be generated by our software based on the running example

The Integration of Process Simulation Within the Business Architecture

To deal with increased competition and technological change, organizations need to strive for a continuous improvement of their business processes. To realize this, simulation models offer a suitable approach to test different process alternatives. In particular, discrete-event simulation employs stochastic models to support operational decision-making inside the organization. However, this operational focus might cause sub-optimization with respect to higher-level organizational goals. Therefore, an integrative view on the business architecture might align strategic, organizational and process perspectives. This has resulted in the expansion of the Process-Goal Alignmentmodeling technique with a simulation mechanism. This paper augments the previous research efforts by including simulation results expressed by confidence intervals, such that the results of process simulations can be accurately integrated with the overall business performance. The design of the business architecture simulation technique is guided by the Design Science Research methodology. This paper communicates about both the design and the demonstration of the simulation technique, while the evaluation of this artifact is subject to future research

Discrete Event Monte-Carlo Simulation Based Decision Support System for Business Process Management

This paper presents a discrete event Monte-Carlo simulation based decision support system (DSS), which helps in managing business processes. Process managers use available information on work load, resource availability, task time, etc. and use their experience to predict the performance of the process and take corrective action, if required. We describe here a method and a system which takes available information about work load, and resource availability, to predict near future system performance using discrete event simulation, and compares the expected performance with desired service level and alerts the manager to take corrective action. The DSS assists operational managers in handling more complexity in decision making. This paper describes the framework for design of such a system which can be used in conjunction to a business process Management system

Ranking of business process simulation tools with DEX/QQ hierarchical decision model

The omnipresent need for optimisation requires constant improvements of companies’ business processes (BPs). Minimising the risk of inappropriate BP being implemented is usually performed by simulating the newly developed BP under various initial conditions and “what-if” scenarios. An effectual business process simulations software (BPSS) is a prerequisite for accurate analysis of an BP. Characterisation of an BPSS tool is a challenging task due to the complex selection criteria that includes quality of visual aspects, simulation capabilities, statistical facilities, quality reporting etc. Under such circumstances, making an optimal decision is challenging. Therefore, various decision support models are employed aiding the BPSS tool selection. The currently established decision support models are either proprietary or comprise only a limited subset of criteria, which affects their accuracy. Addressing this issue, this paper proposes a new hierarchical decision support model for ranking of BPSS based on their technical characteristics by employing DEX and qualitative to quantitative (QQ) methodology. Consequently, the decision expert feeds the required information in a systematic and user friendly manner. There are three significant contributions of the proposed approach. Firstly, the proposed hierarchical model is easily extendible for adding new criteria in the hierarchical structure. Secondly, a fully operational decision support system (DSS) tool that implements the proposed hierarchical model is presented. Finally, the effectiveness of the proposed hierarchical model is assessed by comparing the resulting rankings of BPSS with respect to currently available results

Using Information Systems in Business Decisions

ABSTRACT: The paper presents the benefits of usage of information systems in decisions for businesses, which can reveale the optimal choice of the solution in order to increase competitivity in a strategic economy. Within a company’s computer system, systems for decision support are classified as systems for the management / management decision. They take data from specific transaction processing systems and helps management process at the various levels of decision making. These systems help to implement the decisions, orders and the decisions decomposition that is occurring in the system of management of the company. Operational decisions are found in specialized compartments and are available in the directive needed to conduct operational departments that have the peculiarities of origin. For simulation models are created the required applications and helps decision-makers to make the choice based on the measures imposed by reality and the actual conditions in which the business operates in the specific part. Assisting decision means a permanent dialogue with the user, so that the interface has a much greater importance than other systems. The user, person or group of persons through the role they play in making the decision, is considered part of the system

Hybrid Simulation-based Planning Framework for Agri-Fresh Produce Supply Chain

The ever-increasing demand for fresh and healthy products raises the economic importance of managing Agri-Fresh Produce Supply Chain (AFPSC) effectively. However, the literature review has indicated that many challenges undermine efficient planning for AFPSCs. Stringent regulations on production and logistics activities, production seasonality and high yield variations (quantity and quality), and products vulnerability to multiple natural stresses, alongside with their critical shelf life, impact the planning process. This calls for developing smart planning and decision-support tools which provides higher efficiency for such challenges. Modelling and simulation (M&S) approaches for AFPSC planning problems have a proven record in offering safe and economical solutions. Increase in problem complexity has urged the use of hybrid solutions that integrate different approaches to provide better understanding of the system dynamism in an environment characterised by multi-firm and multi-dimensional relationships. The proposed hybrid simulation-based planning framework for AFPSCs has addressed internal decision-making mechanisms, rules and control procedures to support strategic, tactical and operational planning decisions. An exploratory study has been conducted using semi-structured interviews with twelve managers from different agri-fresh produce organisations. The aim of this study is to understand management practices regarding planning and to gain insights on current challenges. Discussions with managers on planning issues such as resources constraints, outsourcing, capacity, product sensitivity, quality, and lead times have formed the foundation of process mapping. As a result, conceptual modelling process is then used to model supply chain planning activities. These conceptual models are inclusive and reflective to system complexity and decision sensitivity. Verification of logic and accuracy of the conceptual models has been done by few directors in AFPSC before developing a hybrid simulation model. Hybridisation of Discrete Event Simulation (DES), System Dynamics (SD), and Agent-Based Modelling (ABM) has offered flexibility and precision in modelling this complex supply chain. DES provides operational models that include different entities of AFPSC, and SD minds investments decisions according to supply and demand implications, while ABM is concerned with modelling variations of human behaviour and experience. The proposed framework has been validated using Table Grapes Supply Chain (TGSC) case study. Decision makers have appreciated the level of details included in the solution at different planning levels (i.e., operational, tactical and strategic). Results show that around 58% of wasted products can be saved if correct hiring policy is adopted in the management of seasonal labourer recruitment. This would also factor in more than 25% improved profits at packing house entity. Moreover, an anticipation of different supply and demand scenarios demonstrated that inefficiency of internal business processes might undermine the whole business from gaining benefits of market growth opportunities

Evaluating the impact of design decisions on the financial performance of manufacturing companies

Product design decisions can have a significant impact on the financial and operation performance of manufacturing companies. Therefore good analysis of the financial impact of design decisions is required if the profitability of the business is to be maximised. The product design process can be viewed as a chain of decisions which links decisions about the concept to decisions about the detail. The idea of decision chains can be extended to include the design and operation of the 'downstream' business processes which manufacture and support the product. These chains of decisions are not independent but are interrelated in a complex manner. To deal with the interdependencies requires a modelling approach which represents all the chains of decisions, to a level of detail not normally considered in the analysis of product design. The operational, control and financial elements of a manufacturing business constitute a dynamic system. These elements interact with each other and with external elements (i.e. customers and suppliers). Analysing the chain of decisions for such an environment requires the application of simulation techniques, not just to any one area of interest, but to the whole business i.e. an enterprise simulation. To investigate the capability and viability of enterprise simulation an experimental 'Whole Business Simulation' system has been developed. This system combines specialist simulation elements and standard operational applications software packages, to create a model that incorporates all the key elements of a manufacturing business, including its customers and suppliers. By means of a series of experiments, the performance of this system was compared with a range of existing analysis tools (i.e. DFX, capacity calculation, shop floor simulator, and business planner driven by a shop floor simulator)

The role of learning on industrial simulation design and analysis

The capability of modeling real-world system operations has turned simulation into an indispensable problemsolving methodology for business system design and analysis. Today, simulation supports decisions ranging from sourcing to operations to finance, starting at the strategic level and proceeding towards tactical and operational levels of decision-making. In such a dynamic setting, the practice of simulation goes beyond being a static problem-solving exercise and requires integration with learning. This article discusses the role of learning in simulation design and analysis motivated by the needs of industrial problems and describes how selected tools of statistical learning can be utilized for this purpose

Simulation modelling and strategic change : creating the sustainable enterprise

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