simulation based application Decision support in Real-time

simulation based application Decision support in Real-time for Efficient Agile Manufacturing

FoF.NMP.2012-6 Collaborative Project Grant Agreement: 314364

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Final Report

partner(s) IAO IAO – UL – DCU – BSCI – NEX – LEO – BAL – USTUTT – IFX

author(s) Joachim Lentes, Holger Eckstein (Coord.) Carmen Constantinescu (IAO) – Ivor Lanning (UL) – Anna Rotondo (DCU) – Rob Holland (BSCI) – Sébastien Robin (NEX) – Martin Bayer, Jochen Eichert (LEO) – Osman Balkan (BAL) – Olga Lange (USTUTT) – Stefan Heilmayer, Can Sun (IFX)

date 2015-11-30 status FINAL


© DREAM Consortium Page 2/35

Copyright

© 2012 – 2015, DREAM Consortium This document may not be copied, reproduced, or modified in whole or in part for any purpose without written permission from the DREAM Consortium. In addition to such written permission to copy, reproduce or modify this document in whole or part, an acknowledgement of the authors of the document and all applicable portions of the copyright notice must be clearly referenced. All rights reserved. This document may change without notice. The DREAM Consortium consists of the following partners: IAO Fraunhofer-Institut für Arbeitswirtschaft und Organisation, Germany UL University of Limerick, Ireland DCU Dublin City University, Ireland BSCI Boston Scientific Cork Ltd., Ireland NEX Nexedi SA, France LEO Leotech GmbH, Germany BAL Balkan Textile and Cotton Gin Machinery Ltd., Turkey USTUTT Universität Stuttgart, Germany IFX Infineon Technologies AG, Germany

Disclaimer: The information in this document is provided as is and no guarantee or warranty is given or implied that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability.



Table of Contents

1 Executive Summary ...................................................................................................... 6 2 Project context and objectives ....................................................................................... 7 3 Scientific and technical results .................................................................................... 11

3.1 Work Package 2 – Requirements & Pilot Cases Specification ....................................... 11 3.1.1 Task 2.1 Requirements Engineering ......................................................................... 11 3.1.2 Task 2.2 Pilot Cases Design ...................................................................................... 12 3.1.3 Summary for WP2 ..................................................................................................... 13

3.2 Work Package 3 – DREAM Methodologies .................................................................... 13 3.2.1 Task 3.1 System Knowledge & Decisions ................................................................. 13 3.2.2 Task 3.2 Simulation Methodologies ........................................................................... 14 3.2.3 Task 3.3 System Knowledge Extraction .................................................................... 15 3.2.4 Task 3.4 Human-System Interaction ......................................................................... 16 3.2.5 Summary for WP3 ..................................................................................................... 17

3.3 Work Package 4 – DREAM Platform .............................................................................. 18 3.3.1 Task 4.1 System Architecture and Integration ........................................................... 18 3.3.2 One Task 4.2 High Performance Simulation Framework ........................................... 19 3.3.3 Task 4.3 Knowledge Extraction Tool ......................................................................... 20 3.3.4 Task 4.4 User Interface Tool ..................................................................................... 21 3.3.5 Summary for WP4 ..................................................................................................... 22

3.4 Work Package 5 – Pilot Cases Implementation & Validation .......................................... 22 3.4.1 Task 5.1 Industrial Pilots Preparation ........................................................................ 23 3.4.2 Task 5.2 Industrial Pilots Validation ........................................................................... 24 3.4.3 Summary for WP5 ..................................................................................................... 25

3.5 Work Package 6 – Platform Demonstration .................................................................... 26 3.5.1 Task 6.1 Preparation of Demonstration ..................................................................... 26 3.5.2 Task 6.2 Pilots Demonstration ................................................................................... 27 3.5.3 Summary for WP6 ..................................................................................................... 28

4 Potential Impact, Dissemination and Exploitation ........................................................ 29 4.1 Potential Impact .............................................................................................................. 29 4.2 Main Dissemination Activities .......................................................................................... 30 4.3 Exploitation of Results ..................................................................................................... 31

5 Public Website ............................................................................................................ 34 6 References .................................................................................................................. 35



List of Figures

Figure 1: WP2 Basic Workplan ....................................................................................................... 12 Figure 2: Basic workplan of Task 3.4 .............................................................................................. 17 Figure 3 Main modules of DREAM public demonstration ................................................................ 26



Abbreviations

BPMN Business Process Modelling Notation CMSD Integration Definition Language DREAM simulation based application Decision support in Real-time for Efficient Agile

Manufacturing DoW Description of Work IDEF Core Manufacturing Simulation Data SysML Systems Modelling Language



1 Executive Summary

The scale and complexity of the modern manufacturing systems make the integration of predictive simulation modelling and optimisation methodologies into routine decision making processes essential for industrial companies to maintain and advance their competitiveness. Therefore, the following objectives have been defined for the project DREAM (simulation based application Decision support in Real-time for Efficient Agile Manufacturing):

1. To increase the competitiveness of European Manufacturing Companies through the provision of multi-level just-in-time simulation based application decision support.

2. To engineer a semantic free open simulation application development platform to promote simulation based applications by European Manufacturing Companies, IT consultants, Open Source community and Researchers.

3. To address the multi-faceted barriers to the adoption of advanced simulation decision support technologies by manufacturing companies, especially SMEs, by developing methodologies to address system knowledge management and human-system interaction challenges.

4. Using the semantic free simulation application platform to implement novel applications to support decisions at multi-levels in European Manufacturing Companies.

To achieve the defined objectives and to ensure the applicability of the results of the project, the research and development work in DREAM is driven and guided by industrial pilot cases from different branches, i.e. medical technology, small series plastic parts, textile machinery and semiconductors. Essential results achieved by DREAM are

• A platform based on the Open Source ERP-system ERP5 to embed the components • ManPy (Manufacturing in Python): an expandable library of manufacturing objects written

exclusively in Python, making use of the SimPy library to facilitate discrete-event simulation • A knowledge extraction (KE) tool to link production data with simulation software • DREAM GUI: A browser based drag and drop graphical user interface written in JavaScript

using several libraries (JSplumb, JIO etc.) to simplify the creation of browser-based user interfaces



2 Project context and objectives

DREAM (simulation based application Decision support in Real-time for Efficient Agile Manufacturing, FoF-NMP-314364) is a project funded by the European Commission in the frame of the Factories-of-the-Future Public-Private-Partnership. The project started October 1st, 2012 and has had a duration of three years. DREAM brought together innovative industrial companies, leading research and higher education institutions as well as an Open Source-based software company: Leotech Rapid Prototyping und Werkzeugbau GmbH, Balkan Tekstil Makilanari Sanayi Ve Ticaret Limited Sirketi, Boston Scientific Cork Limited, Infineon Technologies AG, Fraunhofer IAO, University of Limerick, Dublin City University, and University of Stuttgart. The scale and complexity of the modern manufacturing systems make essential the integration of predictive simulation modelling and optimisation methodologies into routine decision-making processes. This is not only the case for large companies but also for small- and medium sized companies, e.g. when transitioning from handcraft-like approaches to industrial production of complex goods and series of products. By means of simulation, decisions can be made on a substantial analysis to replace, or at least intuition. To address the resulting challenges, DREAM offers an extensible approach to simulation application engineering, leveraging state-of-the art research on simulation and simulation based optimization. Therefore, the focus of DREAM is to support the provision of simulation based predictive models across the spectra of decisions found in modern manufacturing industries, from strategic to real time. Essential challenges of this focus are:

1. Companies in development of product, processes and in management of manufacturing systems have established decision processes, there is a requirement to embed predictive decision supports that work seamlessly (even unknowingly) within these processes;

2. Demand volatility and shorter product life cycles are forcing product and process engineering to make decisions in shorter time frames creating a need for on-time decision support;

3. For companies to remain competitive in the global economy the scale of investments in product design and manufacturing is increasing, tools that can predict the effect of changes across the complete process chain at the product and process stages are required;

4. There is a high level of expertise and resources required to develop, deploy and maintain simulation based applications, new methodologies and tools for modelling and system knowledge management are required to support companies, especially SMEs, in adopting simulation based decision support;

5. Manufacturing systems are highly reactive systems, due to rapidly changing market demand and unscheduled events (machine breakdown, absentee workers, material quality), resulting in a great need by industry for simulation based real-time decision support that is tightly integrated with Manufacturing Execution Systems (MES).

Therefore, DREAM offers a simulation optimization platform and methodologies that address human-system interaction that will allow seamless integration of on-time analytic simulation based decision-support into existing company decision information processes at multi-levels. Simulation based decision support is provided through a semantic free, modular and open simulation application development environment, that allows tight coupling of simulation based decision support with the existing semantics used by companies in their existing decision processes. Through the provision of quantitative decision support across the process chain the quality and speed of decision making will increase.



Objectives of the project DREAM are:

1. To increase the competitiveness of European Manufacturing Companies through the provision of multi-level just-in-time simulation based application decision support.

2. To engineer a semantic free open simulation application development platform to promote simulation based applications by European Manufacturing Companies, IT consultants, Open Source community and Researchers.

3. To address the multi-faceted barriers to the adoption of advanced simulation decision support technologies by manufacturing companies, especially SMEs, by developing methodologies to address system knowledge management and human-system interaction challenges.

4. Using the semantic free simulation application platform to implement novel applications to support decisions at multi-levels in European Manufacturing Companies.

Building on the leading edge research, expertise and technologies of the DREAM-consortium, a solution to support the application of discrete-event simulation in decision-making in the management of shopfloor and supply chains of industrial companies was developed. The overall DREAM-solution consists of:

• A platform based on the Open Source ERP-system ERP5 to embed the components • ManPy (Manufacturing in Python): an expandable library of manufacturing objects written

exclusively in Python, making use of the SimPy library to facilitate discrete-event simulation • A knowledge extraction (KE) tool to link production data with simulation software • DREAM GUI: A browser based drag and drop graphical user interface written in JavaScript

using several libraries (JSplumb, JIO etc.) to simplify the creation of browser-based user interfaces

The project is industry-led and hence, clear measurable business objectives are targeted by DREAM. On an average base, the industrial partners estimate the following essential Business Measurable Objectives by implementing the foreseen final results of DREAM:

• Reduction in cycle time by 15% and reduced variability on cycle time by 10-20%; • Improved customer service levels by 10-20%; • Increase in first-time-right decision making by 50%; • Reduction of process development and implementation by 25%; • Improved efficiency in production with increased throughput by 10-20%; • Reduction in energy by 20%; • Quicker recovery from disruptions in production by 25%.

From detailed discussions with industrial companies in preparation of this project the following Industrial Objectives were captured to allow the above listed Business Measurable Objectives to be achieved IO1 Better Decision Making

Companies felt that major competitive advantage could be obtained if engineers involved in their product and production design and management processes could be supported with analytic decision support.

IO2 On-Time Real Time Decision Support Companies articulated the need for decision support tools that retrieves and intelligently analyses current and historical information combining this with predictive capabilities to provide their engineers with on-time, real-time decision support and system knowledge.



IO3 Reduction in Implementation Costs Industrial companies stated that current implementation and maintenance costs (expressed in terms of manpower hours, software costs and the high level of expertise required) for simulation decision support tools is too high for these tools to be adopted widely within their companies. They stated a need for a several magnitude reduction in current costs in order for simulation based decision support to be widely adopted across their decision processes by their companies.

To successfully address these industrial objectives, decision support systems must overcome known challenges to model based analysis (i.e. substantial time and skill level requirements) by offering a level of abstraction from model detail which will allow a range of non-expert users to seamlessly (even unknowingly) include model driven predictions amongst their decision factors. This goal will be achieved through the execution of the following technical objectives of the DREAM project: TO1 Simulation Methodologies

• Advance and apply system knowledge methods to reduce the resources required to implement simulation solutions in manufacturing.

• Devise methods for on-time data analysis to support and compliment simulation based analysis.

• Research human-system interaction in the application of simulation to support continued long-term use of simulation by manufacturing organisation.

TO2 Simulation Applications Platform • Develop an open source modular semantic free simulation applications platform. • Advance existing open source simulation optimization solutions to make them fit-for-

purpose for use by manufacturing companies.

TO3 Simulation Applications • Research, develop and implement simulation applications at multi levels in the product and

production design process. • Advance current simulation implement to allow real on-time simulation based decision

support. • Research and develop the application of simulation and machine learning to MES for the

development of a lumped simulation framework. A summary showing the relations of the business metrics, industrial objectives and technical objectives as well as the work packages is shown in the following figure, thereby illustrating the industry-driven approach of DREAM.





3 Scientific and technical results

The following chapters describe the scientific and technological results, which were achieved in the R&D work packages 2 to 6. To summarize, essential results, besides the approaches and methods, achieved by DREAM are

• a platform based on the Open Source ERP-system ERP5 to embed the components; • ManPy (Manufacturing in Python): an expandable library of manufacturing objects written

exclusively in Python, making use of the SimPy library to facilitate discrete-event simulation;

• a knowledge extraction (KE) tool to link production data with simulation software; and • DREAM GUI: A browser based drag and drop graphical user interface written in JavaScript

using several libraries (JSplumb, JIO etc.) to simplify the creation of browser-based user interfaces.

3.1 Work Package 2 – Requirements & Pilot Cases Specification In order to introduce the various targets and diverse needs of the industrial partners in a well-established way, the project followed an industrial user-centred, pilot case-based RTD approach: Use cases and expected results were detailed initially in the project with all stakeholders at the industrial partners together with their needs, and these scenarios and requirements were directly approved by the end-users. All RTD work was consequently guided by these use cases and the results are developed, implemented and validated iteratively to redirect any misleading and undesired RTD work already at early stages in order to ensure full end-user satisfaction of the results at the end of the project. The main objectives of this work package were the following:

• To capture and define the industrial requirements for the DREAM RTD activities on a pilot case individual and universal (pilot independent) level.

• To define and specify the pilot cases describing in-depth the envisaged decision situations in the industrial sectors to be targeted. The pilot cases were used as basis for industrial piloting, testing and evaluation of the DREAM results already in early stages.

3.1.1 Task 2.1 Requirements Engineering The requirements (including enablers, barriers etc.) for decision support in product and production engineering were gathered within the consortium taking into account the individual necessities and preferences of the industrial partners. Then, they were analysed and structured with focus on generality (to differentiate between individual and generic, common requirements). This ensured that the specifications for the subsequent developments (in WP 3, 4, 5) were targeted to the industry partners in the consortium as well as to suite the broadest possible range of companies. Figure 1 provides an overview of the basic approach used in WP 2 as designed by the task leads, UL and IFX. This was designed in advance of any individual industrial partner activity to prepare information and data for the project. The approach was then merged with efforts already undertaken by the partners in gathering relevant information and data at their organisation in anticipation of DREAM commencing. The approach consisted of 5 main steps. Insofar as was possible these steps were conducted in this sequence for each partner. This was constrained by availability to commit to certain tasks at given times by various partners and thus flexibility was required particularly in the timing of onsite workshops. Learning and methodology refinement also occurred over time in WP 2. The four industrial partners conducted individual preparatory work for the project by selecting pilot cases in an area of their business they felt could both benefit from and could support the DREAM concept. Therefore at the project kick-off meeting in Stuttgart, Germany on the 22nd and 23rd of



October 2012, each industrial partner introduced their pilot case. This aided subsequent WP2 information gathering by speeding up the initial stages where the consortium could consider these cases and provide suggestions for refinement where needed. In addition to the 5 main steps of formalised information capture, information was obtained from email exchanges, onsite informal meetings and phone/internet calls with the industrial partners. Information was also collected through discussions at the first DREAM consortium meeting post kick-off which was held at the IAO headquarters in Stuttgart (January 17th and 18th 2013).

Figure 1: WP2 Basic Workplan

3.1.2 Task 2.2 Pilot Cases Design In this task, the specific use cases of the industrial pilot partners have been specified and detailed. Main input to the task were the results from the questionnaire answered by the industrial partners to their specific problem they face in terms of decision taking in production or production network environment. Further details and understanding for the research partners have been gathered via interviews or presentations from the industrial partner representatives. This was often combined with the workshops, which took place at every industrial partner. By that the research partners could even get a deeper insight into the company structures and processes. Beside the visits several phone calls and virtual meetings took place in an iterative approach to ensure a comprehensive understanding of the use cases on research partner’s side. This resulted in a detailed description of the individual problem cases including company background, related processes and roles as well as the target state that shall be achieved via DREAM. Focus was in that sense on the products, the people and the processes related to industrial use cases. Based on this the necessary steps have been defined to achieve the requested state by the implementation of the DREAM solution for each case. Those steps were documented in a specification for every. The industrial pilot cases as described in Task 2.2 were the base for piloting and evaluation of the DREAM developments in WP5.



3.1.3 Summary for WP2 Two deliverables (D2.1, D2.2) were submitted in a report related to: capture and definition of the industrial requirements for the RTD activities on a pilot case individual and universal level, and definition and specification of company pilot cases describing in-depth the envisaged decision situations in the targeted industrial sectors. The RTD work undertaken in subsequent work packages based on the requirements was exploited through the pilot cases to test and evaluate the DREAM results in early stages. In this manner WP2, its tasks and deliverables supported the work undertaken in WP3 and WP4 for RTD development and in WP5 and WP6 which have been concerned with implementing and validating the documented pilot cases and demonstration of the DREAM results. 3.2 Work Package 3 – DREAM Methodologies The aim of the WP3 is to research and develop the DREAM methodology and corresponding tools for best practice implementation of simulation based application in the operations of manufacturing factories. These activities have as staring point the DREAM pilot cases and their specific requirements, by focusing on the specific needs of SMEs. The DREAM methodology consists of two components, the practical user or guidelines to be applied by factories for simulation based application and research results that will guide the technical requirements for the DREAM simulation application platform developed in WP4. 3.2.1 Task 3.1 System Knowledge & Decisions The planned activities to be performed in the framework of Task 3.1 aimed at answering to the main question:

“What pragmatic tools and methods for usage in SMEs and large companies are best to capture knowledge on systems and to store it for future reuse?” (DoW, DREAM, Page 9).

The initial description of the research and development activities planned for this task starts from the requirement that:

„Pragmatic instruments will be developed to support the management of system related knowledge under special consideration of the applicability of the instruments at SMEs to improve decision making. This will be done taking existing methods, tools and standards (real and quasi-ones like SysML and CMSD) into regard. The objective is NOT to develop a highly sophisticated tool that requires high efforts for usage and maintenance, but an instrument that is applicable in daily business with low overheads. The DREAM instruments are intended to provide a zoom function, i.e. to enable the users to start at a high level like the factory and then to “zoom down” to levels with more detail like the production line/cell or machine” (DoW, DREAM, Page 9).

The immediate identified collection of criteria to be satisfied by the DREAM system knowledge capturing tools, such called „DREAM criteria for system knowledge capturing“-consists of the following:

• Pragmatic (practical, realistic); • Applicable by SMEs; • Unsophisticated; • Applicable in daily business with low overheads and • Offer zoom function.



The main activities performed have been structured in the following five groups: 1. Study of best practice for the modelling of DREAM system knowledge; 2. Defining the DREAM selection criteria; 3. Evaluation and selection of the suitable DREAM best practice; 4. Technical recommendation for DREAM platform; 5. Development of the DREAM system knowledge modelling guideline.

The initial list of BP candidates, as specified in the DoW, respectively in the section state of the art and in the detailed presentation of the WP3, Task 3.1 was further-, enhanced by the discussions between the RTD partners in their virtual and consortia meetings, and consists of the following four BPs:

• SysML – Systems Modelling Language; • BPMN – Business Process Modelling Notation; • IDEF – Integration Definition Language; • CMSD – Core Manufacturing Simulation Data

These BPs have been shortly presented, according to the same structure: a) Overview, history, objectives, b) Modelling capabilities, diagram types, c) Modelling tools and d) Examples of employing a specific BP for the system modelling by DREAM industry partners, in this case, by Leotech and Balkan is provided, as well. The objectives of the DREAM selection criteria and their rating activities were the definition and identification of the list of selection criteria. These criteria, which have to be, suitable for DREAM purposes have been then rated using a weighting system. For achievement of these objectives, the following major technical activities have been performed:

• Set-up the first version of the DREAM criteria catalogue, by RTD partners; • Weighting the criteria, priorities, agreed with industry and developer partners; • Update the catalogue according to the results of the collection of DREAM requirements for

system modelling in the request for information documents (RFI), activities performed in the framework of WP2.

Based on the criteria and their respective weighting the best practice for the modelling of system knowledge that was selected is SysML with a total rating of 24. In the deliverable D3.1 was detailed the evaluation and selection of the suitable DREAM best practice software tool for the modelling of system knowledge using SysML. To demonstrate this result in detail and in practice the selected tool, TOPCASED, was employed on an example of system knowledge modelling for the Leotech GmbH and Balkan Ltd. Use cases. Technical aspects related to the integration of the selected DREAM practice into the DREAM framework have been given, mainly consisting of detailed information on the import/export of the models created in the SysML open source modelling tool TOPCASED. Finally, the technical recommendation for the DREAM platform and system knowledge modelling guideline was presented as well. 3.2.2 Task 3.2 Simulation Methodologies T3.2 is concerned with Simulation Methodologies to support efficient and effective simulation modelling within an industrial multi-decision level context. In order to enforce the end-user-centred innovation process of DREAM, the simulation methodologies developed followed research guidelines inspired by real industrial cases. Pilot problem statements described in WP2 were used to preliminarily search for applicable simulation and optimisation methodologies. Following prototype implementations, the methodologies were refined using feedback provided by both industrial partners and applications developers. This reviewing process is indicative of the active collaboration between researchers and practitioners involved in different tasks and work packages.



Based on DREAM Description of Work, T3.2 role in DREAM can be further categorised into four key activities, including definition of suitable model element structures, modelling strategies, metamodels and heuristics, and multi-objective optimisation. Aspects related to conceptual simulation modelling have been analysed and the adoption of a modelling framework inspired by Robinson has been suggested to guide developers and end-users in both the choice of suitable simulation elements and the definition of new elements’ specifications. Moreover, a classification matrix has been defined to frame the relationship between use case parameters and modelling choices. An extensive review of the academic and industrial literature concerned with the DREAM pilot problem statements has been carried out. The revision of solution approaches previously applied to problem statements similar to the pilots has provided guidance to the definition of modelling strategies implemented in DREAM. Specifically, modelling approaches reported in the literature in the field of supply chain planning, workforce allocation, job shop scheduling and resource-constrained project scheduling have been analysed to find efficient solutions to the demand disaggregation problem faced by IFX, the workforce allocation problem at BSCI and the optimal order scheduling problem faced by LEO and BAL. A systematic approach has been used by the RTD partners to compare different modelling strategies in order to select the most efficient one and provide support to verification and validation activities. Computational efficiency is obviously a fundamental aspect of real-time decision support systems, such as DREAM. The opportunity to reduce the response time of simulation applications has been investigated via the use of metamodels and heuristics for the different pilots. In particular, metamodels have been developed to address the IFX and BAL pilots. Heuristics are used to replicate the order allocation logic at IFX as the pilot problem could not be modelled using the discrete event simulation paradigm. Similarly, a variable intensity resource constraint project scheduling approach has been used to optimise capacity allocation for the BAL pilot as pilot abstraction at a project level was made necessary due to the lack of detailed production data. A list scheduler algorithm has been developed to optimised order scheduling for the LEO pilot. Various optimisation approaches have been implemented in the simulation application in order to generate good solutions to the pilot problem statements based on real time data. Along with the heuristics mentioned in the previous paragraph, evolutionary algorithms (Ant Colony Optimisation (ACO) and Genetic Algorithm (GA)) and mathematical programming formulations have been used to optimise various aspects of the pilot problem statements. An ACO approach was initially developed to optimise order scheduling for the LEO pilot; the same approach was then expanded to optimise objective weights in the BSCI workforce allocation problem. GA is used to optimise forecast demand sequence in the IFX pilot. The workforce allocation problem at BSCI and the forecast disaggregation problem at IFX have been formulated as mixed integer linear programming problems. A dual optimisation architecture that accommodates the requirements of multi-objective optimisation in the case of objectives weights difficult to establish from an end-user’s perspective has also been developed and implemented for the IFX and BSCI pilots. The cloud deployment of the platform has also been investigated in terms of the advantages it brings to computation times as it enables parallel computation. 3.2.3 Task 3.3 System Knowledge Extraction The main purpose of Task 3.3 is to identify the best methods to extract such knowledge from IT systems such as Enterprise Resource Planning (ERP) systems or Manufacturing Execution System (MES), knowledge that can be used for system characterization and be used to drive simulation based applications (DREAM – D3.3 2015). This task has two Research Objectives (RO), by covering these ROs the above stated task’s main purpose will be achieved. These Ros are described as follow (DREAM – DOW 2013):

• RO1: To develop methods to support on-time and real-time simulation applications and system analysis;



• RO2: To develop approaches using Machine Learning Methods (MLM) feeding from MES to extract “dynamic characteristics” from a system to support a lumped simulation framework and system causality analysis.

Towards the fulfilment of RO1, we split it in four steps. These steps, which include research and development, are the following:

• Input data phase in DES applications; • Data extraction methodologies and tools; • Development of applications to support on-time and real-time simulation; • Design of an approach to support the use of DES in SMEs.

Towards the fulfilment of RO2, a research approach is developed that makes use of a MLM, called Genetic Programming (GP). This approach and consecutively GP is applied on the flow time data gathered from manufacturing system to derive predictive functions for key metrics, e.g. cycle time, throughput, etc. In order to cover the first step in RO1, a literature review was conducted on the input data process in DES, focusing especially on the automation of this process. The outcome of this review is a submitted paper, which is accepted for publication with revisions (Barlas and Heavey 2015). For the second step, data extraction methodologies and tools, we performed a survey of OS data science tools. The main purpose of this survey was to find out the most appropriate tool that would be the base for the development of the Knowledge Extraction (KE) tool (Barlas et al. 2015). The development of a data interface for exchanging DES data, which supports on-time and real-time simulation by offering means of interoperability between simulation applications and other manufacturing applications, is achieved for the third step. Finally, as it is stated in DOW (Description of Work) (DREAM 2013), the big challenge for this RO is to develop tools and approaches that can be used across different company sizes, with special attention placed on the requirements of SMEs, therefore we designed an approach to support and facilitate the use of DES in SMEs. Regarding the RO2 a published paper acknowledged by DREAM project, named “Generating Operating Curves in Complex Systems Using Machine Learning” in the Proceedings of the 2014 Winter Simulation Conference, written by Can, Heavey and Kabak (2014). This paper proposes using data analytic tools to generate operating curves in complex systems. Operating curves are productivity tools that benchmark factory performance based on key metrics, cycle time and throughput. We apply a machine learning approach on the flow time data gathered from a manufacturing system to derive predictive functions for these metrics. To perform this, we investigate incorporation of detailed shop-floor data typically available from manufacturing execution systems. These functions are in explicit mathematical form and have the ability to predict the operating points and operating curves. Simulation of a real system from semiconductor manufacturing is used to demonstrate the proposed approach. 3.2.4 Task 3.4 Human-System Interaction The main goals of T3.4 activities were:

1. To approach the basics of Human-System Interaction (HIS), the user requirements and the state of the art of software-Assistance of the simulation software.

2. To develop a DREAM concept of pro-active unobtrusive support of users by the system. 3. To implement and to evaluate the DREAM concept on prototypical version of DREAM

software. Based on these goals, the multi-stage evaluation and implementation basic workplan for T3.4 was developed.



Figure 2: Basic workplan of Task 3.4

The impact on other tasks:

1. The DREAM-concept was detailed and transferred to WP4 for software-based implementation.

2. The results of HIS approach were public demonstrated on the MuC2015 conference (“Mensch und Computer 2015”, english “Human and Computer 2015”) from 6th to 9th of September in Stuttgart. These activities are related to WP6 “Platform Demonstration”.

3. The DREAM Platform with HIS focus was presented by research departments of Renault Centre/France and Mercedes Technical Center/Germany.

The efforts of DREAM concept evaluation were published by ITProduction-Wissen Kompakt 2015. These activities are related to WP7 “Dissemination & Exploitation, IPR”. 3.2.5 Summary for WP3 The main objectives of the WP3 and the scientific and pragmatic questions who received in the framework of this WP an answer are as follows:

„O3.1 System Knowledge Modeling: What pragmatic tools and methods for usage in SMEs and large companies are best to capture knowledge on systems and to store it for future reuse? O3.2 Simulation Methodologies: What are the most appropriate methodologies to use for the level of decision with regard to a company's decision hierarchy, company size and sector? What simulation methodologies are required to model interlinked multilevel decisions? O3.3 System Knowledge Extraction: What are the best methods for extracting such knowledge from IT systems like ERP or MES? Which knowledge can be used for system characterization and to drive simulation based applications? O3.4 Human-System Interaction: How to appropriately cater for the human from the human-system perspective within the context of a simulation application development and use?“ (DoW page 9).

The DREAM methodology was developed based on the requirements specification by all industry partners, and the detailed description of all pilot cases performed in WP2. It guided and monitored the development of the platform in WP4. The development process of the DREAM platform is continuously feed-backed by the implementation of the pilot cases and the coressponding



validation realised in in WP5. The WP3 performed activities and developed tools have been demonstrated in the framework of WP6. 3.3 Work Package 4 – DREAM Platform The goal of Work Package 4 has been stated as researching and developing a simulation application platform enabling the ease-of-use application of scenario-based discrete-event simulation in day-to-day business at small manufacturing companies and large industrial enterprises at reasonable costs. According to the Description of Work, the objectives of WP4 are the following:

● To develop an high performance discrete-event simulation framework enabling scenario-based analysis at reasonable costs (based on SimPy and PyPy)

● To devise an application that enables turning of data into knowledge by advanced analysis (based on Rapidminer)

● To develop tools to assist in production planning by ease-of-use multi-level scenario-based discrete-event simulation

To do so, the work has been split into four tasks, T4.1 being the system architecture and integration, where we have designed the overall system architecture of the simulation platform that has been a foundation for the different modules. In this task we have been working on designing the system architecture, on defining the protocols used by different components to communicate together and on some methods to make the installation of the tools easy and repeatable. T4.2 is where the high performance simulation framework was developed. Firstly, some generic simulation components have been created, then these generic components have been extended to develop ad-hoc simulation models suited for the use case of each of the four industrial partners’ use cases. T4.3 is the knowledge extraction tool, which goal is to turn data into knowledge and feed the simulation models with data from enterprise data sources. This has been achieved in four activities, the first step was to design the tool architecture, then to review R and RapidMiner functions and packages that can be used in the tool, the main effort was the continuous development of the tool and lastly all this was proven correct by the verification and validation of the tool through use cases. T4.4 is where an user interface enabling different level of users to define a model visually using an intuitive graphical interface, to run the simulation and then to visualise the results graphically. The first activity has been to prepare a state of the art of the existing libraries and make a demonstration of a possible user interface using jsPlumb library that has been selected as part of this activity. Next, we have defined how the communication between the user interface and the simulation engine would be made, for that we choosen simple HTTP protocol and JSON for the data format. We enabled tools to draw and save a flow by leveraging jsPlumb library and extending until it became the full featured graph editor enabling to edit any kind of directed graph regardless of the semantic attached to the graph. Once we had generic components, we put them all together to for real use cases of the industrial pilot cases. Allowing visualisation of the data was also an important part of this task, we had integrated various visualisation widgets such as graphs, tables and GANTT diagrams to help validate the use cases. 3.3.1 Task 4.1 System Architecture and Integration The main aspect of the system architecture is to make sure the three components used in the platform are integrated seamlessly and yet each of them is independent enough to be easily reusable in another context. The second challenge of this task is that the computer tools can be installed easily. The three components are the simulation engine, the knowledge extraction tool and the graphical user interface. The simulation platform is developed using a modular approach and therefore is flexible in what it can simulate. The pilot cases were used to demonstrate that the platform can be



used to simulate various production models, from production line to job shop. In order to integrate the three components together, a plugin architecture have been setup. The platform itself utilize first some pre-processing plugins, which can gather data from various sources and prepare the data for simulation evaluation. The execution step, is where the simulation engine takes place. Last is the the post-processing steps, where some plugins can prepare the data for visualisation. Both pre and post processing steps make use of objects that are part of the knowledge extraction tool. Cloud-based simulation is one of the new grand challenges in modelling and simulation, whereas Discrete Event Simulation have advanced over the last decades and now is moving into a mature state in which cloud deployment and other advancements are considered integral parts. It was therefore natural that the DREAM platform includes the ability to execute DES scenarios on the cloud, by distributing the calculation on a cluster made of several simulation executors. The ant colony optimisation has been adjusted to run evaluations of scenarios in parallel on a cluster. Distributing the scenario allowed the optimisation step to evaluate scenarios on a much wider range of scenarios and obtain the results much faster. To enable the communication between the components, we have defined an exchange format to represent simulation data. Because of its simplicity and recent wide adoption in software, we choose JSON as the data format. Since many of the simulation components can be represented in the form of a directed graph, the first step is to be able to define the edges and nodes of a graph. In order to keep this format semantic-free, we did not want to use directly “Machines” or “Operator” directly in the graph format, so we chose to embed the meta schema as part of this data format. Along with the graphs, some other information is required to run a model, for example the WIP levels or the shifts of operators and stations. This data is also included in the format definition. The installation of tools is based on SlapOS, an open source multi-cloud platform project. By using SlapOS, a DREAM simulation model can be distributed across an assorted set of servers, virtual machines and even mobile devices. Installation of the DREAM platform and all the dependant software can therefore be achieved without human intervention, just by running one simple command. This installation can independently be performed in the cloud or in a computer running inside a company’s existing IT infrastructure, to ease integration with in house installed software such as Manufacturing Execution System or Enterprise Resource Planning software. One novelty in this platform is that not only the program where the industrial users can create models and run simulation are installed automatically on a personal computer or in the cloud, but also that this platform allows the developers to make changes to the software, run the modified version of the software and publish the modified version of the software to the Git content management repository. 3.3.2 One Task 4.2 High Performance Simulation Framework The simulation framework developed for DREAM is called ManPy (Manufacturing in Python). ManPy’s ultimate objective is to provide industrial practitioners with easy-to-use, expandable and efficient simulation based decision support tools for industrial problem statements at different decision levels. ManPy has been conceptualised as a layer of simulation objects developed using SimPy’s standard classes and is exclusively written in Python; it adopts and expands SimPy’s efficient use of Python generators via the SimPy.Process class. The choice of SimPy as ManPy’s foundation library is suggested in the DREAM DOW and has been further justified by conducting a review of 23 Open Source (OS) Discrete Event Simulation (DES). ManPy was originally built based on SimPy2.3 (http://simpy.sourceforge.net/old/) and consecutively ported to Simpy3 (http://simpy.readthedocs.org/en/latest/) following the release of SimPy3 first stable version; this was done to ensure that ManPy be a state-of-the-art OS simulation library. The porting of ManPy to SimPy3 has been completed with minimal impact on ManPy’s architecture which is based on a linear and efficient hierarchical class structure. ManPy generic classes inherit objects and methods from SimPy and Python. In the following hierarchical level, DES objects inherit from the generic classes. Finally, customised objects inherit their methods from either existing core objects or other customised objects. ManPy objects repository is expandable and customisable; therefore, users may create either completely new or customised objects and incorporate them into the platform

http://simpy.sourceforge.net/old/

http://simpy.readthedocs.org/en/latest/



and also obtain a repository of objects that might be generic, focused on a specific domain or tailored for specific problem statements’ requirements. Like other DES OS software, ManPy has the advantage of being a sustainable alternative to COTS DES packages. Being available at zero license costs, ManPy should prove attractive to companies with limited financial resources and also to companies investigating the opportunity of deploying DES-based decision support before any COTS DES investment decision is made. Unlike other OS packages, ManPy further improves the advantages of OS DES by making simulation modelling more accessible to industrial practitioners not familiar with simulation programming. COTS software packages’ strength is generally related to the provision of tools that facilitate modelling, debugging and experimentation. On the contrary, OS simulation packages currently available on the web, such as SimPy, require considerable programming efforts; hence, their target users are limited to simulation experts with programming skills. ManPy offers something new and accommodate the needs of different levels of users by reducing or even eliminating the requirement of programming skills. Highly customizable, ready to use OS simulation objects, similar to those available in COTS packages, have been developed and can be connected to create a simulation model through a graphical user interface, following the “drag and drop” logic implemented in many COTS simulation packages. ManPy has been developed using an end-user oriented approach as its development guidelines have been set based on the analysis of actual industrial use cases (e.g. DREAM pilots). These industrial cases have been used to identify new objects and relevant features to be incorporated in ManPy in order to make it a highly flexible simulation tool and enhance its simulation capabilities. As a result, the objects currently available in ManPy are effective in modelling generic industrial problem statements. In particular, the pilot systems are validly modelled and the pilot problem statements are effectively addressed by means of simulation-based optimisation so that the versatility of ManPy as a library that can be easily embedded in various optimisation frameworks is demonstrated. As any OS project, ManPy’s development cannot be considered completed and we cannot claim that ManPy is entirely bug-free. Nevertheless, it is in a sufficiently mature state to attract the interest of simulation modellers and software developers. For this reason, ManPy has been released in GitHub (https://github.com/nexedi/dream) so that potential users’ feedback could be used to enhance its functionality. Following good practice in OS software development, a comprehensive ManPy documentation is also available online. This includes a detailed introduction to ManPy’s architecture and currently available simulation objects; it also offers the readers suggestions on how to customise the objects’ behaviour by overriding methods and create new objects based on contingent simulation requirements. 3.3.3 Task 4.3 Knowledge Extraction Tool The main objective of this Task was the development of a tool enabling production planners to examine and analyze large sets of data with the use of Open Source software in order to create variability analysis models from historical data and turn the output of this analysis into knowledge that can be used within the DREAM Simulation Engine. The outcome of this Task reported and submitted in two deliverables, which describe the first and second implementation cycle on the development of the Knowledge Extraction tool. The first two steps towards the development of the KE tool are performed in conjunction with Task 3.3, whose main goal as all tasks of WP3 was to provide the methodologies and approaches to WP4 Tasks in order to proceed with the development of the tools. We conducted a literature review on the automation of input data to DES. This work helped us understand the nature of the input data phase in Discrete Event Simulation projects. By examining the proposed methodologies and tools, we established that automation of input data is required to support real-time simulation. Next, a survey of OS data science tools is presented. The main purpose of this survey was to identify the most appropriate tool that would be the basis for the development of the KE tool. The outcome of this survey led to the selection of Python and the RPy2 ( http://rpy.sourceforge.net ) library as the base for the KE tool.

http://rpy.sourceforge.net/



The third step was to proceed with the actual development of the tool. The open source KE tool consists of features, written in the Python programming language, that enable the three main components: Data extraction; Data processing; Output preparation. The tool reads data from several organisations’ resources; analyses it and outputs it in a format that is applicable to be used by a simulation tool, all conducted in one automated process. The KE tool aims to offer an open source automated solution that reduces the time-consumption in the input data process in DES projects. The deployment of the KE tool in a large scale organisation with availability in data resources was the next step. This helped us validate the tool for usability in real world situations. Next, in our efforts to prove the versatility of the tool and after identifying that the role of DES within Small and Medium Enterprises (SMEs) has in general, received little attention by academics and simulation practitioners, we developed an approach that facilitates and supports the use of real-time DES in SMEs. The SME approach that utilises the KE tool for the supply of real-time data to the simulation model was tested in two pilot cases. More information about the different developed objects of the KE tool is available in GitHub at the following URL ( https://github.com/nexedi/dream/ ). In the repository one can find and download the developed objects themselves, examples with the development of the KE tool in different simulation topologies and a thorough documentation of the tool. The code is kept under version control with Git ( http://git-scm.com/ ), the user can clone and manipulate the different versions of the code through the project repository in GitHub. 3.3.4 Task 4.4 User Interface Tool The graphical user interface has been developed with two ideas in mind: firstly it should help the user design a line visually and it should also provide the user with easy understandable results of simulation experiments that should help him understanding at a glance the output of the model by showing the results in formats such as bar charts, plots, Gantt diagrams or spreadsheets. Since the beginning of the project, three different levels of user interaction have been identified:

● The super user, who will create custom ManPy objects to handle the specification of the case that is being modelled and also configure the specific inputs and outputs.

● The industrial engineer, who will configure the line using objects created by the super user or by using stock ManPy objects. The industrial engineer may also configure some general simulation parameters, such as the length of the simulation.

● The end user, who will have a tailored tool to support him in the decision process. Basically he will either click on a button and get the suggestions from the optimizer, or perform “What if” experiments by changing inputs and seeing the changes in the outputs.

A survey of existing open source charting libraries has been conducted in order to prepare a state of the art of the existing libraries. During these activities, several libraries have been evaluated, using various metrics. The first point was that the library has to be available in a licence which is compatible with LGPL which is required for DREAM. Secondly it must be actively developed and the later point is that it should follow best practice for development, for example using code review and test driven development. After selecting jsPlumb library, we could easily build a first prototype of extending this library to display a simple graph representing a production line. Since the graphical user interface has the form of a web application to be easily accessible without having to install any software on the end user computer, we have chosen HTTP as the protocol for the communication between the user interface and the simulation engine, since this is the most common protocol used for programs to communicate through the network. The data format in this communication follows the definition from T4.1 and is based on JSON, as it this format very widely adopted nowadays. There was a need to have tools to draw and save a graph in this user application, to enable the industrial engineer to design graphically the production line used for the simulation model. This lead to the creation of the so called graph editor that is now a reusable graphical user interface

https://github.com/nexedi/dream/

http://git-scm.com/



component that can easily be integrated to any web application. Deliverables of this project and WP6 presentation “how to develop with the dream platform” describes in great detail how this component can be configured and integrated to view and edit any kind of direct graph, this component is not limited to manufacturing or simulation edition, as the semantic of the objects is part of the configuration of this component. Once we had generic components, we put them all together to for real use cases of the industrial pilot cases and to meet the demanding expectations of the industrial partners. We had to build four complete applications, one for each of the four industrial partners. This could have been achieved by providing an user interface that could be tailored for the need of each industrial partner Allowing visualisation of the data was also an important part of this task, we had integrated various visualisation widgets such as graphs, tables and GANTT diagrams to help validating the use cases. 3.3.5 Summary for WP4 We can claim that the goals of WP4 have been met, given that the high performance discrete-event simulation framework enabling scenario-based analysis at reasonable costs has been released as ManPy and that this framework has been proven generic enough to cover cases from job shop oriented productions where the library is used to do optimisation with simulation where multiple scenarios are evaluated and the software helps the projects manager in determining what is the best allocation of tasks to workers and machines in order to minimize the delay between the actual production dates and the due dates of the orders. The same discrete event simulation library could be integrated in production lines scenarios where we could assist in the decision process in case of employees’ absenteeism or machine breakdown. The knowledge extraction tool has been successfully used to turn lager amount of data into knowledge of by advanced analysis and this has also enabled to automate the process of feeding the simulations models with various sources of inputs. The graphical user interface was an example of how the various tools have been put together to assist in production planning by ease-of-use multi-level scenario-based discrete-event simulation. The simulation application platform has been developed based on the requirements specification and pilot case descriptions from WP2 and on the methodology from WP3. The work has been tuned with the further-development of these methods in WP3. Feedback from pilot cases implementation and validation in WP5 has helped us to work in the right direction. The platform and methodologies have been used as foundations for demonstrations in WP6. From the outcomes of WP4, it has been demonstrated in the exploitation plan that both the simulation engine and the graph editor part of the user interface have a great future outside of the DREAM project. Some examples of integration of both of these two components with the Enterprise Resource Planning software ERP5 have been realized. Some prototypes of integrating ManPy Discrete Event Simulation library in ERP5 optimisations have been realised and the graph editor has been fully integrated to allow users to graphically edit business processes in ERP5 and also to visualize the history of decision workflows. These integrations shows at the same time that these tools are generic enough to be reused in another context and shows great opportunities of being more and more exploited in the future. 3.4 Work Package 5 – Pilot Cases Implementation & Validation WP5 is concerned with “Pilot Cases Implementation and Validation” and has “the overall goal to facilitate the end user-centred innovation process of DREAM”. This suggests that the DREAM platform development be based on pragmatic industrial pilot cases. “The objectives of this work package are therefore:

• to plan the implementation of the pilots for the industrial use cases, • to realize the pilots in a prototypical manner and



• to validate the pilots in industrial settings by the end-users.” WP5 includes two major tasks:

• T5.1 - industrial pilots preparation; • T5.2 – Industrial pilots validation.

Task 5.1 focuses on the development of individual plans for the industrial use cases so that relevant elements of the platform are identified and adapted to the specific cases. The implementation of the industrial pilot is also a fundamental part of this task as it enables testing the viability of the DREAM platform under real-life conditions. Task 5.2 consists of the validation of the platform using various evaluation procedures. Cross functional implementation workshops organised at the industrial partners facilities, standard software testing procedures, technical assessments based on the key metrics identified for each use case, questionnaires and interviews are mentioned in the DOW as preferred means to verify the functionality, acceptability and usability of the platform and ensure that specifications detailed by both end-users and developers are met, end-users are satisfied and significant benefits in terms of system performance improvements are obtained. 3.4.1 Task 5.1 Industrial Pilots Preparation The industrial pilots preparation and implementation have been articulated in two cycles characterised by progressive complexity level in terms of objectives and problem statements. The final use cases aimed at addressing full pilots’ requirements as expressed by industrial partners in WP2 and represent a natural evolution of the first version of the pilots. Whereas the first use cases consisted of simplified conceptualisations of pilot problem statements so that the development process of the DREAM platform could be facilitated, the final use cases incorporate greater details on the systems to be modelled so as to guarantee complete achievement of the pilots’ objectives. For instance, for the LEO and BAL pilots which had been jointly treated in the first cycle, two distinct conceptual models have been developed in the final cycle in order to fulfil specific pilot objectives. Furthermore, in the final cycle, the use cases have been refined using feedback elicited from validation exercises conducted as part of T5.2 activities. The definition process of the use cases enforces the end-user centred nature of DREAM as industrial partners have provided the definition of objectives and scope of the final pilots (WP2) and have also provided valuable feedback while testing the first implemented prototypes so that research directions have been opportunely set. Four use cases, one for each industrial partner, have been considered in DREAM. For each case, a deep insight into the inherent problem statement and the system to be modelled has been gained; following this, technical specifications of components of the DREAM platform and final pilot implementation plans have been derived. The DREAM platform components consist of a simulation application, a knowledge extraction tool and a graphical user interface whose capabilities have been inspired and refined by the pilot cases’ requirements but are not strictly limited to them; indeed, the use cases have been conceptualised in such a way that generic platform requirements applicable to various manufacturing scenarios have been considered. The BAL use case concerns the development of a simulation-based decision support system to facilitate management and engineers in order management decision processes and help make significant improvements in their productivity and service level. In particular, decision support is offered to estimate the earliest possible delivery date for a new customer order that won’t affect the fulfilment of existing orders. The platform also offers support in identifying the best possible way to allocate capacity to different orders, especially in response to productivity disruption events. Due to the complexity of the production system and the lack of detailed information on the inventory level, an aggregate capacity allocation approach based on a discrete time simulation paradigm has been suggested to address this problem; this approach makes use of an event generator that is able to handle the application of the capacity allocation logic at regular time intervals. In the BSCI use case, the operational problem statement identified in WP2 is addressed in the final pilot. BSCI have requested decision support at an operational level to assist line supervisors in



making good workforce allocation decisions based on both available resources and work in progress in the production lines. The simulation application capabilities inherent in the final pilot expand on those already offered for the tactical decision support (first use case) as the possibility of modelling operators and implementing workforce allocation heuristics/optimisation approaches have been accommodated. Likewise, graphical elements associated with the introduction of human resources in the simulation models have also been specified in the final pilot. As a result, multi-level decision support is provided through DREAM. The IFX use case focuses on demand disaggregation in a supply chain planning context. In the final pilot, attention has been paid to model the capacity allocation process with great accuracy as validation experiments and data provided by IFX had shown that the level of abstraction used in the first pilot did not allow for an accurate representation of the demand planning mechanisms adopted in the supply chain. As a result, the disaggregation approach used for identifying optimal production routes for orders and forecast has been differentiated. Furthermore, GUI output widgets have been designed to visualise relevant output information. Finally, the LEO case expands on the previous version by refining details of various system elements. The objective of the LEO use case relates to order management processes and optimal production scheduling and resource allocation. Details introduced in the final use case include the distinction of operations requiring manual set-up, the definition of workforce skillsets, modelling of raw material requirements. Detailed feedback on potential improvements of GUI features had also been provided by LEO to guide the development of the GUI final version. Timelines for the use case definition, implementation and validation were developed for each pilot so as to guide the DREAM development process. 3.4.2 Task 5.2 Industrial Pilots Validation DREAM is an open source simulation based decision support platform and, as such, its validation process has been based on validation frameworks suitable for decision support systems (DSS). DSS validation goes beyond the mere task of verifying that a realistic representation of the system to be modelled has been achieved, which is the ultimate goal in a simulation modelling project. DSS validation also involves the verification that the underlying relationships that govern the decision process are correctly modelled and that the representation of the system in question is appropriate for the analysis purpose. A DSS validation framework developed by Borenstein has been used to validate the DREAM platform throughout its development. This framework addresses validation requirements that are expressed in the Description of Work (DOW) of DREAM , in relation to software testing procedures, end-user satisfaction and business metrics performance. Four fundamental validation steps are included in the reference framework:

• Face validation – consistency between industrial partners’ view on pilot objectives and Research and Technology Development (RTD) partners’ view is verified in this step;

• Subsystem Verification & Validation – each component of the DSS is verified and validated using applicable testing procedures;

• Predictive Validation – the DSS developed is validated using empirical data or reference case studies available in the academic and industrial literature;

• User Assessment – following DSS demonstrations, the end-users evaluate functionality, usability and applicability of the DSS in relation to their pilot case and, more generally, the system where decision support is required.

Each of these validation steps have been adapted and implemented in the DREAM platform in order to fulfil both DREAM validation requirements and specific needs related to the industry pilots. DREAM software development has enormously benefited from its parallel validation process. The involvement of industrial partners in DREAM validation has refined research directions and has led to the definition of ambitious objectives that have been progressively achieved. In particular, face validation has been conducted to ensure that the RTD partners correctly interpreted the pilot problems and fundamental requirements could be set for the platform based on the industrial



partners’ needs. The industrial partners’ involvement in the final pilot definition process has been ensured through extensive e-mail correspondence, virtual meetings and occasional on-site workshops. Suitable software testing procedures applicable to the platform components and identified during the first cycle have been implemented to ensure ongoing verification and validation of relevant modules and simulation objects throughout the software development. Prescriptive validation results demonstrate that a satisfactory level of modelling accuracy has been obtained for all pilot applications and the problem statements solved include complex elements of the original pilots which suggests that effective decision support can be offered through DREAM to the pilot companies. With respect to the first pilot validation cycle, the greater level of detail included in the models developed during the final cycle has allowed for a more classical validation approach whereby real or realistic data have been used to verify the models accuracy and assess the impact that DREAM might have in real production environments. Emphasis has been given to the evaluation of the impact of DREAM on the pilot applications in terms of key performance indices identified by the industrial partners. The results obtained show that significant business performance improvements can be obtained for productivity (in the range of 10-15%) and service related metrics if DREAM is used as an integrated DSS. An evaluation questionnaire that investigates functionality, acceptability and usability aspects of the platform has been answered by the industrial partners. Their feedback suggests that, in general, the pilot problem statements have been correctly captured and appropriate solutions have been developed. As a result, DREAM is considered an effective decision support platform that could be integrated in the company information systems and be effectively used to facilitate management and front-end practitioners in their decisional tasks. Finally, following directions suggested by the Innovation Radar, the commercial potential of DREAM and its market readiness have also been assessed through a system usability scale questionnaire. This was done to sustain DREAM exploitation plans developed in WP7 with industrial practitioners’ evaluation at consortium level. 3.4.3 Summary for WP5 The methodology adopted to prepare and implement the DREAM pilots reveals the end user-centred nature of the DREAM process and is based on an industrial users feedback mechanism that ensures that the DREAM platform development has been guided by the requirements and needs expressed by the industrial partners. To this regard, validation activities running in parallel with the implementation process have aimed at guaranteeing that the industrial requirements were correctly interpreted and the functionalities made available in the platform satisfied the end-users’ needs; the validation feedback has also provided research directions to enhance and expand the proposed solutions. Final use cases have been defined to respond to the general objective of DREAM to create a generic simulation application platform that can be applied to various simulation scenarios; as part of its ultimate objective, the DREAM platform has been developed to address industrial pilots’ requirements as well. The final DREAM pilots are based on more detailed conceptual models so that the requirements initially expressed by the industrial partners are fully addressed. The platform validation has been conducted using a reference DSS validation framework developed by Borenstein. Face validation, sub-system verification and validation, prescriptive validation and end-user evaluation have been successfully completed using the four pilot implementations. Following good practice in software development, the platform components are kept under unit testing which is fundamental to guarantee the possibility of safely expanding the platform functionalities. Pilot evaluations of the DERAM impact on productivity measures demonstrate the benefits that simulation-based decision support can bring to European manufacturing companies. Encouraging feedback has been obtained on the platform functionality, applicability and usability and DREAM can be considered an OS DSS attractive to manufacturing companies, academic institutions and the wider OS community.



3.5 Work Package 6 – Platform Demonstration The overall goal of WP6 is to demonstrate the pilot industrial cases applications and prove the results developed by the RTD work packages of DREAM. The objectives of this work package are:

• to show the prototype of the software platform and representative applications on it • to see how the DREAM solution can support decision making and thus benefit the pilot

case companies • to present results at relevant conferences and appropriate trade fairs • to present selected results and demonstration parts on the Internet for people outside to

access the project

It included two major tasks: the preparation of demonstration and the pilots demonstration. The preparation phase aimed to set up demonstration scenarios with highlighted features of DREAM platform, and the content and details of decision-making supporting steps for each pilot case are defined here. During the project runtime, the proposed scenarios have been continuously reviewed and updated to better fit to the latest requirements. For the preparation of pilot case demonstration, the platform is also tested at the intermediate stages and final stage. 3.5.1 Task 6.1 Preparation of Demonstration The main goals of Task 6.1 focused on:

• Set-up of demonstration scenarios and • Preparation of pilot cases for demonstration.

Set-up of demonstration scenarios Therefore the requirements for internal and public demonstration were analysed and classified. The internal demonstration was elaborated with main goal to demonstrate specific for each company simulation requirements and procedure inside the company and consortium:

1. Semiconductor supply chain, Infineon; 2. Manufacturing line design issues, Boston Scientific; 3. Management of constrained resources, Leotech; 4. Redesign of order management system, Balkan.

The scenarios for public demonstration were elaborated with main goal to explain all DREAM highlights easy but with all detailed information about technical and practical realization. The modular character of demonstration is addressed for all public groups.

Figure 3 Main modules of DREAM public demonstration



As outlook for the future, this modular demonstration can be developed to an application with linked source to each module. It will be tested and prototyped in the Centre for Virtual Engineering (ZVE) at the University of Stuttgart and by Fraunhofer IAO. Preparation of pilot cases for demonstration The preparation of pilot cases was based on DREAM internal discussion, workshops, regular’s phone conference and scientific studies. In the Digital Engineering Lab (part of ZVE) at Fraunhofer IAO/IAT of University Stuttgart were tested also the new demonstration technologies, as Augmented Reality, combination of 2D/3D Visualization and multi-touch table. 3.5.2 Task 6.2 Pilots Demonstration Task 6.2 mainly focused on two parts: the internal demonstrations and the public demonstrations.

1. Part 1: Internal demonstration of pilots cases

The pilot cases have been demonstrated based on the specifications and requirement in Task 6.1. The Internal demonstrations have been held within the consortium as well as within the four pilot case companies. For each of these cases the following parts have been prepared:

• The content of the demonstrations • The storyboard including a detailed description of each single step • The equipment and logistics requirements

The internal demonstrators play an important role to show the most significant results of DREAM to the management and experts in the partner companies. This communication is an important step in initiating the future usage of the DREAM results within each company.

2. Part 2: Public demonstration of pilots case

Beside the internal demonstration also a public demonstration case was developed. For the public demonstrator the contents mentioned above for the internal demonstration have also been developed. Additionally special supporting materials with a broader scope and usage of different media and level of interactivity have been prepared. These include:

• Dream Introduction Movie • Dream stop-Live-Scenario (computer based interactive tool) • Dream overall architecture documentation • Dream main features documentation • Interview movies of consortium members about DREAM impact • Dream board game

The whole bunch of supporting materials allows demonstrations tailored to the knowledge level and size of the audience. Public demonstrations have been organized in three European centres in Germany, France and Ireland. In particular these have taken place in:

• Venue 1: ZVE centre, Fraunhofer IAO, Stuttgart, Germany o Event: Mensch und Computer 2015 o Date: September 6-9, 2015 o Participants: 80 representatives of research and industrial organizations

• Venue 2: Enterprise Research Centre, UL, Limerick, Ireland o Date: September 21, 2015 o Participants: Members of local and national enterprise boards

• Venue 3: Nexedi SA, Lille, France



o Event: Nexedi Academic Exchange o Date: September 8, 2015 o Participants: IT-faculty/student members from universities around the world

3.5.3 Summary for WP6 For the public demonstration, one of the industrial pilot cases has been adapted and was developed as a generic pilot case. This generic case has been demonstrated at 3 European centers in Germany, France and Ireland. For both internal and public demonstrations, the first and final descriptions of scenarios have been provided via D6.11 and D6.12. And their corresponding supporting materials have been developed accordingly and provided in D6.21 respectively D6.22.



4 Potential Impact, Dissemination and Exploitation

4.1 Potential Impact The process of developing products, manufacturing processes and the management of manufacturing systems can be characterised by the wide range of decisions at multi-levels across the process chain that need to be taken by engineers in very short time frames. These decisions very often have large implications for a company’s profitability. Currently most companies have well established information support processes to support these decisions, but lack analytic decision support capabilities that can provide engineers with persistent on-time support for business decisions. Therefore, the focus of DREAM is to support the provision of simulation based predictive models across the spectra of decisions found in modern manufacturing industries, from strategic to real time. Therefore, DREAM offers a simulation optimization platform and methodologies that address human-system interaction that will allow seamless integration of on-time analytic simulation based decision-support into existing company decision information processes at multi-levels. Simulation based decision support is provided through a semantic free, modular and open simulation application development environment, that allows tight coupling of simulation based decision support with the existing semantics used by companies in their existing decision processes. Through the provision of quantitative decision support across the process chain the quality and speed of decision making will increase. The project is industry-led and hence, clear measurable business objectives are targeted by DREAM. On an average base, the industrial partners estimate the following essential Business Measurable Objectives by implementing the foreseen final results of DREAM:

• Reduction in cycle time by 15% and reduced variability on cycle time by 10-20%; • Improved customer service levels by 10-20%; • Increase in first-time-right decision making by 50%; • Reduction of process development and implementation by 25%; • Improved efficiency in production with increased throughput by 10-20%; • Reduction in energy by 20%; • Quicker recovery from disruptions in production by 25%.

The potential impact of DREAM on productivity performance of European Manufacturing Companies has been investigated by means of pilot applications. From this perspective WP5 offers fundamental results to substantiate the statement that real competitive advantage can be gained by companies that adopt integrated open-source (discrete event) simulation-based platforms as a means to support real-time decision processes across all decision levels. In particular, the validation results confirm that simulation-based decision support can still be beneficial even in small and medium enterprise contexts characterised by minimal data digitisation. Based on the objectives listed in the DOW and specific pilot requirements extrapolated from WP2, KPIs have been identified for each pilot. The pilot applications have offered test scenarios used to generate realistic estimates of the impact of DREAM on the identified KPIs based on the envisaged use of the platform within the pilot organisations. Given the various nature of the pilot companies involved in DREAM and their corresponding industries, the results obtained can be generalised to a wider spectrum of companies. For instance, in the BSCI pilot, DREAM has been used to support tactical and operational decisions. At tactical level, line design issues can be solved by simulating alternative scenarios and assessing the impact of line design changes on performance metrics; design of experiment approaches can be jointly used to further support optimal decision making in this context. Test scenarios suggest that minimal design changes, such as the reduction of the production batch size, can generate significant productivity and resource utilisation improvements: 5% cycle time



reduction, 2% increase in throughput, reduction of waiting (10%) and blockage (5%) times. This shows that tangible benefits can be obtained at tactical level when DREAM is used. In particular, with respect to other simulation packages available, the advantage of DREAM consists of offering objects specifically built for the BSCI production lines; these objects incorporate the production flow logic implemented at the Cork plant. At operational level, it is demonstrated that higher workforce allocation flexibility can lead to considerable improvements in productivity (14%); this can be achieved at no additional training costs by simply exploiting the workforce cross-skills in a more effective fashion. From this viewpoint, DREAM can also offer industrial engineers a means of exploring the impact of training policies on productivity performance by performing what-if analyses on the set of skills required to operate a production line. Likewise, simulation analyses can be conducted in relation to the number of skilled operator that should be assigned to a line in order to achieve productivity targets. For the IFX pilot, it has been shown that DREAM could positively impact the demand planning process as the introduction of an evolutionary optimisation logic can lead to reduced number of excess orders (20%), expected lateness reduction (~75%), earliness reduction (~60%) and higher likelihood to obtain bottlenecks utilisation closer to the target levels in terms of both average (~15%) and variability (~15%). It is worth noting that the DREAM approach is based on simplifying assumptions whereby some planning constraints considered by the divisional model are neglected. These assumptions were made so as to avoid exceeding calculation resources. Nevertheless, the comparative results obtained through the validation analysis demonstrate that if both approaches could be implemented with the same constraint details, DREAM would be definitely the better choice. Moreover, DREAM introduces optimisation elements concerning target level utilisation that are not considered by the IFX demand planning system. For the two SMEs involved in DREAM, the level of improvements to expect in their decision making is even greater because they had no previous means of aiding their decision making. In particular, the companies have no means of estimating and offering valid delivery dates to customers at the order negotiation phase, as this should take into account the current status of the shop floor and project its future status based on the operations that have to be completed. In general, for similar manufacturing companies where decision support in needed in the order management process, the potential impacts of the implementation of the DREAM platform can be summarised as follows:

• DREAM generates multiple decision making possibilities, and simulate into the future their final impact on the objectives. Out of these it is able to identify the best decisions to follow.

• DREAM delivers an optimal long term schedule that can suggest plans to be followed for several weeks of operational level work.

• DREAM has a KE tool which can be used to keep track of the current system status in real time.

• DREAM’s capacity utilisation metrics and Gantt chart schedule shows the level of operational capacity utilisation across the planning period, and the slack times that exist during the execution of a project. As a result, variable capacity can be planned in a more effective manner.

4.2 Main Dissemination Activities Dissemination of results has the objective of transferring general know-how from the project to the wider public and promoting the wider adoption and endorsement of project results. The main objectives of the amePLM dissemination strategy are the following:

• web portal setup and management: this activity started from the beginning and important results are achieved. First of all, the project web portal has been defined and developed to support information and communication needs of all the project stakeholders. The web portal foresees access to a public area, providing information on project and public results and to a restricted area for use by the project partners, devoted to support project



governance, and managed by the management group. The website is available at the following address: http://www.dream-simulation.eu/

• events organisation/participation: the aim of this activity consists in disseminating the project concepts and results. The partners of DREAM project participated at several academic conferences, industrial fairs and workshops;

• dissemination material: the official templates of the project have been set up and used by all the partners for preparing internal documents, official deliverables, internal and public presentations. The Consortium has developed also a project leaflet and new versions are part of the on-going work.

• the DREAM-consortium sees dissemination as a continuous activity which will be continued after the project end to benefit from the project and its results in an ongoing manner.

Target Groups Having clearly identified what it is the project has to disseminate, the consortium defined the “whom we want to disseminate”. This requires some analysis of your various stakeholders. The term “target audience or group” can be used to describe the different groups of stakeholders connected to the project. For the sake of clarity, audience groups for dissemination activities of DREAM include:

• academy/scientific community and students, as the future players of the new economic and social scenarios addressed by the project;

• employers’ associations, for consensus building on new industrial paradigms; • professionals and professional associations, as major players in the amePLM vision; • industry and business as end users of the results.

To address the target groups, numerous activities have been executed during the project duration, please refer to the respective overview provided, and will be continued after the project end. For example, DREAM-results will be lectured at courses of the involved universities (Stuttgart, Limerick and Dublin) and will be spread by further scientific publications. Besides dissemination, the DREAM partners see exploitation as an ongoing activity, going far beyond the projects’ end. 4.3 Exploitation of Results Essential “tangible” results, besides the approaches and methods, developed by DREAM and laying the foundation are the following software modules:

• a platform based on the Open Source ERP-system ERP5 to embed the components; • ManPy (Manufacturing in Python): an expandable library of manufacturing objects written

exclusively in Python, making use of the SimPy library to facilitate discrete-event simulation;

• a knowledge extraction (KE) tool to link production data with simulation software; and • DREAM GUI: A browser based drag and drop graphical user interface written in JavaScript

using several libraries (JSplumb, JIO etc.) to simplify the creation of browser-based user interfaces.

DREAM platform exploitation will be implemented through an open source community sponsored by privates companies and research institutions, focusing on the “ManPy” library and modeled after the successful approach of the “scikit-learn”1 project. Private companies in this community are in charge of providing enterprise services and cloud offloading services to users of the DREAM platform, which comprise manufacturing companies but also new digital industries that can also benefit from discrete simulation for business process optimization. Research institutions, RTD



partners, are in charge of extending the DREAM platform with new algorithms and educational materials. Maintenance of the code base is a shared effort of private companies and research institutions from the DREAM consortium. Open source communities in related fields (ex. python, data science, etc.) and open source applications (ex. ERP, CRM, etc.) are leveraged to strengthen the attraction of the DREAM platform. The mission of private companies which sponsor the DREAM platform is to provide professional services based on the ManPy framework in order to solve problems in the field of actionable analytics in different fields of industry, including manufacturing but also e-business, legal, accounting, etc. The mission of research institutions which sponsor the DREAM platform is to extend the ManPy framework with new generic classes and new algorithms. Maintenance of the ManPy open source project (software, documentation) is shared by private companies and research institutions which form the main relation with industrial and academic partners. Key to success of professional services based on ManPy is the ability of skilled engineers to turn a complex simulation problem into a ManPy configuration, in significantly shorter time than it would take a newbie engineer to learn and configure ManPy by himself. Another key to success is to provide services at significant low cost and with significant higher flexibility than competitors that use traditional commercial off the shelve simulation platforms such as “Plant Simulation”. Another key to success is to combine ManPy simulation with new technologies such as web, mobile and hybrid cloud where competitors are not present. Key to adoption of ManPy is the ability to access an easy to understand documentation that can be used for example to train students to discrete event simulation. ManPy and the DREAM platform should be simple enough for businesses to start using it on simple cases without having to purchase professional services. Wider adoption of the DREAM platform on simple cases leads to more revenue to help customers solve more complex cases. The objective of the ManPy community is to ensure sufficient revenues with professional services or research contracts to finance future R&D of the DREAM Platform, mostly ManPy library, and its documentation. Exploitation of DREAM platform's workflow editor will also be achieved through Nexedi's OfficeJS platform as planned in the Description of Work (DOW) of DREAM project, since this workflow editor can be applied to multiple business fields in addition to discrete event simulation. Private companies which sponsor the DREAM platform generate revenue through professional services sold to businesses that need further expertise than the one they can get through the default simulation classes of ManPy or by reading the manuals of the DREAM platform. The first type of professional service is the implementation of custom applications based on the DREAM platform. This includes consulting, project management, configuration and development. It may also include research and development of the DREAM platform itself whenever it is required to extend it to address complex cases. The different kinds of implementation services include: simulation problem resolution, DREAM Designer integration and DREAM platform integration. Simulation problem resolution consists of providing to a client (ex. manufacturing company) consulting, project management and configuration of the DREAM platform in order to solve a business optimization problem using a simulation method based on the ManPy library. This service leverages methodologies that are part of the Key Exploitation Results of the project defined during the Exploitation Strategy Seminar (ESS).



DREAM Designer integration consists of providing to another software company software development service to integrate DREAM Design to another application (ex. ERP, CMS, etc.). DREAM Platform integration consists of providing to another software company consulting and software development service to integrate DREAM Designer and DREAM Runner to an existing software (ex. ERP, BPM, etc.) with the ultimate goal to include optimization or analytics features to that software. Training services consist of teaching engineers or developers how to use or extend DREAM Designer or DREAM Runner. They can be based on the lecture materials that have been prepared simulation based application Decision support in Real-time for Efficient Agile Manufacturing by UL for academic dissemination and that are now being integrated as HTML page to the ManPy community web site. Support services consist of providing guaranteed bug fixing to users (engineers, developers, endusers) of the DREAM Platform. Cloud services consist of providing a collection of cloud nodes running SlapOS to execute DREAM Platform models more easily and faster than it would take on the user's computer. First cloud service prototype has been implemented in 2014.



5 Public Website

Up-to-date information is provided at www.dream-simulation.eu .

http://www.dream-simulation.eu/



6 References

Aberdour, M. (2007), "Achieving quality in open-source software", Software, IEEE, Vol.24 (1), pp. 58-64.

Barlas, P. and C. Heavey (2015). “Automation of Input Data to Discrete Event Simulation: A Review.” International Journal of Modeling, Simulation, and Scientific Computing [accepted with revisions].

Barlas, P., I. Lanning and C. Heavey (2015). “A survey of open source data science tools.” International Journal of Intelligent Computing and Cybernetics 8(3).

Borenstein, D. (1998), "Towards a practical method to validate decision support systems", Decis.Support Syst., Vol.23 (3), pp. 227-239.

Can, B., and C. Heavey. 2012, 2. A comparison of genetic programming and artificial neural networks in metamodeling of discrete-event simulation models. Computers and Operations Research (39). 424-436.

Dagkakis, G., C. Heavey and C. T. Papadopoulos 2013, "A Review of Open Source Discrete Event Simulation Software",9th Conference on Stochastic Models of Manufacturing and Service Operations, 27-35. DREAM - Description of Work. 2013.

DREAM – DOW, Description of Work 2013.

DREAM – D3.3, DREAM system knowledge extraction instruments 2015.

Michlmayr, M., Hunt, F. and Probert, D. (2005), "Quality practices and problems in free software projects",Proceedings of the First International Conference on Open Source Systems, 24-28.

Michlmayr, M., Hunt, F.and Probert, D.(2007), "Release management in free software projects: Practices and problems", in Open Source Development, Adoption and Innovation, Springer, pp. 295-300.

Olaitan, O., P. Young and J. Geraghty 2015, "Variable Intensity RCPSP Approach to a Case Study Flow Shop", Proceedings of the 2015 Summer Simulation Conference, Chicago, July 2015,.

Olaitan, O., et al., (2014), "Implementing ManPy, a Semantic-free Open-source Discrete Event Simulation Package, in a Job Shop", Procedia CIRP, Vol.25 pp. 253-260.

Robinson, S. (2008), "Conceptual modelling for simulation Part I: definition and requirements", J.Oper.Res.Soc., Vol.59 (3), pp. 278-290.

Robinson, S. (2008), "Conceptual modelling for simulation Part II: a framework for conceptual modelling", J.Oper.Res.Soc., Vol.59 (3), pp. 291-304.

Rotondo, A., et al., 2015, "Embedding Optimization with Discrete Event Simulation", 10th Conference on Stochastic Models of Manufacturing and Service Operations, July 2015, Volos, Greece.

Documents

simulation based application Decision support in Real-time