Showing posts with label Process. Show all posts
Showing posts with label Process. Show all posts

Sunday, June 27, 2010

Collaboration and Process Modelling in Engineering and Business

Use of Semantic Web and Web 2.0 techniques could enable domain experts such as engineers to be involved in the modelling of a problem such as product design, and so understand, assess, and develop possible solutions. The article also examines how modelling, ontologies and Semantic Web/Web 2.0 technology can aid in collaborative management of complex systems such as those involved in process modelling and product design. Visualisation of the most useful representation of the collaborative knowledge and models, and translation between the human and computing representation of this is important for this collaborative modelling.

Visualisation is necessary to provide traceability for decision making, as an audit trail for information, to justify a decision and/or cost, or to understand a process, or a product data structure. It is necessary to navigate a database in order to validate it, the structure of the visual interface needs to show the structure of the data, and the model. The best way to do this is to relate them all so that any change to the database, changes the structure of the model and this changes the structure of the visualisation, or vice versa. Semantic search allows someone to see results or individual items but enables going straight to these items, that does not show the context and the model is still a 'black box', unless the Semantic search actually returns the relevant portion of the structure so that the context of the returned information is displayed.

For each organisation type, a process architecture(s) can be identified that offers potential for reuse by other organisations of the same type. This can enable a better connection between process designers and modelling tools and a shared understanding between domain experts and model developers. A shared understanding and reuse of process architectures can enable improved decision making and performance especially for complex systems, indeed, complexity is a core problem found in most organisational activities. Problems with incompatible software and team communication cause inability to meet delivery deadlines and to control costs, poor product/service quality, delays and cancelled orders, resulting in lack of competitiveness and, ultimately, job losses. So shared understanding of processes between all involved within organisations is essential.

A User Driven Modelling/Programming (UDM/P) collaborative approach and its application to systems/process modelling research needs to be developed. This could enable taxonomic and diagrammatic visualisation and calculation of sequences, times, and costs for complex processes. Provision of a process repository of reusable process architectures could assist industry and organisations to develop more effective service and production processes.

Examples of difficulties in modelling complex processes/projects include:


Lack of interoperable tools that are usable by domain experts with limited programming knowledge.
Lack of tools for process modelling and web service discovery and lack of online search facilities is hindering development and reuse of process models.
Overuse of non integrated spreadsheets and poor links between these and 'high end' tools, such as specialist process modelling tools and CAD. This hampers the scope for modellers to investigate problems systematically and communicate solutions between users of the various tools.
Poor human/computer translation that fails to convey the model present in human minds to computer models. A systematic approach of layered abstraction combined with sophisticated structuring and translation is required for successful modelling.
Because of the lack of automated human computer translation, modellers currently need to request the help of professional developers such as provided by an IT department, this can slow model creation and embed misunderstandings.

Almost all organisations use spreadsheets to manage information and calculations. However, it is often difficult to follow the relationships between items of information, leading to confusion, and incorrect conclusions. Spreadsheets are often produced by individuals who have had little formal training and there may be inaccurate and undocumented models produced that lack visibility of structure and relationships. Spreadsheets are popular because models can be created without writing code. with aerospace, ideas discussed in this paper are built partly from that Rolls-Royce aerospace DATUM project. Thus we will develop structured visual process modelling tool(s) that can be used without code writing (though they will produce code that can be edited if required).

Collaborative modelling to determine requirements was investigated. Alternatives to current software development approaches are necessary. The need is to translate from a model-based visual representation understood by users to software. Successful interaction requires mapping between levels of abstraction, and translation between these levels required by people and computers is difficult. He explains that this problem means systems are created that make people cope with the problems of mis-translation. Rules and information can be illustrated diagrammatically. It is possible to describe algorithms through concrete examples rather than abstractly. Models must be designed and visualised to convey to users a representation of problems that assists with their vision of them. This subject is basis of the development and visualisation techniques evaluated in this paper. These techniques enable users to create and understand models, which are translated into software representations.

Collaborative modelling can aid supply chain management. There is much evidence that collaborative modelling using web-based techniques is practical and important. An open standard web driven method of collaboration is required, in order to enable organisations and individuals to become more involved in projects with web technologies. Open standards can assist in an organisation's collaborative product development. The next task is to develop such research into visual end user programming to enable those with limited software expertise to make use of collaboration and process modelling tools. There is a need for demonstrating the application of web-based simulations for major projects. They argue the need for web-based modelling to be focussed on solving real-world problems in order to be successful.

Possible solution

A solution to the above problems would be constructionist modelling/simulation of problems using accessible and easily used tools that can empower domain experts to create collaborative systems. Thus the overriding goal of this research is to provide a means whereby control over simple or complex processes/projects and modelling of them is improved. Semantic Web software will be investigated to allow collective modelling of complex problems and workflows, using a standardised infrastructure.

Repositories of process architectures are held in an ontology, and linked via translation and visualisation in multiple process modelling tools, and web applications and web services, to maximise reuse. Ontologies are highly structured information sources. This could enable automated web service discovery, composition, and execution, service reuse. This would be enabled by building of process models and modelling systems using Protégé and Jena.

The main aim of the research is to incorporate visualisation of the problem and take a step towards enabling non programmers to add and edit process models interactively. This could assist companies/organisations to become more agile in solving problems and incorporating lessons learned. To achieve this, it is necessary to make it possible for industry to link formerly incompatible software via open standards, thereby improving team working. This could allow people to model problems at a high level of abstraction and automate translation to computer code. This approach of empowering users by enabling abstraction from software details, and concentration on business needs is important. This can be achieved by enabling non-programmers to create models for process modelling and decision support. The mechanism for this is to supply domain experts with the tools they need to become end user programmers/modellers.

It is essential that new ways of enabling collaboration between all those involved in process modelling software creation and use are investigated. An important part of the project was to investigate the use of Semantic Web languages for programming, rather than just for representation of information, applying this technique to process model creation. Such Semantic Web programming could build on languages such as SPARQL (SPARQL Protocol And RDF Query Language), to provide automated querying and eventually model building and translation. Interoperable process modelling/planning can be assisted by Process Specification Language (PSL) for exchange of process/workflow/production planning, scheduling, and simulation information. The use of open standard languages for declarative programming can ease the translation between different representations of information, and ease interoperability between systems. This translation or 'Program Transformation' allows for writing in one representation or language, and translating to another. This is particularly useful for language independent programming, or for high level and end user translation from human representation to a language more easily interpreted by computer systems. Techniques such as Model Driven Programming, Generative Modelling/Programming and Meta Programming will be used to create the modelling environment for end users. Translation using the above approaches and between them can enable non programmers fo development of systems that are not a 'black box'; so using a more open approach.

If a problem is more visual and nearer to the persons' way of thinking', it can assist a modellers' tasks. Diagrammatic Modelling such as used in UML can assist with this, and Activity diagrams are often used for production of OWL-S process models and services. UML (Unified Modeling Language) tools need to be extended to better enable modelling of collaborative tasks. Production of better and more adaptable and applicable models can be enabled by meeting the objectives of enabling better Maintenance, Extensibility, Ease of Use, and Sharing of Information. These objectives will be enabled by better structuring and better visualisation; this requires work on structuring using Semantic Web and Ontologies, and enabling better visualisation through and for end user programming techniques. This makes the models more accessible, and so easier to edit, reuse, adapt and maintain.

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Sunday, March 14, 2010

Simple Statistical Process Control (SPC)

Many small businesses have been asked to begin performing statistical process control on a part they've been manufacturing or are considering manufacturing, as a requirement of ISO-9000 or other new quality system requirements. Confronting the highly technical presentation of information published on the topic sends most small manufacturers into an information overload. There is a simple way to approach this and start using this extremely useful SPC technique without facing the prospect of learning advances statistical mathematics.

The most useful statistical process technique for small manufacturers is what is known as the average and range control chart, or more informally the X Bar R chart. This simple way of looking at the control of a process may be used for literally any process, from machining precision metal parts to making pizzas, using this control method, you are able to greatly increase consistency in making anything.

An average and range control chart is generated for each characteristic of the process you wish to control. For instance in a lathe turned pin, you might choose to have an X Bar R chart for the outside diameter and another for the length. In the case of pizza manufacture you may decide to have one for the quantity of cheese added and another for the crust dough thickness. In any case, you only use a control chart for those characteristics that are critical to the quality of your product. These are usually referred to as Key Product Characteristics (KPC) and usually are determined by important fit of function parameters.

The average and range control chart tells you when you need to adjust a process to keep it under control. Likewise it tells you when to keep your hands off. Because over-controlling a process introduces even more variability into it. For instance of you adjust a machine every time it makes a part that is not exactly nominal, your variations will increase rather than decrease, because there is always some inherent variability in any process. Allow that inherent variability to take place and the process will stay centered.

An example of this was in a second operation machining operation being performed by a plastics manufacturer. Once an hour, the supervisor took a part from the production, measured it and adjusted the machine so that the next part would come out on the nominal. After introducing the X Bar R control chart to the process and teaching the supervisor to not make any adjustments to the process until the process actually strayed outside the control limits, it was found that no machine adjustments were needed for at least a week, and the precision of the process increased greatly.

The introduction of the average and range control chart, and the performance of the SPC tasks actually saved money because it made the process more efficient. The operator was making parts on a continual basis instead of waiting for the supervisor to make the machine adjustments every hour.

Using average and range control charts does not require expensive software. Simple Microsoft Excel spreadsheet templates are available to do the functions. It doesn't require an extensive mathematics background, only simple training in how to put data into the spreadsheet an dhow to interpret the results. Using one will increase the quality and efficiency of any manufacturing process.

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