ERP Integration into Generic Plant Automation Model

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1 Integration into Generic Plant Automation Model Yoseba K. Penya, Alexei Bratoukhine and Thilo Sauter Institute of Computer Technology VIENNA UNIVERSITY OF TECHNOLOGY Gußhausstraße 27/E384, A-1040 Wien AUSTRIA {yoseba, bratukhi, Abstract Mass-customized production systems are challenging in that they intend to provide customspecific products at the price of conventional mass production. Since this also includes small production volumes, the manufacturing systems must achieve flexibility, easy reconfigurability, and a totally product-oriented approach. In this paper, we introduce a flexible automation model developed within the scope of the European Union-funded BADIS project (Plant Automation Based on Distributed Systems) that meets these requirements by combining Plug-and-Participate technology with Software Agents. I. INTRODUCTION Enterprise Resource Planning () has become an important key to the survival of many companies in the current rapidly increasing pace of business. New facts as deregulation, globalisation, mergers, acquisition and hard competition have forced firms to extremely improve the use of each resource. With this purpose as guideline, the concept of was developed to automate core corporate activities such as human resources, supply chain management (SCM) or product manufacturing. systems, as successors of Management Information Systems (MIS) and Expert Systems, address the problems inherent to the transition from mass production to mass customization [1]. This term, coined by Davis in [2], refers to a system where one-of-a-kind product is manufactured on large scale [3] but at the cost of mass production [4]. This way, the customer might be able to choose from a wide range of varieties and characteristics without having increased the price that would be paid for a fixed-variety product. Following the categorization done in [5], this paper deals with the implications derived from mass customization as operations management. Within this context, we introduce the on-going research project BADIS [6] (funded by the European Union under the IMS and IST programmes) that proposes a solution based on the combination of distributed network technology and software agents. The goal of BADIS is to achieve an intelligent manufacturing system with a productoriented approach, suitable primarily for single-piece production systems, but also for the challenges of masscustomized production. The remainder of the paper is divided as follows. In section II, we describe the problem addressed by the BADIS project. Section III explains how it can be integrated within an system. In section IV, we introduce the architecture of a BADIS automation plant and in section V, the way to achieve a productoriented approach with Software Agents. Finally, we draw the conclusions inferred from the issued argumentation. II. PROBLEMS ADDRESSED The translation from mass production to mass customization implies a subsequent change from a seriesoriented approach to a product-oriented one. Thereby, the emphasis may be shifted from a pure cost effectiveness to a more flexible system, able to quickly respond to different demands. Furthermore, unlike what is exposed in [7], it is not necessary to focus the process on the tracking of the design changes, but on providing updated information about the current situation of the machinery, stocks, and other products. Derived time waste like the set-up of the machines may be reduced with techniques such as Single Minute Exchange Die (SMED), but they still need actualized data to plan the moment when the tooling must be performed. Moreover, such information-flow schemes usually lead to the so-called scheduling problem [8]: how to adapt dynamically the demand to the existing resources in an optimal way (taking into account that a masscustomization system is a demand-driven one). Finally, robustness is mandatory as well, which leads to the requirements that a mass-customization production system should be flexible, fault-tolerant, and it should also have a real-time information system combined with an effective dynamical scheduling.

2 BADIS Accounting & Financial Fixed Assets Warehouse & Commercial Production -Parser -Parser... -Parser Employees Machines Material s Agency Interface Service SCM CRM Figure 1: Integration of BADIS and an system III. MODULAR ARCHITECTURE: DIVIDE AND CONQUER Lately, software programs are usually designed as modular architectures. This practice principally presents the following advantages: Separated development of each module Isolation of the problems and Software reusability. In terms, the modularization corresponds to componentization, the division of the tasks and responsibilities of the whole system between different components, as shown in Fig. 1. This helps the vendors to extend the core system with SCM (Supply Chain Management), CRM (Customer Relationship Management), and other -related solutions. Moreover, the ability to integrate various Information Systems within the is becoming an important factor for both enterprises and vendors. At this point, (extended Mark-up Language) arises as a reliable solution for a valid and universal interface. In the plant automation pyramid, is on the top layer, control devices are on the lower level and Manufacturing Execution System (MES) and SCADA systems are in the middle. Today, MES typically is a centralized system that controls the execution of orders performed on the shop floor level. The idea behind BADIS is to decentralize this system by using a community of intelligent software agents in order to make an execution of orders more flexible. Furthermore, the BADIS approach provides an interface to the SCADA system, gives an infrastructure to control the devices layer and partly implements functionality by providing an interface to the so-called BADIS community. According to [12], functions covered by the MES are the following: Resource allocation, scheduling, and dispatching: This most complicated function of the MES manages the resources of the plans and performs order execution. On one hand, the goal of this function is to optimize the usage of resources, such as machines, tools, labor skills, or materials. On the other hand, the product creation must be optimized as well. In BADIS this functionality is distributed in the community of agents which act individually with respect to their own tasks, such as product creation or machine optimization. Document control, data collection, product tracking and genealogy, process management and performance analysis: This function provides information to the system regarding the current situation of the production and product processing. Maintenance management, which tracks and directs the activities to maintain the equipment and tools, in order to ensure their for manufacturing and to ensure scheduling for periodic or preventive maintenance. While the mainly gathers the information about the product to be manufactured, the SCM prepares the raw materials needed for the process and BADIS performs the development itself. Of course, there are running solutions available (such as FMS) that already fulfil this role. The difference is the fact that BADIS not only substitutes the MES functionality, but it also achieves a better control of the production by focusing it on the individual development of each product, as it will be shown in section VI. Obviously, this productoriented approach is ideal for mass-customization systems. IV. NETWORKED PLANT AUTOMA- TION As already mentioned in section II, one of the desirable aspects for mass-customization systems is a real-time information flow. The plant automation functions themselves are within BADIS accomplished by elements known as Cooperative Manufacturing Units (s). They represent the functionality of the plant (like manufacturing facilities, control devices, databases, etc.). A may offer a simple process in one device, the whole functionality of it or even an entire treatment

3 within a group of machines represented by the. The s are divided in three categories, depending on their task [9]: Manufacturing s are used for the physical processing of products. It and can be a single machine, only one function of a device, a set of modules, or the whole production line at all. This type of always has a hardware (manufacturing) component, which can be used by the agent community in order to execute orders. Logical s provide computational services like scheduling algorithms or database search or any other calculation and analysis issues. This can be a software program or a logical device. This type of does not need a hardware part, or at least it does not depend on it. Unlike manufacturing s, logical s operate with data instead of work pieces. SCADA s for Supervision, Control, and Data Acquisition. A SCADA system is centralized by nature. Therefore, attempting to distribute it makes no sense. In fact, the SCADA is a special type of logical providing an interface to a possibly existing SCADA system. Each of the various s hosts a stationary software agent (the so-called Residential Agent, ) which acts as an interface between the BADIS community and the. In this context, the term agent means autonomous software programs that are designed to solve problems by using some means of reasoning and communication [10]. Moreover, the Residential Agent represents the cleverness of the local machine that cooperates with the system during resource allocation, task execution, etc. All s are connected and form a network. Therefore, not only is the manufacturing itself distributed, but also the knowledge and information retrieval. When a is connected to the community, its Residential Agent contacts a Look-up Service (LUS) in the network and registers there the capabilities of the machine or group of machines it represents. This behavior allows first to maintain a plug-and-participate (PnP) system and second, to keep the information about the plant updated. Furthermore, the PnP functionality makes the setup of the system easier, in case of tooling, removing of a machine or installation of a new one. Since the LUS renews the database periodically, it is guaranteed that the information retrieved is up-to-date. V. ONE PRODUCT, ONE AGENT One of the major advantages of BADIS over other plant automation systems is its strict product orientation. This is based on the concept one product, one agent, which means that every single work piece (or lot of identical work pieces) is represented by a software agent. AGENT CREATION The production flow in a BADIS plant starts with the system receiving an input of the client about the product. After matching this information with the real capabilities of the plant, the system issues a work order that clearly specifies the different machines that can be used to fulfill every step of the work order. Obviously, there may be redundant or similar functionalities available in several machines. This can be clarified with the following example: Consider a plant with two types of drilling machines, which can drill either one or two holes at a time. The task of drilling two holes into a work piece could be performed by both types of machines, but the one-hole drillers would need two steps to manage it. In this way, the drilling of three holes on a piece could be shaped into an ordered graph as shown in Fig. 2. The graph describes three different paths, two of them using the two-hole machine () and the third one, with a triple loop on the one-hole (1H) machine. Drilling 3 Holes 1H 1H 1H Figure 2: Graph corresponding to the drilling machine example. When the has examined all the possible paths to fulfil the manufacturing of the product, it creates the work order and sends it to the so-called Agency, the interface between the community and the. The Agency creates a new agent, called the Product Agent (), and provides it with the work order and necessary information to the successful development of the task. The is now ready to interact with the - BADIS system, as shown in Fig. 3. SCHEDULING The Product Agent analyses the work order and sends to the Look-up Service the functionalities required for the execution of the individual production steps. The LUS answers with a list of possible s where the tasks could be performed, including the following information: Machine identifier, Maximum time expected to finish the task and Current status of the. These data are only rough estimates that are provided to the LUS by every on a regular basis and serve as 1H

4 a first starting point for the calculation of the schedule and the subsequent optimization. LUS AF Agency SCADA - Figure 3: Components of the BADIS system The completes the graph with the estimated time for every production step to get a first overview. The initial classification of the paths is most likely with respect to processing time, although other criteria can be conceived (e.g., combined execution time and costs). In the example of the drilling-machines it would be as follows: Path 1: 30 min. Path 2: 35 min. Path 3: 35 min. Drilling 3 Holes 10 min 10 min 10 min 1H 1H 1H Path 1 25 min 1H 25 min 10 min Path 2 Path 3 Figure 4: Graph with time estimations. Next, the must calculate a more realistic estimation taking into account the actual work load of the s. For this purpose, it contacts all the s that form the path with the best estimate (in the example path 1) and get the first available time slot that also fits its own plans (accounting for the time needed for previous tasks, work piece delivery, etc). Finally, it considers if the total time estimation for the path is acceptable and confirms the time slot reservations. It may happen that the s are overloaded and therefore, the time estimation becomes unacceptable due to waiting times. In this case, the processes the next best path in the same way, until one of them is suitable. If the does not manage to find an acceptable path for the manufacturing of the work piece, it returns an error to the Agency. There are still two parameters that complicate the scheduling process but that make it more flexible and useful as well: depth of scheduling and priorities. Depth of scheduling means for how many steps in advance the reserves resources. There are three possibilities to do this: Advance scheduling: In this case a allocates the resources for all tasks in the work order. Thus, the analyses the whole work order and chooses the best possible way of execution. A problem may arise if a re-scheduling process is necessary. For instance, the receives the notification that a scheduled resource is no longer available, so it has to re-schedule all following tasks and re-allocate the appropriate resources. This leads to a snowball effect, which dramatically increases the network traffic. This kind of scheduling is suitable for work orders with few tasks and large bandwidth of the network. Step-by-step scheduling: In this case a allocates a resource for the next task only when the current one is completed. In fact, real scheduling is not necessary for the, which makes the optimization of resource usage impossible. On the other hand, the traffic problem is avoided. This method is reasonable in the case of large numbers of s with many tasks within a work order and a weak network capacity. Hybrid approach: The most flexible solution is to use a hybrid method. In this case a analyses a work order several steps in advance and allocates resources for a certain number of tasks, but not for the whole work order. The number of tasks which a analyses in advance is called the scheduling depth. Depending on the implementation the depth can differ from one step (step-by-step scheduling) to N (advance planning), where N is a number of tasks in the work order. Finally, only tuning can help to define the scheduling depth and the use of priorities, since there is no exact formula that helps to get them. Both parameters extremely rely on the characteristics of the system: number of s, execution time on every, number of agents in the systems at a time. WORK ORDER EXECUTION When the scheduling is finished, the starts the migration within the network escorting the work piece on its way through the plant. At this stage, the tasks of the are first to control that everything works under normal conditions and second to retrieve the data related to the operations. As mentioned before, the scheduling may not yet be done completely or a re-scheduling pro-

5 process might affect the plans of the Product Agent. Therefore, it could happen that the must go through a new scheduling process to reconstruct the execution plan for its work piece. Finally, there is a concept that allows the BADIS model to deal with series production. The model as it was described until now is suitable primarily for productions where one product is manufactured at a time. If more items of a product are required (which applies to mass-customization systems), BADIS should create an agent for each of them, which could negatively affect the throughput of the system. The concept to remedy this situation is called granularity and allows - BADIS to specify that more than one item of a product is going to be processed. Therefore the complete lot can be represented as one work piece related to one. This increases the effectiveness of machine usage and decreases the number of s migrating within the plant network; hence, the network traffic is abated. In singlepiece production, the granularity parameter must of course be set to one. VI. CONCLUSION The evolution from mass production to mass customization requires not only a change from centralized to decentralized systems, but also shifting from a seriesoriented to a product-oriented approach. The proposed agent-based approach is a reasonable way to achieve the required flexibility. Of course, there are other solutions in the market that combine software agents and network technology. For instance, the Holonic Manufacturing Systems project [12] proposes a decentralized plant of immobile agents where the emphasis is rather on the production and not the product. By contrast, BADIS proposes a product-oriented solution, based on the division of the plant between small, modular and intelligent units. This approach allows achieving a decentralized view and gaining robustness and flexibility. Finally, since every Product Agent represents one product (irrespective of the lot size) and controls its manufacturing process, the system is very scalable, and an increase in the number of Product Agents does not result in an increased complexity of the individual system components. ABBREVIATIONS CRM LUS MES MIS PnP Cooperative Manufacturing Unit Customer Relationship Management Enterprise Resource Planning Look-up service Manufacturing Execution System Management Information Systems Product Agent Product Management Agent Plug and Participate Residential Agent SCADA Supervision, Control and Data Acquisition SCM Supply Chain Management extensible Markup Language REFERENCES [1] Dimitri N. Chorafas Integrating, CRM, Supply Chain Management, and Smart Materials, Auerbach 2001, pp. 7-8 [2] Stan M. Davis, "Future Perfect", Reading, Massachusetts, 1987, pp [3] Thomas P. Cullinae, Pratap S.S. Chinnaiah, Naken Wongvasu and Sagar V. Kamarthi "A Generic IDEF0 Model of a Production System for Mass Customization", in PICMET 1997, Portland International Conference on Management and Technology, 1997, pp [4] Simon Proops, "Mass Customisation: Stimulating the knowledgeable Market", in Mass Customization (Digest No: 1996/181), IEE Colloquium on, 1996, pp. 1/1-1/6 [5] Alan Pilkington and Derrick Chong, "Mass Customization: Conflicting Definitions", in ICMIT 2000, Management of Innovation and Technology, Proceedings of the 2000 IEEE International Conference on, 2000, pp , vol.1 [6] BADIS, IST , [7] Helen Xie, "Tracking of Design Changes for Collaborative Product Development", in Computer Supported Cooperative Work in Design, The Sixth International Conference on, 2001, pp [8] Thilo Sauter and Pierre Massotte, "Enhancement of Distributed Production Systems through Software Agents", in ETFA 2001, in Emerging Technologies and Factory Automation, IEEE International Conference on, 2001, pp [9] Thilo Sauter and Peter Palensky, "Network Technology for Distributed Plant Automation", in INES 2001, Intelligent Engineering Systems, IEEE International Conference on, 2001, pp [10] Charles J. Petrie : «What s an Agent and what s so intelligent about it?», in IEEE Internet Computing Vol.1, Number 4, July/August 1997, pp. 4-5 [11] M. Fletcher, S.M. Deen, Fault-tolerant holonic manufacturing systems, Concurrency and Computation Practice & Experience, vol.13, no.1, 2001, pp ,. [12] MES Association official web-site.

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