The remainder of the paper is organized as follows: Section 2 reviews the ... control on the shop floor of a manufacturing plant. .... with the development of the next generation of computing technologies AmI focuses on the user and how.
Ambient Intelligent Framework for Manufacturing Process Optimisation Gearoid Hynes1, Fergal Monaghan1, David O’Sullivan1 1
Digital Enterprise Research Institute, University Road, Galway, Ireland {gearoid.hynes, fergal.monaghan, david.osullivan}@deri.org http://www.deri.org
Abstract. This paper discusses a framework to optimise manufacturing processes for small to medium enterprises through the application of ambient intelligent technologies. To profit in today’s market, small manufacturers must be efficient and agile to minimise cost and to meet variable market demand. Smart tags, agent technology, multi-modal communication, semantic reasoning technology and contextaware applications can be leveraged to provide the manager with intuitive access to the real-time data describing the status of the shop floor environment. Two manufacturing processes are targeted for systemic innovation: shop floor control and machine maintenance.
1
Introduction
Today’s market calls for smaller batch sizes of products with a larger range of variants of those products [1]. Market turbulence forces manufacturing plants to constantly adjust their production volume of products, variants and quantities. This is especially true of small to medium sized enterprises (SMEs) that rely on the flexibility and agility provided by their relatively small size to compete with larger competitors. SMEs have to meet the challenges of the increasing product variants and service content of products by innovating their manufacturing processes. Plant managers must also protect long-term investments in manufacturing systems. Therefore there is a need for flexible and agile manufacturing systems that provide the capability for systemic innovation to the plant manager. In particular, two processes that systemic innovation can be beneficial to are shop floor control and machine maintenance. Ben-Arieh et al state that “shop floor control involves the efficient and effective utilisation of resources at the lowest level in a manufacturing facility” [2]. Deterministic solutions to this problem are intended to solve it in an ideal predictable world, and are inappropriate in a harsh, real-world environment due to the uncertainty of machine failures, the arrival of parts, priority changes and a whole host of changing variables in the manufacturing facility [2]. Dynamic solutions involving the gathering of hard real-time data are desirable instead [2] to help the manager to consider various alternatives quickly. To further complicate the problem is the rise of flexible, reconfigurable manufacturing systems (RMS) [3]. On top of day-to-day shop floor control there is now a need for shorter product life cycles (the time from product conception to release) and a decrease in the batch size required per product [1]. This trend is being driven by market turbulence and sees plant managers look to RMS reconfigurable assembly machinery to protect long-term investment [1]. There is a significant advantage in having the agility to adapt manufacturing configuration to meet demand [1]. Machine maintenance further constrains manufacturing performance as machine downtime must be kept to an absolute minimum to remain efficient and competitive in todays market [4]. Unbehend calls for systems that can constantly monitor machines, recognise maintenance requirements in time, diagnose faults, troubleshoot and help maintenance engineers in preventative and repair actions [4]. The way forward for process innovation in industry lies in the radical innovation of the whole working environment to focus it on the human actor and by applying the systemic innovation approach. The Ambient Intelligence philosophy sees this human actor surrounded by environments which are sensitive and responsive to their wishes. This paper proposes an Ambient Intelligence (AmI) framework to enable SMEs to more easily introduce new manufacturing processes and optimise the processes that are currently
in use. A solution which incorporates technology from the fields of Ambient Intelligence and the Semantic Web to provide systemic innovation to the processes of shop floor control and machine maintenance will be discussed. The remainder of the paper is organized as follows: Section 2 reviews the manufacturing processes of shop floor control and machine maintenance. In Sections 3 and 4 we introduce the fields of Ambient Intelligence and Semantic Web and highlight the enabling technologies we use from each. We present our System Description, the Semantic Agent-based Generic Extensible (SAGE) framework, in Section 5. Section 6 illustrates the usage of our approach in several scenarios based around three different companies. Section 7 concludes the paper.
2
Processes
2.1
Shop Floor Control
Shop floor control is concerned with the efficient management and usage of resources at the lowest level of control on the shop floor of a manufacturing plant. Long range planning and deterministic solutions to scheduling problems in shop floor control to date have included mathematical models, heuristics and knowledge-based systems, each of which has met with varying success [2]. Mathematical models provide an optimal solution, but are unable to provide a solution which can be implemented in a reasonable amount of time, which is costly in the interim and by which stage the problem may have changed. Heuristics have the opposite problem in that they provide a very quick solution that is unfortunately short-sighted. They cannot take into account real-time information or consider parallel or alternate process plans. Knowledgebased systems do not scale-up well as they become disordered and search-intensive with size. They also have poor conflict resolution capabilities and inherit the problems of the heuristics approach. In place of these, Ben-Arieh et al call for solutions which monitor the environment in real-time and present relevant information to the manager so that they can make timely, informed decisions. To further complicate the problem is the rise of flexible, Reconfigurable Manufacturing Systems (RMS). In the past, efficiency in manufacturing meant efficiently continuing a constant process. Systems were configured once to run in a stable environment [3]. Today enterprises face a turbulent – permanently changing – environment and so to adapt must be able to reconfigure their systems continuously. Configurations of a system may be perceived as critical discrete points in an evolutionary trajectory. The possibility of exhibiting multiple configurations provides systems with means to meet diverse requirements. For instance, many living creatures adopt different configurations in different environmental situations. Many products such as cars and planes are developed and maintained with multiple configurations satisfying different sets of constraints. Transition of system from one configuration to another is called reconfiguration in this paper. Configuration and reconfiguration are inherently embedded in the whole life cycle of systems, as dominant means for systems to come into being, to grow to maturity, to deteriorate, and sometimes to resurrect. It has been observed that reconfiguration is one of the basic means for survivals and developments of systems (both natural and artificial ones). Many giant creatures failed to survive because of incapability of fast change/reconfiguration. This observation also applies to manufacturing. Companies in a turbulently changing market can only exist by means of dynamic reconfiguration. Enterprises cannot simply rid themselves of the old and purchase new replacements: there is a significant advantage in having the agility to adapt manufacturing configuration to meet demand and plant managers are looking to RMS reconfigurable assembly machinery to protect long-term investment [1]. The capability of reconfiguration will be the essential requirement for manufacturing systems to respond to unpredictable turbulence in environments. In order to respond to market turbulence, a manufacturing system must be able to dynamically change its configurations (reconfiguration). Manufacturing systems have to have the ability to change the configuration of their own structure as well as the structure and the functional principles of the production system itself. This gives rise to developing a dynamic reconfigurable manufacturing system to provide an integrated solution for manufacturing companies.
This is best highlighted by the fact that now there is generally a need for shorter product life cycles (the time from product conception to release), a decrease in the batch size required per product, but a larger number of variations on a product [1]. Instead of manufacturing large numbers of product x efficiently and to a high standard, manufacturers must now manufacture smaller numbers of each of products x1, x2, y and z efficiently and to a high standard. Market demand dictates the current levels of output for each product and product variant, and therefore the desired configuration of the manufacturing system. A configuration is a temporarily fixed state of a system under certain conditions [1]. The configuration of a manufacturing system may include the following components: • Concepts, rules/principles. • Complete set of constituent components and their attributes. • System architecture defining internal relationships and external relationships. • Methodologies and technologies including human skills and knowledge. The system configuration is a dynamic concept. Under different conditions, a system will exhibit different configurations that give the system the capability of evolution.
2.2
Machine Maintenance
Plant and equipment maintenance is a key issue in relation to the productivity of a manufacturing SME and has been since their adoption by some American based companies during World War II. Preventive Maintenance (PM) is the original maintenance strategy from which the others grew. This was originally a relatively straight-forward concept which primarily consisted of equipment overhauls done at regular intervals in order to keep down time to a minimum. Strategies began to get increasingly complicated when costs associated with basic PM began to rise sharply in relation to other costs. Reliability Centered Maintenance (RCM) and Total Productive Maintenance (TPM) are two industrial strategies used as long term methods for continuous improvement of machine maintenance. The history of each of these strategies can be traced back to the 1960's [5] [6] and have become increasingly popular over the last few decades. Maintenance strategies have evolved from the basic pre-war situation of fix it when it is broken to the modern situation where reliability, safety, quality, equipment life, cost effectiveness and environmental damage are key factors [7]. TPM is a maintenance program for the upkeep of plant and equipment focused on groups of employees systematically performing maintenance tasks. This strategy emphasises the role of people in the maintenance process and the importance of production and maintenance staff working together. Rather than focusing the maintenance on the individual pieces of equipment the emphasis is placed upon the process in which each piece of equipment belongs. TPM's two primary goals are to eliminate both defects and breakdowns and hence reduce costs and improve the efficiency of the equipment. Other benefits or TPM are increased employee efficiency, morale and job satisfaction. TPM is in particular suited to SME's because of its adaptability, particularly in volatile economic environments which most SMEs operate in. RCM is a systematic process of preserving a system's function by applying effective preventive maintenance (PM) tasks, differing from most PM approaches by focusing on function rather than equipment, governing the maintenance policy at the level of plant and is applicable in large and complex systems [6]. As with all maintenance strategies the primary goal of RCM is to reduce the costs incurred from plant malfunction or failure. RCM focuses on the preservation of system functionality. This is achieved by performing several tasks such as the prioritisation of defined failure modes as not all failures have as detremental an effect on production as others. For each of the failure modes effective PM tasks must be identified and applied when appropriate. Through the proposed system each of the maintenance strategies can be analysed and the appropriate one can be implemented. Information on production efficiency which includes data on stock, employee and machine efficiencies are combined to inform management on the most effective strategy for the manufacturing setup at that particular time. Combining the AmI infrastructure with the various strategies will allow innovative solutions to the maintenance problem to be realised. The data, managed by the semantic system components, from the AmI sensors provides far greater detail than is ordinarily available
on implemented maintenance strategies. The gathering and analysis of this information is automated and hence allows the production to be more efficient without creating any additional workload for the staff.
3
Ambient Intelligence
Ambient Intelligence (AmI) is a recently developed vision for the future of human interactions with electronics within their environment. As the name suggests the vision primarily consists of the user being surrounded by electronics, however the electronics are concealed from the user. They are embedded into ordinary, everyday objects such as chairs, walls, floors etc. The concept of ambient intelligence was first proposed by Philips Technologies in the 1998 in a series of workshops organised within Philips [8]. In 2001 ISTAG adopted Philips’ vision of AmI as one of their primary themes for the 6th Framework on IST Research. The end result of AmI becoming one of the primary themes is that a budget of €3.7billion is available for AmI based research in Europe. Both Pervasive (or ubiquitous) Computing (PvC), and AmI are very similar but they should not be confused with each other. The primary differences lie in their focuses. While PvC is primarily concerned with the development of the next generation of computing technologies AmI focuses on the user and how technologies can be used by the general public, businesses and government agencies. AmI can be looked at as combining three relatively new technologies: Pervasive Computing which incorporates the embedding of electronic devices into everyday objects, Ubiquitous Communication which allows these objects to communicate with each other and Intelligent User Interfaces which allow the user to interface with the embedded devices. The vision for AmI has been heavily influenced by the work of the late Mark Weiser on Ubiquitous Computing (UC) and therefore many of the analogies that apply to UC also apply to AmI. In one such analogy Weiser said UC can be looked on as the opposite of Virtual Reality (VR) [9]. VR is primarily concerned with transplanting the human user into an artificial, computer-generated, world; UC aims to incorporate the computer into the user’s environment. This led to UC sometimes being called Embodied Virtuality to highlight the process of “drawing computers out of their electronic shells”. In his description, Weiser goes on to explain how VR has its uses in simulating otherwise inaccessible realms for exploration, but that at the end of the day VR is only a map, not a territory. On the other hand, AmI takes the next step on from UC and enables real environments that are sensitive and responsive to the presence of people [10]. AmI has been called Augmented Reality to reflect this comparison with VR [11]. In general practice, an AmI system is a distributed network of tiny, invisible electronic devices that are embedded into an environment to sense the needs of the user/surroundings and to automatically provide assistance [12]. In theory, AmI sees electronics integrated into every physical object [13]. Remagnino defines the 3 aspects of an AmI system as sensing, understanding and execution. So to qualify as ambient intelligent, he says a system must be user-centric, should autonomously interpret the intentions and wishes of the user, and should actuate some response to facilitate these intentions and wishes [12]. Furthermore, all this must be done ubiquitously, anthropomorphically, pervasively, non-intrusively and transparently [12]. By doing so, AmI alleviates the weight of the well-documented information overload phenomenon [14] by moving possibly useful information to the periphery of the senses – ready-to-hand if needed – but not crowding the centre of attention [9]. To quote Weiser: “The most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” As an example, he gives writing as the original information technology which is now taken for granted, put in the toolbox of humanity and used to move on to further advances. Similarly, AmI has far-reaching implications in a broad array of application areas, and will permeate all areas of society [9] [15]. Within the proposed system, the human actor is the primary focus, which ties in perfectly with the concept of AmI. As mentioned above Remagnino states there are three aspects to an AmI system: sensing, understanding and execution. When we divide the system into these three categories we get the following:
•
• •
Sensing: AmI infrastructure or the combination of both Pervasive and Ubiquitous Computing system components. This consists of sensors attached to the three primary actors (employees, stock and machinery) and the communication of the retrieved data to the understanding component via both wired and wireless networking. Understanding: Semantic Web technologies (discussed in detail in the following section) manage, interpret and reason over all data returned from the sensing component. Execution: Within SAGE a wide variety of execution options are available. For instance relevant information can be supplied to the user at the right place, at the right time and in the right way to allow them to make quick, informed decisions. In terms of an automated response, materials can be re-routed in a more effective manner, employees can be assigned new tasks and maintenance can be scheduled for a particular time on a particular machine.
Further details on the three categories, within SAGE are contained within the System Description section. The system uses agent technology to enable Ambient Intelligence for the manufacturing shop floor:
Smart Tags & Agent Technology Smart tags can be used to tag the key items in a factory environment e.g. employees, stock and machinery. The smart tags' sensors can monitor the status of their host and environment. Agent software can periodically gather the input from the smart tags and infer contextual information about the state of the factory floor, sub areas of the floor, works in progress etc. Agent programs specialise from standard programs in that they are daemons (i.e. they run indefinitely in the background, not just to complete a task and return some output before exiting), they are goal-oriented as opposed to task-oriented (i.e. the user specifies a goal or desired world-state rather than a list of tasks to be performed), they have freedom within constraints on how to satisfy that goal, generally they are intelligent and can learn, and they can work independent from user input. This means that agents could work all day on the factory floor, meeting goals that they may have detected from the users' behaviour. This is core to the philosophy of Ambient Intelligence.
4
Semantic Web
Semantic Web is the vision of the original creator of the World Wide Web, Tim Berners-Lee. The purpose of semantic web is to give machines the ability to understand the context to web content. At present the content of pages on the internet is primarily written in HTML which is used to describe the layout of the webpage from a visual perspective. The internet is awash with information and it is becoming increasingly difficult to locate a particular piece of data. Paul Krill talks about information overload [16] which is as a result of a massive increase in the amount of information available to us the Semantic Web can solve this problem for us. Fensel et al believe that Semantic Web-enabled services (Semantic Web Services or SWS) will “transform the web from a collection of information into a distributed computational device” [17]. They introduce the Web Service Modelling Framework, which promises to enable SWS through both the description of the services a node can provide and also the mediation necessary to build more specific services on-the-fly from distributed service nodes [17]. This would allow individual devices to communicate and contribute their capabilities to a distributed system to accomplish a desired goal. The Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries, is a collaborative effort led by the World Wide Web Consortium (W3C) with participation from a large number of researchers and industrial partners. It is based on the Resource Description Framework (RDF), which integrates a variety of applications using XML for syntax and URIs for naming. In February 2004, RDF and OWL (Web Ontology Language) were released as recommendations by W3C. A strong improvement is needed in lowering the cost of data integration between heterogeneous systems in SMEs in order to foster systemic innovation. Semantic Web will play an important role in the field of Enterprise Information Integration. To use this technology to support systemic innovation, user-friendly and accessible Web application are needed to let those who are not experts in the
field of Semantic Webs become fluent in their use it and collaborate by providing metadata and information. The ability of Semantic Web technologies to find, understand and manage data means that it has many applications beyond the WWW. Of particular interest is the use of Semantic Web technologies within the concept of AmI and ubiquitous computing (UbiComp). At present the Internet is the only true ubiquitous computing system in the world today, and the arrival of the Semantic Web will help electronics to disappear by providing large-scale distributed computing to enable a ubiquitous device communication infrastructure [15]. At present the technology isn't currently available to create an environment with complete Ambient Intelligence, however many of the gaps resulting from the technological inadequacies can be rectified through the use of Semantic Web. For instance Korpipää et al have designed a context ontology that provides a semantic interface to their sensors on mobile devices. Chen [18] developed a semantic context broker (CoBrA) for pervasive computing environments in order to overcome technological shortcomings of the operational environment. Due to versatility of the Semantic Web it is not surprising that there is a substantial body of research that applies it to the manufacturing environment. The Manufacturing Engineering Laboratory of the National Institute of Standards and Technology (NIST) is developing a semantic system which tests manufacturing operations for standards compliance, internally and externally interoperability and future semantic technology integration requirements. Cecil [19] uses the semantic web as an alternative approach for distributed manufacturing. However the key difference between the above manufacturing applications and this one is their key focus. Their focus is not on the physical manufacturing of products; rather it is on the other aspects involved in the running of SMEs. By focusing on the factory floor we have an environment where an AmI infrastructure can be combined with Semantic Web technologies. Combining Semantic Web and AmI in order to provide users with invaluable information about the manufacturing environment is a novel approach. The system is focused on the human actor within the factory; however the other actors such as machinery and stock are not neglected. As Heckmann [20] points out Semantic Web technologies are ideally suited to the role of mediator between the various independent system components, this is precisely the manner in which it is being used within this system. Several Semantic Web technologies are used in the system:
Resource Description Framework and the Web Ontology Language RDF is a storage model used in the Semantic Web. It is commonly implemented using the eXtensible Markup Language (XML) and the language is referred to as RDF/XML. RDF stores data in a graph of concepts and relationships. It adds meaning to data, relating concept to concept in the form of statements consisting of subject, predicate and object. These are similar to simple natural language sentences and are commonly referred to as triples, having the concepts as the subject and object while the relationship between them is the predicate. The value added by RDF is that it allows the relationship between objects to be explicated which allows understanding to be conveyed in a standard way. OWL is built on top of RDF and adds a powerful data classification model which facilitates reasoning. OWL classifies data in graphs called ontologies. The ontology gives a descriptive class to each subject, predicate and object which allows reasoning programs, such as Racer Pro, to infer further triple statements from those asserted. For example, if we use an OWL ontology to assert the triples “sheep eat grass”, “vegetarians eat plants” and “grass is_a plant”, then a reasoning program can infer the further fact that “sheep is_a vegetarian”. This is a simple example, but more complex ontologies involving many more concepts and relationships, i.e. triples, can leverage the processing power of machines to automatically infer understanding from information detected from the environment. Furthermore, this understanding can be shared with other machines due to the interoperable nature of RDF [21].
5
System Description
The SAGE (Semantic Agent-based Generic Extensible) framework is an extensible, agent-based framework for manufacturing applications. At the centre of SAGE are two indispensable components, the Mediator and the RDF store. Connected to the Mediator is any number of agents with any number of functions. This section describes in detail the Mediator and the RDF Store along with the primary agents being used for the manufacturing application.
5.1
Mediator and RDF Store
The key to the extensibility of SAGE is the Mediator. The functions which the mediator performs are relatively straightforward, generic functions that are related to the data stored in the RDF Store, none of which are specific to a particular agent. Any specialised functionality is provided by the agents that communicate with the Mediator. The purpose of this is to keep the mediator as independent as possible from the application SAGE is being used for at that particular time. This functional simplicity allows a limitless variety of agents to interact with it and hence be included in SAGE. If new functionality is required for a specific implementation, specialised agents and any additional ontologies to hold information of interest can be added to SAGE. The Mediator is the only element in SAGE that reads and writes to the RDF store. Agents pass their information for storage to the Mediator which can check the data, add it to the store, and perform any updates on the store as necessary. As triples are added to the store, they form an incomplete ontology. The Mediator decides when to initiate reasoning over the information to infer further facts about the environment from those asserted by the agents (see section on Semantic Web for example). A reasoning program such as Racer Pro runs through the ontology and completes it. Immediately after reasoning, the ontology stored in the RDF store is complete and the system has a full understanding of the environment at that moment. If agents wish to be notified of changes to certain triples in the store, they can register themselves as interested listeners on those triples with the Mediator. When a change occurs, an event is triggered and the Mediator notifies the agents of the change, at which point they can take action. The Mediator responsibilities: • Read/write triples from/to the RDF store • Initiate reasoning over the ontology in the RDF store • Notify interested agents of relevant changes to data in RDF store
Yet Another RDF Store The RDF Store that the Mediator is built on is Yet Another RDF Store (YARS). YARS is a lightweight, open-source, Java-based RDF Store with a small footprint that can be embedded into an application and adapted to meet special application needs. Its novel indexing structure allows it to outperform several of its peers: it is 4 to 7 times faster than similar Sesame implementations and up to 400 times faster than the Redland database. YARS stores RDF in the form of triples in N3 notation as opposed to RDF/XML notation, and it also stores the origins of the triples [22]. This can be used to track from which agent a particular triple of interest came from. Consequently this adds a 4th dimension to the querying that can be performed on the RDF store: it is possible to search through the information by contributing agent. Therefore while the information from all agents is massed together into the one store to help with the generation of context, it is still possible to trace where that information came from.
Figure 5.1 SAGE Framework generic core components and extensible agents
5.2
Sensor Agent
The sensor agent is the interface between the real world and the systems understanding of the real world. Each one is highly specialised to understand and communicate with a specific type of sensor or group of sensors. They are responsible for taking readings or measurements from the hardware sensors periodically and giving meaning to the data. They do this by translating the readings into meaningful semantic statements in the form of triples. For example, a sensor agent which is listening to sensors on Machine B can decide to check the sensor readings, which it knows to be temperature readings in degrees Celsius, and from the returned value of “52” construct the triple “MachineB-hasTemperature-52°C”. The sensor agent then passes the new triple to the Mediator for storage in the RDF Store and consequent usage by all agents. The sensor agent can also make decisions based on knowledge stored in the RDF Store. It first of all must register itself as a listener with the Mediator, describing which triples it is interested in. Then changes to those triples will trigger an event to notify the sensor agent. For example, it can decide that if there is a high variance between subsequent sensor readings to increase the frequency of readings for a short burst to capture what is happening. This can be used to detect irregular activity in the environment or to detect malfunction in other sensors or agents that took the readings. Sensor agent responsibilities: • Take sensor readings periodically • Translate into triples • Increase/decrease frequency of readings
5.3
Context Agent
The context agent uses the sensor data provided by the sensor agent; however it is important to point out that both of these agents are independent of each other. The context agent is not aware of how the sensor data is placed in the RDF store; it is simply informed by the Mediator every time new data is available. The context agent uses both current sensor data and old context data in order to reason the current state of the object in question. Its first step is to take relevant information about the previous context of resources on the shop floor, along with their most recent readings, from the RDF Store. Then it calculates a set of statistics about the previous and current states of the resources in question. From these statistics the context agent can compute the probability of different context states. Based on previously held threshold values for probability, the context agent then decides the context of the resources. Over time these threshold values can change, providing a capacity to learn to the context agent. It is important to note that the context agent is a step above simply assigning context to resources based on their immediate sensor readings. It has access to information from the RDF Store on employees, scheduling, machines and stock etc. that allow it to make truly informed decisions taking into account the past, present and desired future. It can take into account such things as how long an employee must be present at a workstation and what business would they have at that particular workstation before deciding that that employee is in fact working at that workstation. The ability of the context agent to learn over time also gives it the ability to perform in a dynamically changing workplace that in practice does not conform to a theoretical or ideal model such as scheduling. Context agent responsibilities: • Calculate statistics • Compute probabilities • Decide context • Learn over time
5.4
Shop-floor Control Agent
The Shop-floor Control Agent uses information from various sources to control the operation of the factory floor. It primarily relies on information from the Context Agent to keep it up to date with the real-time state of the entire factory floor. Using this information and information from the employee roster, order book, stock room and others it controls how orders are routed through the factory and it also controls what workstations employees are working at.
5.5
Maintenance Agent
The purpose of the Maintenance Agent is to monitor and control all maintenance needed on the factory floor. It schedules what maintenance is needed where, according to the current maintenance strategy in operation, and when is the most appropriate for this maintenance to occur. It also monitors the effectiveness of the maintenance strategy in operation and it can compare it to other maintenance strategies and if necessary make recommendations to management.
6
Scenarios
This section outlines a number of scenarios to illustrate how SAGE works. The first set of scenarios, Company A, illustrates how each of the agents are used and also how they interact with the mediator. The other two scenarios, Companies B and C, demonstrate how flexible the framework is and also introduce two new agents in order to demonstrate how new agents can be created which easily slot into SAGE without having to modify any of the existing components.
6.1
Company A
Company A is large SME with 200 employees; it manufactures a large range of telecommunications products. The telecommunication market is becoming increasingly difficult to operate in due to the large number of Asian companies entering the market offering cheaper products. Company A has realised that they cannot compete directly with the Asian companies, therefore they are focusing on the markets which require extremely high quality products thus giving them an advantage over the competition from the East due to their highly skilled work force. Company A is interested in implementing a system which would help them keep their costs to a minimum by insuring their assets (employees, stock and machinery) are used as efficiently as possible while not compromising the quality of the product.
Shop-floor Control Scenario Ed, a wire crimper, arrives at his workstation (Workstation19) in the morning and there is no work waiting there for him to complete. The sensor agent, by monitoring the smart-tag on Ed’s ID, is automatically updating Ed’s location and this data is being inserted into the RDF store. The context agent notices that Ed is at his workstation and that there is nothing at for him to work on, Ed’s context in the RDF Store is updated to reflect this. Since Ed’s context is in the RDF Store other agents can access it, in this scenario the Shop-floor control agent has registered an interest in the context of all factory floor workers and therefore is sent Ed’s context. The Shop-floor control agent then tells the person in charge of cutting the wires (the step before crimping) that Ed needs a batch of wires to work on. The batch of wires (Batch22) also has a smart-tag attached to it so when it arrives at Workstation19 the sensor agent updates the location of that batch in the RDF Store. Once the context agent realises that the batch of wires has arrived, it can update Ed’s context from idle to working on Batch22. Once Ed has finished crimping Batch22 and it has moved onto the next phase of the processing Ed’s context is returned to idle and the cycle starts all over again.
Complex Context Scenario Ed is working on Batch22 at Workstation19 and at some point during the processing of the batch he decides to go to the toilet. As soon as Ed’s ID tag goes out of range of Workstation19 it is obvious that he is no longer working on the batch and his context is changed to “In Building”, this tells us that Ed is no longer at a workstation but has not left the building. Upon leaving the toilet Ed decides to ask the manager a question and on his way to talk with him Ed passes by Workstation19 and for a moment the Sensor Agent updates his location to be at Workstation19. This information is then supplied to the Context Agent but it does not automatically update Ed’s context to be “Working on Batch22 at Workstation19” because it realises that Ed may just be passing through. Therefore the Context Agent waits for further location readings consistent with the first one before making an adjustment. Because the following readings state that Ed is not at Workstation19 the Context Agent keeps Ed’s context listed as “In Building”. When Ed is talking to the manager he sits down on a set at Workstation43 beside the manager. Workstation43 is used for putting power supplies into PCB’s, something which Ed isn’t trained to do. The Sensor Agent lists Ed’s location as Workstation43 and it has also listed that Batch7 is waiting at that workstation to be processed. However the Context Agent knows better than to list Ed as working on Batch7 at Workstaton43 because Ed isn’t trained to fit power supplies and also he is scheduled to be working at Workstation19 all day today.
Machine Maintenance Scenario The factory is currently using a Total Productive Maintenance (TPM) strategy which is being autonomously controlled by the Machine Maintenance Agent. The machines at Ed’s workstation are due to be serviced after lunch and this information is included in the RDF Store by the Machine Maintenance Agent so that other agents can access it. This particular piece of information is of use to the Shop-floor
Agent because it will affect the workstations which are available to it. Therefore once Ed is finished with Batch22 the Shop-floor Agent instructs Batch27 to be sent to Ed because it is a smaller batch and he will have it finished by lunch time. Once lunch time is over the Shop-floor control agent reassigns Ed to another workstation in order to free up Workstation19 so that its machinery can be serviced. Once Ed goes to his new workstation the Context Agent, using information provided by the Sensor Agent, will change the context of both Workstation19 and Ed. Workstation19 will be idle and Ed will be working at a different workstation. When the Machine Maintenance Agent notices that Workstation19 is idle it can initiate the appropriate maintenance for it. Once the maintenance is complete the Machine Maintenance Agent updates the RDF Store to reflect this and the Shop-floor Agent then assigns an employee and some work to Workstation19.
6.2
Company B
Company B is a small SME with 15 employees. They manufacture, repair and refurbish fire engines. They are interested in implementing an automated stock control system. At present they don’t have any type stock control system in place. Company B are not interested in machine maintenance strategies or automated shop floor control, therefore only a subset of SAGE will be implemented.
Stock Control Scenario #1 Like most of the employees at Company B, Chris works on all aspects of their operation, he also has the added responsibility of being in charge of the stock. His fellow employees simply take parts from the stock room as they need them and don’t keep track of what they take or leave back. This has always been the case, however now thanks to SAGE every time they remove or return items it is autonomously recorded. James removes a pump, with a smart tag connected to it, from the stock room to be used on a fire truck assembly. The only area with an ambient infrastructure is the stock room, therefore once the pump leaves the stock room the Context Agent, using information from the Sensor Agent, updates its context to being outside of the stock room. At this point the Stock Control Agent realises that there are now only two pumps left in stock and over the next two weeks they will need three pumps for the orders which are due to be completed. Chris has configured the Stock Control Agent to automatically place the orders rather than sending a message to Chris telling him to manually order the pumps. Once the agent has completed the order it sends a message to Chris stating this.
Stock Control Scenario #2 John and Chris are working a fire truck that needs to be refurbished. Chris has already gotten the new ladder from the stock room but John doesn’t know this and goes to get another ladder for the fire truck. The Stock Control Agent notices that another ladder has been taken from the stock room, there are now no ladders left and a ladder will be required for an order which is due to be completed next week. The Stock Control Agent does not automatically order a new ladder because using the information on the orders that are currently being processed and the number of ladders that have been taken from the stock room that there is one ladder too many on the factory floor. The agent keeps the number of ladders in stock at one but it changes its stated location from the stock room to the factory floor.
6.3
Company C
The regulations governing the medical any products involved in the medical industry are extremely stringent and companies must adhere to strict standards such as the ISO standards. Company C is one such company and in order to achieve the new standards they must have complete traceability and accountability over the entire manufacturing process. They are also interested in getting information on the efficiencies within their factory. Traceability: Orders go through several phases as they progress from when the order is placed to the finished product. In order to maintain 100% traceability it is necessary to know what components went into the product, when and from where those components were supplied, who worked on the product at each stage of its production and who performed the final inspection. Accountability: Full accountability allows the company to know which employee performed a particular task. This allows important information to be gathered such as recognizing individuals with high error rates, most common overall errors, and it allows the company to take measures such as increased employee training to decrease the overall error level and hence increase the overall productivity. Efficiency: Having a system which automatically updates traceability information in real-time also allows the company to monitor Works In Progress (WIPs) and efficiency at the same time by using the information provided from the traceability application. Information about employees, stock usage and the assembly line are of interest to efficiency which can be inferred from the data acquired by the system. In order for SAGE to meet all the requirements of Company C three agents are required, Sensor Agent, Context Agent and TAE (Traceability, Accountability and Efficiency) Agent. The TAE agent takes the context information, interprets it and displays the results for the user.
Traceability, Accountability and Efficiency Scenario There are four people working on the assembly of a power supply which is designed for use with a Computerized Tomography (CT) scanning machine. The assembly is broken into three subassemblies, each of which has one person working on it and the final person works on combining the three individual subassemblies into the final power supply cabinet. Dennis, Andy and Nigel are trained in each of the subassemblies so the particular subassembly they are working on changes frequently. Dennis is working at Workstation1 working on subassembly A, Andy is at Workstation2 working on subassembly B and Nigel is at Workstation3 working on subassembly C. The Sensor Agent and Context Agent work as normal updating which worker is working on what subassembly at what workstation. The TAE agent uses this realtime supply of information and combines it with older to supply the user with information on Traceability, Accountability and Efficiency. TAE has noticed that having Dennis working on subassembly A is not as efficient as when he was working on subassembly B, therefore using this information the user can take effective action. Dennis can be changed back to subassembly B and he can also be scheduled for extra training on subassembly A. One of Company C’s CT scan power supplies has failed in the local hospital and because of that the hospitals only CT scanner is no longer operational. Company C replaces the power supply as quickly as possible but the failure has resulted in two full days worth of scans having to be rescheduled. This failure could have drastic implications for future hospital contracts which Company C tender for. The broken power supply is inspected and the fault is located. Because of the 100% traceability supplied by the SAGE’s TAE Agent, Company C can find out who worked on the faulty subassembly, what supplier the faulty component was bought from, then the subassembly was completed and what maintenance has been done on the power supply. Using this information they can take appropriate action to insure that such a failure never occurs again and hence make assurances to the hospital that the failure was a once off event.
7
Conclusions
The capturing and understanding of relevant data from the manufacturing shop floor and the consequent decision-making of a plant manager are very important for small-to-medium sized enterprises to remain agile and flexible in a turbulent market. Deterministic and predictive solutions have drawbacks in that the theories they adhere to in practice do not cope well with the unpredictable nature of market demand and the fast-changing parameters of the factory environment. Our approach overcomes this problem by monitoring the key resources of the shop floor – employees, stock and machinery – in real-time using an ambient intelligent framework. The agent-based framework can determine context, detect events, actuate responses, and present relevant reports to the user to enable prompt action to be taken. Our agent framework is generic, modular and extensible to suit the needs of the enterprise.
Acknowledgements This work is supported by Science Foundation Ireland (SFI) under the DERI-Lion project (SFI/02/CE1/l131) and by the European Union’s IST programme under the AmI4SME project (FP6017120), and the Irish Research Council for Science Engineering and Technology.
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