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Human Factors and Ergonomics in Manufacturing, Vol. 10 (4) 431–452 (2000) © 2000 John Wiley & Sons, Inc.

Establishing Information Requirements for Supervisory Controllers in a Flexible Manufacturing System Using GTA John M. Usher and David B. Kaber Department of Industrial Engineering, Mississippi State University 125 McCain Building, Mississippi State, MS 39762–9542

ABSTRACT In this article we consider the technological change that has occurred in complex manufacturing systems within the past two decades and the implications it has had on the role of human operators in manufacturing systems control. Our examination ranges from the traditional production line manned by skilled machinists to flexible manufacturing systems (FMS) under supervisory control. On the basis of this study, we raise the question as to whether new advanced manufacturing technology interfaces are supportive of human operators in their responsibilities to manufacturing systems. We address this problem by analyzing supervisory controller information requirements for intervening in complex process control tasks as part of FMS operation. This analysis was conducted using a cognitive engineering research methodology, which has not previously been applied, in the domain of manufacturing. The method of GTA was applied to supervisory control of an FMS and produced detailed information requirements, which facilitated the formulation of general design guidelines for FMS interface design. The guidelines are aimed at supporting human operator process strategy development and decision making. © 2000 John Wiley & Sons, Inc.

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INTRODUCTION

With the advent of advanced manufacturing technologies (AMTs) such as computernumerical controlled (CNC) machining centers, and their incorporation in flexible manufacturing cells (FMCs), the role of human operators in manufacturing processes has changed significantly. This role has shifted from requiring human direct, manual control of single machine operations, to one in which operators primarily monitor and supervise automated control of machine cells performing the very tasks they once owned (Hwang, Barfield, Chang, & Salvendy, 1984; Szelke & Markus, 1993). As part of the development of AMTs, control systems have necessarily increased in complexity, being transformed from manual controls, transparent in the machine functions they influence, to automated controls for impacting cost efficiency and accuracy. This change has been a critical determinant in the manufacturing responsibilities of the human and the time frame within which these responsibilities can be met. Operators have been shifted from maintaining the functions of determining independent machine parameter settings (e.g., tool speed and feed rate), monitoring these parameters, and making near-term process decisions and taking action, to monitoring integrated machine response measures (e.g., metal removal rate, machining stability) and extended-term operations planning. Unfortunately, it is speculated that many current automated systems are not supportive of human 431

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operators in their new responsibilities through system interfaces (Martensson, 1996). Yet, in spite of this, the initiative to further automate complex manufacturing systems continues to press on with the approach of a new century, often going unchecked with respect to the impact on the human. Automation in manufacturing systems has been motivated by perceived needs of humanmachine system designers to off-load as much task responsibility as possible from human operators in order to reduce workload and to minimize human inputs into system performance to prevent potential errors. (Throughout the history of human-machine systems [e.g., aircraft] operation, operators have developed the reputation of being the harbingers of errors often leading to catastrophic accidents [Heinrich, Petersen, & Ross, 1980; Norman, 1986]. This may or may not actually be the case, as the design of systems permitting human control has more recently been emphasized as the root of errors with the ownership for accidents being placed on the system designer [Norman, 1986; Wickens, 1992].) One notion is that system performance may be improved by allocating to a computer functions that humans can accomplish but that cause high workload (Wickens, 1992, p. 531). Others have offered that automation can be used to reduce task workload by allocating to a computer routine and redundant tasks during complex human-machine system operations (Sheridan, 1993); however, this recommendation has often been applied across the spectrum of manufacturing operations. Increased automation in manufacturing systems design has also been pursued due to management interest in reducing crew sizes and, consequently, operational costs. This has been the case in the aviation industry as well where “autonavigators” and like forms of cockpit automation have been explored to reduce flight crew sizes, and ultimately reduce crew costs (Wiener, 1988). These issues have not only motivated manufacturing automation, but the implementation of technology in the design of other complex systems involving some degree of human operator system control, including aircraft, air traffic management systems, and process control systems. Since the initiative toward greater and greater machine intelligence is persistent, it becomes more incumbent upon manufacturing and human factors engineers to ensure a prudent approach to automation is taken to optimize human functioning in interacting with systems in which errors and failures can be catastrophic. This may best occur by developing a thorough understanding of manufacturing technology, the roles human operators and automated controllers are to play in system performance, the information needs of operators, and effective means by which to structure human-machine interactions to promote performance. Methods to accomplishing this may include conducting technological inventories, conducting expert knowledge elicitation (e.g., interviews, verbal protocols), and engineering (see Klein, 1998), as well as conducting hierarchical or goal-oriented task analyses (see Dix, Finlay, Abowd, & Bealle, 1998, pp. 262–268; Endsley, 1993).

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CHANGE IN MANUFACTURING TECHNOLOGY

The rapid pace of technological change has had a profound affect on manufacturing over the last several decades. Since the development of the numerically controlled (NC) machining center in the early 1950s, manufacturing systems have incorporated increasingly advanced forms of automation (e.g., industrial robots and automated guided vehicles [AGVs]) for improved system efficiency and productivity (Stahre, 1996). In a range of applications, manual operation and control of machine tools has been replaced with au-

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tomation of varying degrees. This involvement of automation has spanned from performance of a single operational task to complete control of an entire production process for a variety of products. Benefits in manufacturing system economic profitability have been realized from these changes; however, this has not occurred without the costs of increased system complexity (from an operator’s perspective) and reliance on a system’s ability to reason and adapt to the process disturbances that arise. The flexible manufacturing system (FMS) has represented the pinnacle of humanautomation integration in the manufacturing industry for the last 2 decades. The technologies that comprise an FMS include, but are not limited to, NC or CNC machining centers, robotics, conveyors, AGVs, computers, and programmable logic controllers. The structure of a typical FMS is shown in Figure 1, and it can include various combinations of these technologies. Specifically, an FMS can be defined to consist of several semiindependent NC machines linked together by a common transport system under the guidance of a single control system. The nature of the system is such that material flow is asynchronous and equipment may be accessed in any order dictated by the control system (Rembold, Nnaji, and Storr, 1993). Given that an FMS aims to produce a variety of parts at low to medium volumes, one might compare its operation to that of traditional batch systems, and possibly some job-shops. Compared to an FMS, such traditional systems are comprised of a number of nonintegrated NC machines being operated by skilled machinists. In a job-shop, control of production flow may resemble an FMS in the asynchronous appearance of material movement through the system. However, as batch sizes increase, the movement of material through the shop begins to take on a more synchronous flow. Whereas a typical production line may be able to achieve higher production rates than an

Figure 1

Structure of a typical FMS.

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FMS, the FMS can more economically produce a wider variety of parts at medium volumes and in much smaller batch sizes (Maleki, 1991; Stahre & Johansson, 1996). From a control perspective, an FMS can be represented using the hierarchy shown in Figure 1 (Maleki, 1991). The manufacturing equipment on the shop floor is grouped into manufacturing cells with a programmable controller (most likely) providing direct monitoring, coordination, and control of activities that take place on the equipment within a cell. Then, at the next level, it is the supervisory controller that monitors, coordinates, and controls the activities of the cells and the material handling equipment that transports parts and tooling between cells. The human-machine interfaces used in supervisory control of the FMS present summarized machining cell and job status reports and relate this information to accomplishment of overall production goals. Control of a traditional production line, unlike FMS control, is decentralized across workstations arranged according to product flow or machine type. Human operators serve as monitors of basic automated functions that occur based on mechanical programming carried out in advance of production and aimed at achieving operator goals. Machinists rely on NC displays for system status and develop near-term production goals by perceiving inventory surpluses and shortages along a line. 3. CHANGE IN THE ROLE OF HUMAN OPERATORS IN MANUFACTURING SYSTEMS 3.1. Manual Control and Supervisory Control Along with changes in AMTs have come changes in the role of the human operator in systems control. Historically, manual control activities in manufacturing have involved a single operator responsible for the direct operation of one or two machines with the influence of their production decisions covering a short-term period. Operators in this situation were concerned with producing parts of a job fed to them from one or more upstream workstations. Based on predefined process plans passed to the operators, they would plan the exact steps necessary to perform the operations associated with the job in their possession, often modifying the process plan to cater to the specific characteristics of their machine. Once the plan was determined, the operator was directly responsible for carrying out the steps prescribed by their plan (including process setup) and monitoring the success of each task within the plan. During execution, the detection of any problems resulted in the operator directly intervening with a revision to the plan to alleviate the problem. Over time, the operator would learn more about the operation and control of the machine through his or her experiences. The introduction of AMTs into manufacturing systems has reduced process reliance on human input and operators have moved from working as machine tenders providing direct manual control of the machine and its tooling, to supervisory controllers of larger systems such as FMCs and FMSs (Stahre, 1996). The roles of the human operator in a supervisory controller position have been identified to include planning, programming, monitoring, intervening, and learning (Sheridan, 1992). Even though the labeling of these roles is similar to those identified above for the operator involved in direct control, it is the definition and implementation of them that changes when performed in a supervisory position. Now operators are involved in long-term operations planning for multiple product types to ensure overall demand is met, and instead of the operator having direct interaction with the machine in terms of commands and responses, such interactions are


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modulated and filtered by electromechanical interfaces as part of the process automation. Depending on the level of system autonomy, these interfaces or computer systems may maintain varying degrees of responsibility for, or complete ownership of, the various functions identified as part of supervisory control. Therefore, a result of the implementation of automation has been the removal of some of the operational responsibilities originally placed on the operator. This has contributed to the progressive deskilling of machinists and machine operators over time in terms of direct control capabilities. However, it has also attenuated operator physical workload and permitted operators to dedicate time to performing additional higher cognitive functions associated with many manufacturing resources including planning and process decision making. In fact, due to the use of automation, there is less of a need for the operator to be involved in manually intensive tasks associated with one machine, reducing physical workload and the potential for fatigue. This allows the opportunity for system designers to place operators in more supervisory roles where they can make use of cognitive abilities for performing tasks that are difficult or currently impossible to replicate using artificial intelligence. In particular, as industry strives to create more autonomous systems, the fact that artificial intelligence is currently unable to handle all possible anomalies within a system means that humans will be retained in the control loops to handle such events (Usher, 1999; Wright and Bourne, 1988). Although the opportunity exists for FMS system designers to utilize operators as cognitive resources for high-level planning and processing functions, this is still a developing trend. Unfortunately, the predominant approach to AMT development has been to automate what is possible and leave the remainder of operational responsibility to the human, as compared to considering the cognitive capabilities of the operator in the job design process and allocating performance functions appropriately. 3.2. The Relationship between Allocation of Roles in Manufacturing and Information Requirements Given these changes in the manufacturing operator’s role from traditional production machinist and shop-floor supervisor to FMS supervisory controller, the information requirements for decision making critical to the attainment of manufacturing goals have changed as well. Here we make comparison of the basic information requirements for the historical roles of machinist and shop-floor supervisor, involving direct interaction with machine systems, to the contemporary role of supervisory controller using manufacturing system user and process interfaces (Stahre, 1996). The FMS supervisory controller may in fact carry the responsibilities of multiple personnel from the traditional production line operation including process planning and process intervention. This makes sense in that through the aid of automation, the human is capable of handling a greater task load. Unlike the role of machine tender, supervisory control also involves larger planning and process intervention components, as mentioned in the previous section. Shop-floor supervisors have historically maintained these activities. For example, the machine tender often has the necessary experience to identify an impending system failure or the need for maintenance. Identification of such conditions would usually prompt the operator to request assistance from the shop-floor supervisor to determine what changes need to be made in the production schedule given the need for maintenance and so forth. Therefore, the machine tender handles monitoring and error diagnosis, and the floor supervisor performs needed on-line planning. Subsequently, the operator executes process intervention according to the supervisor’s plan. The super-

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visory controller of an FMS is often expected not only to monitor multiple machining centers and conveyance systems for impending failures or maintenance needs, but also to intervene in the process, making necessary modifications to the system based on appropriately updated production schedules. This may bode of an idealized form of supervisory control; however, all the general functions of supervisory control may be required in FMS operation, including planning, teaching, monitoring, intervening, and learning, and operators may choose among functions and prioritize satisfying functional responsibilities based on task requirements. Further, different supervisory control functions may be combined differently, or they be performed simultaneously by a single operator across multiple systems (Sheridan, 1992). Comparing an FMS operator’s supervisory control role to direct operation and control of one or two machines on a traditional production line, the significant increase in the breadth of the FMS operator’s responsibilities to the system is probably the most basic explanation for the change in operator information requirements with time. The supervisory controller may not only require detailed information on the status of machines within a single manufacturing cell to determine the need for potential process interventions, but overall production schedule data, as well, to establish whether job order demands placed on the entire FMS are met. Further, the information requirements of a supervisory controller are usually for data supportive of long-term planning, which can be pursued in systems capable of greater production control such as FMSs. The information requirements for the machine tender on the automated assembly line are limited to knowledge of the status of their machine and the current job demands as well as demands posed by workstations immediately upstream. The traditional production line supervisor’s information requirements for planning differ from those of the supervisory controller in performing the same functions. The line supervisor usually requires only data that are supportive of short-term planning, for example, daily production scheduling on a cell-by-cell basis. The line supervisor does not require the overall scheduling “picture” of a supervisory controller in an FMS. These generalizations on the types of information used by different manufacturing workers in different system contexts are intended to demonstrate how the functions of operators, dependent upon system structure and technology, influence their information needs for performance. They are not intended to represent the composite of detailed information requirements of, for example, a supervisory controller of an FMS. However, the next section seeks to address this challenge within a limited scope.

4. GTA FOR DETERMINING MANUFACTURING OPERATOR INFORMATION REQUIREMENTS Methods have been developed through cognitive engineering research for the purposes of identifying detailed information requirements of operators of complex systems to support interface design and performance of general functions and tasks. GTA is an information requirement assessment methodology, which was developed by Endsley (1993). In general, the method involves: (a) identifying the major goals of operators in complex system operations depending upon the roles assigned to them, such as resolving capacity bottlenecks and addressing “hot” orders as part of process intervention in supervisory control of an advanced manufacturing system; (b) identifying subgoals that are supportive of the overall goal; (c) identifying specific operational tasks to achieve subgoals; (d) creating


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critical questions aimed at addressing decision making in task performance; and (e) developing operator information requirements to answer these questions. Endsley (1993) conducted a study of information requirements for air-to-air combat fighters using goal-directed task analysis (GTA). She developed pilot situation awareness requirements in combative engagements by: identifying tactical goals such as, “kill an enemy aircraft”; formulating supportive subgoals, including, “engage enemy aircraft”; identifying subobjectives like, “employ weapons”; and, on this basis, specifying information requirements for achieving the subobjective, including, “weapon activation/lockon,” “time to impact,” and “kill assessment.” This application of GTA provided descriptive information on pilot activities and needs in a combat situation and is supportive of, for example, cockpit display design to facilitate effective decision making under time pressure. Since critical decision making and time pressure are intrinsic to complex manufacturing, GTA may also have utility to this domain like aircraft piloting. Endsley and Rodgers (1994) presented a GTA on en route air traffic control (ATC), revealing an overarching goal of assuring flight safety. They identified subgoals such as avoiding (aircraft flight) conflicts along with specific tasks to be accomplished in achieving subgoals (e.g., assessing aircraft separation). They also identified specific questions to be answered by a controller in attempting to meet a goal and the operator information requirements to address these questions. For example, in ensuring aircraft separation, a controller must answer the question Does the vertical separation of two aircraft meet or exceed the federal limits? This requires a controller to perform the tasks of determining the current and projected vertical distance between aircraft along a route. In order to accomplish these tasks, the controller needs to know what the altitude is of each aircraft, the accuracy of the altitude information, the altitude rate of change (i.e., climbing/ descending), and so forth (Endsley & Rodgers, 1994). It is important to note that in both of these analyses, operator information requirements were established absent of consideration of the characteristics of the machine technology the human is to control or interface with. This is critical because the analysis would otherwise be influenced by limitations of the technology (i.e., its capability to transmit information to an operator or to receive human control inputs). Consequently, the results of the analysis would be limited to a specific system. Information requirement assessment methodologies similar to GTA have been developed by manufacturing researchers and applied to, for example, the job content of FMSs (Martensson, 1996), as well as the functions of a supervisory controller in FMS operation including monitoring and intervention (Szelke and Markus, 1993). These methods are presented in the following section. 4.1. Identification of Information Needs and Requirements in Manufacturing System Control Martensson (1995) developed a procedure for matching human needs in manufacturing with systems configuration to ensure, for example, that machine interfaces are supportive of human information requirements. This method is comparable to GTA. The procedure starts by identifying the goals of the individual and relates them to specific life needs including those associated with work, such as self-actualization and job satisfaction. On this basis, specific work requirements are formulated—such as task content, level of responsibility, information processing requirements, and task difficulty or load—that will allow workers to satisfy their overall work needs. The desired requirements of work can

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then be used to determine the task information needs of operators for effective performance; how information should be structured for accurate operator perception; and decision support systems needed to assist operators toward productivity and safety. In this application, Martensson’s (1996) intent was not to directly determine the information needs of an operator to facilitate human-machine system attainment of performance goals. In this way, the application differs from cognitive engineering uses of GTA like Endsley’s (1993) analysis of fighter piloting. Martensson (1996) related operator life needs to roles assumed in control systems and the impact of operator control on manufacturing system productivity and safety; whereas Endsley (1993) identified specific tactical goals and related them to operator information requirements. Szelke and Markus (1993) applied a method similar to GTA to an adapted form of the classical model of supervisory control put forth by Sheridan (1992). This process was used to develop a cognitive model of supervisory controller functioning facilitating design of a dynamic user interface. The cognitive model was initially used to predict the information requirements of operators, depending upon dynamic system states and their functional responsibilities to the system. Subsequently, the structure of information displays capable of changing to meet operator requirements was predicted. The supervisory control model they studied included the functions of planning, monitoring, intervening, and the additional function of administration. These functions were described in detail in terms of specific activities operators might perform toward their fulfillment. For example, intervening functions of the human included diagnosing failures, correcting failures, performing exception handling, overriding the system to provide missing data, and offering human guidance in the control process. Using the GTA-like approach, Szelke and Markus (1993) constructed a finite state diagram of specific activities requiring operator intervention in a supervisory control loop including resolving bottlenecks and expediting orders. The authors generated alternative plans by which to address the goals of intervention, and for each plan they determined tasks to be carried out. The tasks were linked to detailed system actions in information presentation during operator intervention. The actions involved updating interfaces to support human task performance. For example, one plan to address resolving capacity bottlenecks required selecting a new route. The task to this plan was to select jobs for processing on the new route and the supporting system actions were to update and display new schedules. These represent manufacturing methods in existence for human operator information requirements analysis. GTA can be considered as another approach to this type of work. The level of information detail realized in the analyses published by Martensson (1996) and Szelke and Markus (1993), in terms of system information presentation requirements, did not approach that demonstrated by Endsley and Rodgers (1994) in their application of GTA. With respect to Szelke and Markus’s (1993) study, this may be due to the fact that their objective was to develop an interface for a specific type of system and not to establish general information requirements for a generic task context. Consequently, the results of Szelke and Markus’s (1993) research may be limited in applicability to the domain of manufacturing systems, as a whole, compared to the usefulness of Endsley and Rodgers’s (1994) results to ATC, in general. GTA presents an alternate approach to defining the information requirements for manufacturing system operators with a high level of detail. However, no formal evaluation comparing the results of the various information requirements analysis methods discussed here has been conducted.


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4.2. An Example Application of GTA to Supervisory Control in Manufacturing In the earlier discussion of applications of information requirement analyses similar to GTA, all results have appeared to be potentially useful for supporting interface design. However, the level of information detail revealed across the manufacturing studies has been limited. There is a need to apply formal GTA to the context of manufacturing to reveal a high level of detail in operator information requirements aimed at facilitating systems interface design and optimizing human-machine interaction. In order to demonstrate the potential effectiveness of GTA for establishing information requirements, here, we present an analysis of the intervention function in supervisory control of an adaptation of a real FMS described by Martensson (1996). Intervening activities of an operator in this context include avoiding bottlenecks, expediting critical orders, and maintaining normal system functioning. Martensson (1996) detailed a medium-sized FMS composed of seven multiaxis machining centers, 16 robots, a coordinate measuring machine, a part-washing station, and several computers. In this system, palletized parts were transported between machines by conveyor. We hypothesized how this system should operate. We assumed that such a system would be managed by a centralized FMS control system, as depicted in Figure 1. This control concept is consistent with the perspective of supervisory control expounded earlier. The control system, or host computer, would be able to process large amounts of information with respect to each part produced. The functions of the human in system control are detailed later. The overall system would be capable of producing any of the different parts from a family of high-tolerance machined aluminum parts. The hypothetical FMS could be considered as a generic system. The overall goal, subgoals, and objectives of FMS production are presented in Figure 2. The goal of an FMS is to meet demand as dictated by job orders released to production. Subgoals to this overall goal include scheduling job production to meet due dates, avoiding FMS workstation capacity bottlenecks, expediting critical job orders, and maintaining the system in a normal mode of operation. The subgoal of manufacturing jobs, for example, involves developing a production schedule, which seeks to optimize such scheduling criteria as tardiness, flow time, and makespan. This subgoal can be accomplished by selecting appropriate production plans that balance the use of machine and shop-floor operator resources. Attainment of this subgoal, and others, requires the supervisory control function of planning (see Sheridan, 1992). This function, like operator process intervention, may have associated with it a set of unique information requirements. Beyond planning to achieve the overall FMS goal, operator monitoring is required as part of supervisory control to avoid workstation bottlenecks, specifically identifying potential problems with schedules in relation to established criteria. Monitoring may also be required to detect actual system problems in attempting to maintain normal system functioning, such as excessive tool wear or machine failure as a result of poor preventive maintenance. The function of monitoring may have associated with it specific information requirements that may differ from those of planning. The function of supervisory control intervention in the FMS context (described earlier) may involve diagnosing system problems, dealing with process schedule disturbances, implementing new processing plans, and performing required functions including administrative tasks like process start-up and shutdown. These activities may occur in attempt-

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Figure 2

Overview of goal, subgoals, and objective to FMS production.

ing to achieve the subgoals of FMS operation, including manufacturing to meet due dates, avoiding bottlenecks, and maintaining normal states of system operation. The supervisory control intervention function is dependent upon the monitoring function for revealing any potential system problems or actual errors. One common goal of intervention is the elimination of processing disturbances or bottlenecks resulting from bad scheduling (planning) and uncovered by good monitoring. If a bottleneck occurs in production, the overall goal of the FMS is compromised. In order to resolve a bottleneck, a human operator may intervene in the supervisory control process to reassign job orders to alternate routes through the FMS. This activity involves the planning and programming (or implementation) functions of supervisory control. Although Sheridan (1992) described the planning and programming functions of supervisory control to be essentially off-line tasks performed in advance of system functioning, in the context of the FMS, it may be necessary for on-line planning and programming to occur. For example, these functions may be required as part of process intervention to develop new job routings to eliminate bottlenecks. In fact, one of Sheridan’s (1997, p. 1314) presentations of supervisory control suggests that such on-line tasks occur through the intervening function (e.g., updating system instructions). In order for an operator to develop alternate processing routes, or to reassign job orders, specific information requirements must be met that may differ from those of supervisory controller planning and monitoring. Figure 3 offers a graphical representation of a partial GTA on the overall FMS goal of meeting production demand and the subgoal of avoiding FMS workstation bottlenecks. The figure details operator subobjectives and tasks associated with meeting the objective of resolving a capacity


Figure 3

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An example partial GTA for the intervention role of a supervisory controller in an FMS.

bottleneck, specifically either maintaining the bottleneck at full capacity or rerouting or suspending jobs. This breakdown of the intervention function of supervisory controller in FMS operation can be used as a framework for establishing the specific operator information requirements for each task. The tasks set out in Figure 3 are key elements in a comprehensive GTA of the four major subgoals to achieving planned FMS output including manufacturing jobs to meet due dates, avoiding bottlenecks, maintaining normal system functioning, and expediting orders if necessary, which is presented in Appendix 1. In the Appendix, task-level analysis leads to specific operational questions that must be answered for performance and, consequently, identification of information sources that allow the questions to be addressed. All the functions of supervisory control, including planning, programming, monitoring, intervening, and learning, are required for an FMS operator to achieve the subgoals shown in Figure 3. With the information requirements for the collective role of supervisory controller determined in the Appendix, comprehensive display design can be approached. 5. DESIGN GUIDELINES FOR SUPERVISORY CONTROL DISPLAY CONTENT IN MANUFACTURING In this section we present information display guidelines to support human operator decision making in the intervention role of supervisory control for the FMS context described above. The guidelines are based on the example GTA and address supervisory

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control display content and, to a lesser extent, information presentation methods. The ultimate objective of any interface design guideline should be to promote operator performance and reduce system error rates, which in turn may promote overall system productivity. As well, guidelines should be developed to encourage ease of human use of automation and improved operator subjective perceptions of personal performance motivating confidence in role-playing. Based on the information requirements presented in Appendix 1, and aspects of human cognition including situation awareness, display design guidelines for the task of maintaining any bottlenecks in FMS processing at full capacity include providing operators with status information on identified bottlenecks. Specifically, displays should indicate the overall capacity figures for workstations and their projected future job load. This information should allow for the identification of potential bottlenecks, which is a highlevel objective to avoiding them, and serve to facilitate operator awareness of which workstations should be attended to and maintained at capacity. Determining jobs to reroute in FMS processing in order to take advantage of underutilized system resources is also a task to potentially resolve bottlenecks. Here we offer information display design guidelines to address supervisory controller system awareness requirements for accomplishing this task: • A list of job order numbers at the workstation should be presented to facilitate operator: (a) awareness of bottlenecked jobs; (b) understanding of the relation of jobs to the goal of resolving bottlenecks; and (c) projection of which jobs to focus attentional resources on in the future. (This guideline is aimed at addressing the question, What jobs are at the bottleneck workstation?) • A list of upstream job order numbers should be displayed to facilitate operator: (a) awareness of jobs upstream to the bottleneck; (b) understanding of the relation of the upstream jobs to the goal of resolving bottlenecks; and (c) projection of which jobs to focus attentional resources on in the future. (This guideline is aimed at addressing the question, What jobs are scheduled to visit the bottleneck workstation within a certain time period?) • A list of job order numbers and their required processing times at the bottlenecked machine should be presented to facilitate operator: (a) awareness of the impact of jobs on the bottleneck; (b) understanding of the impact of each job on the goal of resolving the bottleneck; and (c) projection of which jobs to focus attentional resource allocations on in future planning of routing alternatives. (This guideline is aimed at addressing the question, What load does each identified job place on the bottleneck?) • A list of job order numbers, batch sizes, due dates, slack times remaining, and estimates of processing times remaining should be presented to facilitate operator: (a) awareness of job characteristics; (b) understanding of each job’s priority in relationship to the overall FMS goal of achieving planned output; (c) projection of which jobs to focus attentional resource allocations on in future planning of routing alternatives. (This guideline is aimed at addressing the question, What are the characteristics [or the current status] of the identified jobs? This may help to identify the criticality of each job.) • A list of operations/features remaining to be processed for each job should be presented to facilitate operator: (a) awareness of the requirements to complete each job; (b) understanding of the amount of processing effort involved in alternate job routings to satisfy the goal of resolving the bottleneck; and (c) projection of which job to


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reroute in order to produce the minimal disturbance on overall system operations. (This guideline is aimed at addressing the question, What operations [part features] remain unprocessed for each identified job?) Guidelines for the intervention task of determining alternate routes for selected jobs include: • A list of operations/features remaining to be processed for each job at the bottlenecked workstation should be presented to facilitate operator: (a) awareness of the requirements to complete each job; (b) understanding of the amount of processing effort involved in the use of a new plan to satisfy the goal of resolving the bottleneck; and (c) projection of which jobs to reroute in order to produce the minimal disturbance on overall system operations. (This guideline is aimed at addressing the question, What operations [part features] remain unprocessed for each job selected for rerouting?) • A list of viable plans (routing alternatives) for jobs selected for rerouting should be presented to facilitate operator: (a) awareness of choices in terms of alternative routings; (b) understanding of the relation of new job routings to the utilization of machine resources and the goal of processing without bottlenecks; and (c) projection of the attainable production output given revised plans. (This guideline is aimed at addressing the question, What alternative routes are available to complete processing for each selected job?) • An option to create a new plan different from alternatives posed by the FMS control system should be presented to facilitate operator: (a) awareness of system limitations in-process planning and the potential for computer formulated routings to generate new bottlenecks; (b) understanding of the relation of system process planning limitations and the overall goal of meeting production demand; and (c) projection of the impact of operator formulated job routings on bottleneck resolution and overall production. (This guideline is aimed at addressing the question, What alternative routes are available to complete processing for each selected job?) Here we offer a single supervisory control display design guideline to support operator selection of the best job routing alternative based on the information requirements analysis for the objective of resolving bottlenecks. Specifically, a graphical display with values of the total load, as well as estimates of the remaining processing time and cost of each alternative route should be presented to facilitate operator: (a) awareness of the value of each alternative route; (b) understanding of the relationship between an alternative route and the overall goal to achieve planned output; and (c) projection of which route changes to consider for implementation. (This guideline is aimed at addressing the question, What is the worth of each proposed alternative route? More specifically, it answers the question, What is the total load [sum of the number of jobs in the queue and in-process on each machine on the route] of the machines in the alternate routes offered for each job, as well as the remaining processing time and cost estimates for each alternative route?) Guidelines for implementing alternate job routing strategies for bottleneck resolution are largely dependent upon the interface control technology available to the supervisory controller in an FMS and, consequently, may vary significantly from system to system. For this reason, specific display or control guidelines to support this task are not prescribed here.

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With respect to suspending FMS processing of jobs that are not urgent as an additional task to resolving capacity bottlenecks, several information display guidelines can be offered. In order to identify jobs with high slack or jobs that are not urgent, an operator must be aware of job due dates and anticipated completion times. With this in mind, for all FMS workstations, current job lists should be presented along with their slack times to facilitate operator awareness of early jobs, which jobs could be suspended to free up system resources for processing other jobs to resolve potential bottlenecks, and to allow operators to make projections on which jobs could be suspended in the future. If jobs are suspended to resolve bottlenecks by, for example, storing them in buffer space to a workstation, or at a temporary stocking point, information display should be provided to operators listing the suspended jobs. This display would serve as a memory aid to operators to facilitate recall of jobs in the buffer or stock subsequent to bottleneck resolution. Such a display can be critical to overall task performance as it can reduce cognitive resource demands during identification of high-slack jobs due to the need to retain in working memory information on previously suspended jobs. Based on the supervisory control information requirements analysis presented in Appendix 1, we were able to formulate several general guidelines for information to be presented through FMS interfaces. As stated, the guidelines laid out in this section address the intervention function of supervisory control in FMS processing. The Appendix presents information requirements associated with all supervisory control functions required to address the broad subgoals of achieving FMS planned output and could be used to formulate guidelines for comprehensive FMS interface content design. However, whether developing an interface to aid an operator in performance of a single system function or complete control, it remains critical to verify that the data being presented based on GTA is in fact supportive of effective human-machine interaction. This can be accomplished only through laboratory or field testing of interfaces and measuring operator subjective perceptions of display content as well as system performance. Once the specific information content of a display has been validated, the question needs to be addressed as to how the data should be presented. The types of displays and controls as part of the humanmachine interface in manufacturing systems can dictate whether information requirements for task performance, formulated on the basis of GTA, are accurately and saliently fulfilled. Further, the timing of information presentation based on the state of the system is critical to successful performance and can also dictate efficiency in terms of operator use of interfaces. 6.

SUMMARY OF CRITICAL GUIDELINES AND CONCLUSIONS

Changes in AMTs over time have been described, specifically by referring to the contexts of traditional production systems and FMSs, revealing automation advances in the latter toward increased overall system efficiency, productivity, and profitability. These changes were related to the roles human operators play in manufacturing, ranging from machine tender to supervisory controller, and the specific functions involved in each. Critical differences were identified in roles in terms of defining programming and planning functions, direct operation and operator control actions through human-machine interfaces, and the scope of the impact of decisions on production scheduling. These differences were reflected through operator information requirements for maintaining shop-floor supervisor and supervisory control functions. Generalizations were made as to the information needed by these different workers, including machine status data versus summary reports relating job orders to the system objective of meeting overall customer demand.


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Methods for identifying detailed information requirements were reviewed with GTA emerging as a tool worthy of application to the role of a supervisory controller in an FMS. Information needed to support the intervention function of supervisory control in a constrained FMS context was developed. In conducting this analysis, it was surprising to find that specific information requirements for manufacturing systems supervisory control had not been previously established using methods similar to GTA. The example GTA application was used to motivate manufacturing system display content guidelines development to promote enhanced human-machine interaction. Specific display guidelines were identified for supporting operator performance. The guidelines were developed to support human cognition in FMS supervisory control, specifically the development of system or situation awareness and decision making. Future research examining the composite of supervisory control information requirements for FMS control laid out in Appendix 1 needs to be conducted with the objective of promoting overall system performance through improved comprehensive human-machine interface design. Further, empirical studies need to be made of the effectiveness of existing supervisory control interfaces for comparison with that of new supervisory control display designs based on information needs established through GTA for reducing system error rates and increasing productivity. APPENDIX 1 Complete GTA for achieving FMS planned output including information requirements for FMS supervisory control.

The format employed in this analysis is as follows: X. GOAL X.X. Subgoal X.X.X. Objective X.X.X.X. Subobjective TX Task • Questions to be answered to meet the goal Note: If an objective does lend itself to further decomposition, then it is possible for the subobjective to be missing from the listing. In these cases, the tasks (TX) follow at the very next level in the listing.

1. ACHIEVE PLANNED OUTPUT (i.e., meet the demand dictated by the job orders released to production) 1.1 Manufacture jobs to meet due dates 1.1.1 Maintain job release schedule T1 Release jobs according to schedule • What jobs are scheduled for release? • List of job orders scheduled for release within a user-specified time horizon that specifies order number, release date, due date, and batch size. This will facilitate operator: (1) awareness of each job’s characteristics

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(2) understanding of each job’s priority in relationship to the overall FMS goal of achieving planned output (3) projection of which jobs may need to be the focus of future attentional resources allocations • Are resources available for a selected job to be released? • List of resources required for the selected job to include: machines, fixtures, and tooling. • List of the current status of each resource. This will facilitate operator: (1) awareness of what resources are needed to successfully process a job (2) understanding of what resource changes must take place prior to the release of the job 1.1.2 Monitor job progress T1 Check status of released jobs • What jobs are currently released to the shop? • List of the existing job orders in the shop, their due dates, and expected completion times. This will facilitate operator: (1) awareness of what jobs are in the shop (2) understanding of what jobs are nearing their due date (3) projection of which jobs may be late T2 Identify late jobs • What jobs are currently behind schedule? • List of the existing job orders in the shop, their due dates, and expected completion times (same as in 1.1.2). This will facilitate operator: (1) awareness of when jobs are expected to be completed and when they are due (2) understanding of what jobs are late or possess the potential to be late (3) projection of which jobs may be late and require expediting 1.1.3 Expedite late job orders (See subgoal 1.3) 1.2 Avoid bottlenecks 1.2.1 Identify capacity bottlenecks T1 Examine current workstation queues • What is the current queue size and processing time requirements of the jobs waiting in each workstation queue? • Number of jobs and total processing time requirements for each workstation. This will facilitate operator: (1) awareness of loads on each workstation (2) understanding of the location of potential bottleneck(s) (3) projection of which bottleneck is worst T2 Determine future jobs assigned to each workstations • What jobs are currently scheduled to visit a workstation? • List of the released jobs scheduled to visit a workstation. This will facilitate operator: (1) awareness of future loads on each workstation


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(2) understanding of how future jobs may contribute to workstation loads (3) projection of how future load may create or worsen a bottleneck situation T3 Calculate an overall utilization for each workstation reflecting current and future loading (over a finite horizon) • How is the queue size changing over time? • Projected processing time requirements at each workstation over a given time horizon. This will facilitate operator: (1) awareness of future loads on each workstation (2) understanding of the changing nature of workstation load (3) projection of how future load may create or worsen a bottleneck situation T4 Tag potential bottleneck workstations • Which workstations represent potential bottlenecks? • Show results from T3 1.2.2 Resolve capacity bottlenecks 1.2.2.1 Keep bottleneck(s) operating at full capacity T1 Identify bottlenecks that are idle • Given tagged bottlenecks identified in 1.2.1.4, which workstations are idle? • Current status of tagged bottlenecks. This will facilitate operator awareness of where to focus their attention. 1.2.2.2 Reroute jobs to underutilized resources T1 Determine what job(s) to reroute • What jobs are at the bottleneck workstation? • List of job numbers at the workstation is presented to facilitate operator: (1) awareness of jobs at bottleneck (2) understanding of relation of jobs to goal of resolving bottlenecks (3) projection of which jobs to focus future attentional resources allocations on • What jobs are scheduled to visit the bottleneck workstation within a certain time period? • List of job numbers at the workstation is presented to facilitate operator: (1) awareness of jobs at bottleneck (2) understanding of relation of jobs to goal of resolving bottlenecks (3) projection of which jobs to focus future attentional resources allocations on • What load does each identified job place on the bottleneck? • List of job number and processing time required of the bottleneck machine by each job is presented to facilitate operator: (1) awareness of each job’s impact on bottleneck

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(2) understanding of each job’s impact on the goal of resolving a bottleneck (3) projection of which jobs to focus future attentional resources allocations on (in planning routing alternatives) • What is the current status of the identified jobs? (Helps identify the criticality of each job) • List of job number, batch size, due date, slack time remaining, and estimate of processing time remaining is presented to facilitate operator: (1) awareness of each job’s characteristics (2) understanding of each job’s priority in relationship to the overall FMS goal of achieving planned output (3) projection of which jobs to focus future attentional resources allocations on (in planning routing alternatives) • What operations remain unprocessed for each identified job? • List of operations remaining to be processed for each job is presented to facilitate operator: (1) awareness of the requirements to complete each job (2) understanding of the amount of processing effort involved in the change to satisfy the goal of resolving a bottleneck (3) projection of which job to change to have a minimal disturbance to overall system operation T2 Determine alternate routes for selected jobs • What operations remain unprocessed for each selected job? • List of operations remaining to be processed for each job is presented to facilitate operator: (1) awareness of the requirements to complete each job (2) understanding of the amount of processing effort involved in the change (use of new plan) to satisfy the goal of resolving a bottleneck (3) projection of which job to change to have a minimal disturbance to overall system operation • What alternative routes are available to complete processing for each selected job? • List of viable plans (routing alternatives) for selected jobs is presented to facilitate operator awareness of the different choices in terms of alternative routings • Provide an option to create a new plan T3 Select the best route • What is the worth of each proposed alternative route? • What is the total load (sum of the number of jobs in the queue and in-process) of the machines in the alternate routes offered for each job, as well as the remaining processing time and cost estimates for each alternative route? • List of the values of the total load, as well as estimates of the remaining processing time and cost of each alternative route, is presented to facilitate operator:


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(1) awareness of the value of each alternative route (2) understanding of the relationship between an alternative route and the overall goal to achieve planned output (3) projection of which route changes to consider for implementation T4 Implement selected route 1.2.2.3 Suspend job(s) with high slack T1 Identify jobs that are ahead of schedule • What jobs have a scheduled completion time that is less than their due date? • For a selected workstation, list jobs and their respective slack times (due date—scheduled completion time). This will facilitate operator: (1) awareness of which jobs are ahead of schedule (2) understanding of which jobs are candidates for suspension (3) projection of which job(s) to suspend T2 Suspend job(s) and if no buffer space is available, route suspended jobs to an intermediate stocking point 1.3 Expedite critical ~overdue! order~s! 1.3.1 Split job for parallel processing of the order T1 Identify workstation containing the critical order • At what workstation is the current job located? • List the current location of selected job order. This will facilitate operator: (1) awareness of where (at what machine) the job is currently located in the shop T2 Determine alternate routes for selected job order • What operations remain unprocessed for the selected job? • List of operations remaining to be processed for the job is presented to facilitate operator: (1) awareness of the requirements to complete the job order (2) understanding of the amount of processing effort involved in the change (use of new plan) to satisfy the goal of expediting the order (3) projection of what will need to be done to complete the job • What alternative routes are available to complete processing of the selected job? • List of viable plans (routing alternatives) for selected job is presented to facilitate operator awareness of choices in terms of alternative routings • Provide an option to create a new plan T3 Select the best route • What is the worth of each proposed alternative route? • What is the total load (sum of the number of jobs in the queue and in-process) on each of the machines in the alternate routes offered

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for each job, as well as the remaining processing time and cost estimates for each alternative route? • List of the total load, as well as estimates of the remaining processing time and cost of each alternative route, is presented to facilitate operator: (1) awareness of the value of each alternative route (2) understanding of the relationship between an alternative route and the overall goal to achieve planned output (3) projection of which route changes to consider for implementation T4 Determine how to split the batch • What batch size should be used for each route (current plus alternatives)? • What is the total load (sum of the number of jobs in the queue and in-process) on each of the machines for the alternate routes offered, as well as the remaining processing time and cost estimates for the alternative route for a range of possible batch sizes? • List of the values of the total load, as well as estimates of the remaining processing time and cost of the alternative route as a function of batch size, is presented to facilitate operator understanding of effects of job size on order processing T5 Implement selected route • Provide table for operator to change job parameters (routing) 1.3.2 Change dispatching sequence of jobs at a workstation T1 Identify workstation containing the critical order • At what workstation is the current job located? • List the current location of selected job order. This will facilitate operator awareness of where (at what machine) the job is currently located in the shop. T2 Identify the status of job orders currently at the identified workstation • What is the order status and slack of each job at the workstation scheduled ahead of the critical order? • List of the job orders currently scheduled for dispatch at the workstation along with their order status (normal, expedite, etc.), their due date, estimated completion time, and calculated slack. This will facilitate operator: (1) understanding of the relative importance of each job at the workstation (2) projection of how best to sequence the jobs T3 Alter dispatching sequence of jobs at the workstation to accommodate critical order(s) T4 Label job as critical for use in sequencing at future workstations 1.3.3 Suspend processing (preempt) of a current job T1 Identify workstation containing the critical order • At what workstation is the current job located? • List the workstation where the job is located. This will facilitate operator awareness of the workstation where attention needs to be focused


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T2 Identify the status of the job order executing at the workstation • What is the order status and slack of the job currently being machined at the workstation? • List the order status (normal, expedite, etc.), due date, estimated completion time, and calculated slack for the job being executed. This will facilitate operator: (1) awareness of the job’s current status in the shop (2) understanding of the importance of the job in-process relative to the critical order queued at the workstation (3) projection of whether to suspend the current job at the workstation in favor of the critical order T3 Preempt job execution and place in queue for processing next T4 Start execution of the critical job on the workstation 1.3.4 Reroute critical job orders to reduce delays T1 Determine alternative routes for critical job orders (See objective 1.3.1, task T2) T2 Select the best route based on resource availability (See objective 1.3.1, task T3) T3 Implement new route (See objective 1.3.1, task T5) 1.4 Maintain normal system functioning 1.4.1 Neutralize process disturbances 1.4.1.1 Recognize process disturbance–detect critical system events (resource failure, etc.) T1 Identify the potential formation of a bottleneck (See objective 1.2.1, T1) T2 Identify resource error or failure • What resources (i.e., machines, conveyors, AGVs, etc.) are nonoperational? • List of resources currently tagged as nonoperational. This will facilitate operator: (1) awareness of where attention should be focused (2) understanding of what resources need to be fixed 1.4.1.2 Resolve process disturbance T1 Decide action to remedy disturbance • Information requirements are a function of disturbance type. T2 Implement action 1.4.2 Follow maintenance plans 1.4.2.1 Take resource off-line T1 Update computer database as to the status of the resource 1.4.2.2 Await completion of maintenance T1 Monitor progress of maintenance 1.4.2.3 Bring resource back on-line T2 Update computer database as to the status of the resource REFERENCES Dix, A., Finlay, J., Abowd, G., & Bealle, R. (1998). Human-computer interaction (2nd ed.). New York: Prentice Hall.

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