Planning, Scheduling and Dispatching Tasks in ...

Planning, Scheduling and Dispatching Tasks in Production Control KENNETH N. MCKAY 1) VINCENT C.S. WIERS 2) 1)

2)

Department of Management Sciences University of Waterloo Waterloo, Ontario Canada N2L 3G1 [email protected]

Institute for Business Engineering and Technology Application (BETA) Eindhoven University of Technology PO Box 513, Eindhoven 5600 MB The Netherlands [email protected]

Planning, Scheduling and Dispatching Tasks in Production Control

Abstract: “What is the difference between planning and scheduling?” Production control encompasses many tasks performed by humans, three of which are: planning, scheduling, and dispatching. In the past, the only criterion that could distinguish between the tasks was that planning is usually on a higher level than scheduling and scheduling is on a higher level than dispatching. Hence, the tasks are often ambiguous, unclear, and subject to speculation. There are few formal studies on the actual tasks of planning, scheduling, and dispatching, and there are no known studies that compare or discuss all three. In this paper it is argued that it is important to understand the differences between the tasks. An action science and ethnographic case study is presented as the empirical basis for the discussion, and the implications for decision support system in production control tasks are presented.

Planning, Scheduling and Dispatching Tasks in Production Control 1 Introduction “What is the difference between planning and scheduling?” This question has been posed many times by academics that study humans in production control and has led to long discussions at conferences. One of the key issues associated with improving and advancing production control is recognizing the different tasks that are carried out by humans. Throughout the past century, the production control function has had some of its key components described as planning, scheduling, and dispatching (e.g., Coburn, 1918; Knoeppel, 1920; Koepke, 1941; Magee, 1958; Conway et al., 1967; Greene, 1970; Pinedo, 1995; Pinedo & Chao, 1999). However, researchers that study production control tasks in practice are unable to clearly separate a planner from a scheduler or a scheduler from a dispatcher (Crawford & Wiers, 2001). This paper aims to give insight into the type of tasks that are carried out in production control, focusing on planning, scheduling and dispatching. A case study that involved task analysis is used to describe production control tasks in terms of the decision making horizon, the decision making process itself and the context. Subsequently, we translate the task descriptions to simple information input and output characteristics. The model that we present on planners, schedulers and dispatchers is not a cognitive task model as such; instead, the main contribution of the model lies in the recognition and delineation of the tasks using simple information input/output characteristics. It also describes the implications on decision support systems (DSSs) for the different tasks. The paper is structured as follows: Section 2 elaborates on the concepts discussed in this paper by giving an overview on production control concepts, functions and tasks. Section 3 contains a literature review on production control task models and presents the task analysis framework used in this paper. Section 4 contains the case study, including a description of the action science and ethnographic methodologies used in this research. In Section 5, the similarities and differences between the planning, scheduling and dispatching tasks are discussed and presented in a model. Also, the implications on designing decision support are presented. Lastly, in Section 6, the conclusion is presented.

2 Production control: concepts, functions and tasks 2.1

Concepts and functions

Production control concerns itself with the coordination of manufacturing activities over a time horizon. Because the production control field is large and complex, it is often decomposed into several elements. One of the decompositions made is between ‘higher’ and ‘lower’ levels of production control. The higher levels usually deal with longer time horizons, and aggregates the control elements. For example, a high level of production control at a truck manufacturing company might look at trucks per month. A low level might look at engine components to be made tomorrow and specifically what is being manufactured at two in the afternoon. The complete coordination problem is very broad and deep. Production control systems are often decomposed using these time and aggregation concepts into a hierarchically organized planning and control structure to reduce the complexity of the control problem. This approach is known as Hierarchical Production Planning (HPP). The hierarchical production planning paradigm is widely used and has become an accepted planning and control strategy for many medium to large manufacturing organizations. Bertrand et al. (1990) describe the hierarchical production planning paradigm and they distinguish between 1

goodsflow control, which concerns planning and control decisions on the factory level, and production unit control, which concerns planning and control decisions on the production unit level. The goodsflow control level also coordinates the various underlying production units. This is depicted in Figure 1. goodsflow control

decoupling point

production unit control A D

decoupling point

C

production unit control A D

decoupling point

C

Figure 1: Goodsflow control and production unit control (adapted from Bertrand et al., 1990)

A production unit is an outlined part of the production process of a company. A production unit produces a specific set of products from a specific set of materials or components, with a specific set of capacity resources. Dependent on the complexity of a production system, a production unit can be a single machine, a group of machines with the associated personnel, or an entire factory. Production control decisions are often decomposed to conform to the decomposition of the production system. Production control at the goodsflow level is associated with planning, whereas production control at the production unit control level is associated with scheduling. Planning and scheduling are also well-known concepts in the operations research literature. In the operations research domain, planning can be defined as: applying ordering constraints on actions (e.g., Burgess & Steel, 1996). Scheduling is defined as: allocating jobs to resources in time (Conway et al., 1967). 2.2

Functions and tasks

The concepts of planning, scheduling and dispatching in production control are defined more or less as black boxes. Academic and research oriented literature, such as Bertrand et al. (1990), define material planning in terms of the input, i.e. the master production schedule, the bill-of-materials and inventory data; output (e.g., timed-phased requirements records); and objectives (e.g., desire to provide the right part at the right time). However, apart from the basic logic, there are no descriptions on how this function should actually be carried out in practice, i.e. how the black box performs its task. In the industrial sector, the widely used Manufacturing Resource Planning (MRP II) concept identifies the following functions: master production scheduling, rough-cut capacity planning, material requirements planning, detailed capacity planning, work order release, and shop floor control (Plossl and Wight 1967, Orlicky 1975, Vollmann et al., 1988). Apart from the basic logic, the literature on MRP II does not describe how each function should actually be carried out in practice. In many production settings, this lack of clarity and prescription leads to the situation where almost every firm or plant interprets the meaning of planning, scheduling, and dispatching differently – task assignments, naming, organizational structure, and so forth. This inconsistent and imprecise structure within and between firms means that the production control tasks are difficult to study scientifically: observe, describe, or compare. The task combinations appear almost limitless. For example, from a production control point of view, a machine operator may take on the role of dispatcher, or one person may be planner and scheduler and dis2

patcher. The employees may also expedite shipping, contact suppliers regarding material arrival, audit inventory levels, and reconcile shop floor results to name just a few of the many activities planners and schedulers have been observed to do. Figure 2 illustrates some of the possible variations in task content. planning

Task 1

scheduling warehouse management

material management inventory control shopfloor Task 2 execution

Figure 2: Possible variations in production control tasks

The definitions of planning, scheduling and dispatching from the production control field do not clearly indicate how the functions should be carried out. For example, if one human does both planning and scheduling, how to clearly recognize the planning and scheduling part of the job is not defined? What is the difference between either ‘applying ordering constraints on actions’ and ‘allocating jobs to resources at a specific point of time in the future’? In other words, what is the difference between planning and scheduling? And where does dispatching fit in?

3 Production control task models 3.1

Literature review

There have been several field studies on humans that perform production control functions (an overview is given in Crawford & Wiers, 2001). However, researchers that study production control tasks in practice rarely separate a planner from a scheduler or a scheduler from a dispatcher. For example, Sanderson & Moray (1990) use scheduling models to understand how time pressure affects tasks. Nevertheless, in the study, humans are compared with dispatching rules. Sanderson (1989) reviews the human planning and scheduling role in advanced manufacturing systems, and indicates that the focus for her review is scheduling and dispatching. Sanderson defines scheduling and dispatching as “relatively short-term activities determining how and when a product will be produced.” There are few studies on production control tasks that aim to construct a comprehensive task model, and the studies fail to build upon each other (Crawford & Wiers, 2001). The majority of studies appear to start from a new perspective, disregarding previous work and have failed to create a firm foundation for a definitive model for production control tasks. In some of the early studies, the focus was to construct a mathematical model to describe the scheduler’s decision behavior (e.g. Dutton, 1962, 1964; Fox & Kriebel, 1967; Hurst and McNamara, 1967; and Dutton & Starbuck, 1971). Whereas these authors all claim to study scheduling problems, the studies contain the following variety of tasks: scheduling corrugated box fabrication, production sequencing in a shoe-box plant, planning a woollen mill, production sequencing and machine time prediction in a textile plant. Thurley and Hamblin (1962) conducted five small case studies in which a variety of manufacturing situations were analyzed. Their focus of research was the fact that technological, human and organizational factors all influenced the behavior of production supervisors who also carried out planning and scheduling tasks as part of their roles. However, they did not make a clear distinction between the planning and scheduling tasks. Other knowledge elicitation studies have been carried out 3

under simulated or controlled conditions (Tabe et al, 1988; Lopez et al, 1998) or as limited field studies (Duchessi & O’ Keefe, 1990; Norman & Naveed, 1990). The findings tend to be specific to particular research conditions and therefore cannot be applied generically. Focusing on the human and system interaction aspects, McKay (1992) attempts to explain how the human is crucial to production control decision making and proposes a framework that captures the type of man-machine control that is necessary. Subsequent research by McKay et al. (1995a, 1995b) addressed other issues related to the human factors of scheduling, many of which deserve further research. One of the few attempts to build a task model for a production control task is described in Sanderson (1991). She constructs a framework for the Model Human Scheduler (MHS) that consists of twenty–seven production rules linking different types of scheduling activities. However, this line of research has not been pursued. In studying tasks in production control, Crawford (2000) distinguishes between three roles: an interpersonal role, an informational role and a decisional role. Furthermore, she describes three tasks for a scheduler: formal tasks, housekeeping tasks and compensation tasks. However, in this study the distinction is not clear between the three levels of decision making. Furthermore, it is not clear if the model needs to be adapted if an individual finds themselves in a multi-tasked situation, e.g. formal and housekeeping. 3.2

Framework for task analysis

From a task characteristics point of view, there seem to be similarities between planning and scheduling on the one hand, and scheduling and dispatching on the other. These similarities lead to the fact that models developed by earlier studies are not strictly confined by the authors to either planning, scheduling or dispatching tasks. The only distinguishing characteristic for planning, scheduling and dispatching that can be derived from existing literature is that planning is done on a higher aggregation level and dispatching on a lower aggregation level, with scheduling in-between. Because this paper attempts to describe the difference between planning, scheduling and dispatching, the focus will be on the interrelationships between the tasks. More specifically, this paper focuses on the aggregation level of the input and output of the tasks to distinguish between planning, scheduling and dispatching. Three task characteristics will be used as a framework to analyze the tasks: • Horizon and Timing. The decision making horizon can be measured in years, months, weeks, days, hours, and minutes. The timing of decisions can be continuous or periodic. • Decision Making. The type of decisions that are made in the task can be defined, and the amount of autonomy that is typically present in the task can be specified. • Context. The decision making context can define the when, what, and why that helps to define the functions and tasks that a planner, scheduler or dispatcher has to interact with. A possible fourth task characteristic would be performance; however, this characteristic is found to be closely related to the decision making in the production control tasks. Therefore, descriptions about task performance are embedded in the decision making category.

4 Case study 4.1

Background

A long term field study on production control has been conducted in one plant. Over a six year time period, the study has focused upon dispatching, scheduling, and planning within the plant. The horizontal scope within the plant ranged from the receiving of incoming raw materials to the shipping of finished goods. The study also occurred at different vertical levels within the plant: from the shop floor to management policies. 4

The following subsection overviews the methodology and the case study site. The focus of the following subsections is not on the methodology per se or the various detailed observations that accumulated over the study period; instead, the purpose is to establish the general field methods that allowed the aggregate view in Section 5 to be prepared. For those interested, detailed observations and aspects on production control have been reported elsewhere (McKay, 1992; McKay et al., 1995a; Wiers & van der Schaaf, 1997; McKay & Wiers, 2001; Wiers, 2001). 4.2

Research methodology

4.2.1 Introduction The research methodology was a combination of action science and ethnography. As the research activity was extended and broad in its scope, the two methods were used at different times for different goals. In both, the researcher was involved in the situation and was a full participant with a role to play. In the action science vein, the researcher asks “How does it happen to be?” and “How might we transform what we discover?” (Argyris et al., 1985, p. 228). The action science research may also have a priori theories or issues that are being used and tested. In contrast, the focus of the ethnographic approach is to learn from the subjects, avoiding judgement and biases, and to relatively passive in the situation (Spradley 1979). Case studies of the type described by Yin (1989) are typically of a limited duration with a single snapshot, or with a restricted number of visits. Action science studies are normally more extensive, but rarely extend to the duration typical of ethnographic studies, which try to capture full life cycles and the related dynamics. Each technique, action science or ethnographic analysis, is suitable for different research objectives and situations. At the study site, the activities were wide-ranging horizontally and vertically throughout the plant ranging from shipping to receiving and from senior management strategies to shop floor operations. As a result, a mixture of action science and more traditional ethnographic research was used. Action science was used in the study of daily operational decisions in one area of the plant with a study and transform role. Ethnographic analysis techniques were used for researching planning, scheduling, and dispatching throughout the plant with an integration role. 4.2.2 Action science An action science oriented study can be considered a fourth type of case study (Argyris et al., 1985), where limited case studies are usually either exploratory, descriptive, or explanatory (Yin, 1989, p. 13). Gummesson (1988) discusses Yin’s categories and this fourth style, and he claims “Action research is the most demanding and far-reaching method of doing case study research.” (p.99). Subsequently, Gummesson lists eight characteristics exhibited by action science research. In action science, the researcher may do some or all of the activities related to basic case study data collection and analysis, but also be part of the actual process, participating in the case, and partially responsible for any success or failure (e.g., Jorgensen 1989, Easterby-Smith et al., 1991). One of the goals of action science is to discover what practical information is used to trigger and control the actions. This is done by participating in the process. Subsequently, this information is then used to transform the situation using a theoretical foundation. The action science methodology can be used to discover new understanding of the triggers and issues in a situation. The larger phenomenon might be previously identified, but the details not understood. Action science can also be used to test existing theories of cause and effect. In the action science methodology, an existing situation is identified, prescriptions are in place, and the process of study and transform scientifically applied, recorded, and analyzed. If the research 5

effort is merely applying widely existing knowledge and techniques to the situation without a scientific agenda, the activity may resemble consultancy with little academic value. At the study site, the action science approach was used to study and then transform the short term scheduling activities for part of the plant. Using concepts and theories of task allocation and context-sensitive software engineering, a single integrated task was decomposed into two tasks with an added worker, granularity of decision elements changed, decision horizons altered, work visibility tuned to remove irrelevant items, and specialized decision support systems created for the two workers. This transformation activity, which took approximately two years, provided the opportunity to start analyzing and dissecting what was truly different between dispatching and scheduling. 4.2.3 Ethnographic approaches Ethnographic approaches are intended to be less invasive than action science activities. With the intent to learn from the subjects, there are three phases of ethnographic research commonly undertaken: observation (non-participant to full participant involvement), interviews, and quantitative analysis based on questionnaires, structured interviews or data collection (see Spradley, 1979; Grills, 1998; and Schensul, 1999). When a colony or group is studied, the methodology progresses from the observation level to the quantitative analysis. Following the action science research where tasks were radically transformed, we were given the opportunity to replace an existing set of decision support tools with an integrated tool. In this case, the goal was not to radically transform the task, and a different research approach was needed. The replacement of the tools was completed over a two year period, during which the first decision support system continued to be used and supported. Both areas have continued to use the various decision support tools and have received ongoing support in the two years since. Both activities, the transformation and integration roles, provided an opportunity to observe the scheduling tasks for an extended time period (six years) and to see the longitudinal aspects. It is our view that the scheduling task exists within a culture, and is itself a culture with behaviors, terminology, norms, expectations, interactions and interrelationships. This is similar to the view taken by Hutchins (1995) in studying the cognition elements of navigators on naval vessels. However, the navigation task is at one end of the cognitive planning spectrum, while factory dispatching and scheduling is at the other: approaching dozens and possibly hundreds of harbors each day in a continuous fashion does not typically happen for large military vessels, as does the job scheduling activity in a factory. Neither does it have the constraint elasticity that factory planning and scheduling has: the wharf cannot be moved quickly or another channel dug in real time. Furthermore, the navigators have one main objective, docking the vessel, with many factors to control, while a factory scheduler has many independent objectives. Nevertheless, there are similarities in that cultures exist that define the task, terminology is heavily used, and there are many expectations in the workplace that guide behavior and interpretations. In both studies, production control and naval navigation, ethnographic and field study methods were used to observe and understand what the task really was, what the people really did, and why they did it. 4.3

The factory

4.3.1 Primary process A brief description of the field study environment is given below; a more extensive description is given in McKay & Buzacott (2000). The site, which we will call ACME, is in the middle of the automotive supply chain, has approximately one dozen final assembly lines and ships roughly four dozen final items in a just-in-time fashion to several different customers. ACME runs a three shift operation and often plans production for the weekend. The flow time 6

is measured in minutes and production is in hundreds per shift for each line. The factory is composed of two major areas: a flow shop and a job shop. Production is pulled through the plant from the finished goods inventory. The assembly area builds on dedicated resources and fills a target level of on-hand finished goods. The job shop works in a batch mode with a cyclic pattern. The area is made up of cells and each cell has one or more machines. The capacity in the job shop is theoretically balanced to that of the flow shop. There are a number of characteristics about ACME’s job shop which are not found in traditional job shop problem formulations (e.g., Conway et al., 1967; Baker, 1974; Pinedo, 1995; Pinedo & Chao 1999): • Virtual flow lines • Crewing issues • Independent setup phase • Transfer within batch capability • Ability to pause a job indefinitely and move resources • Independent but co-dependent parts The above issues complicate the decision process beyond that of simple job characteristics being matched with machine availability, which is the traditional view of job scheduling. With the exception of the job shop, the plant’s production system is relatively straightforward. 4.3.2 Production control Production control in the plant focuses on the following parameters: • Setups (e.g., unnecessary setups caused by preemptions, miscues in communication) • Direct manpower levels (e.g., overtime) • Indirect manpower associated with production (e.g., support groups) • Inventory (e.g., too much, too little) • Shipping (e.g., expediting deliveries at a higher cost) Missing from the above description is any mention of ‘traditional’ production metrics such as minimizing lateness, weighted tardiness, the number of late jobs, and so forth. In lean manufacturing as practiced in the automotive sector, these metrics are not the major ones to track or focus upon. For example, in the factory we are describing, the concept of ‘lateness’ is not used in planning, scheduling or dispatching. Lateness is not part of the final schedule. If there is any lateness, it is not planned and is considered totally unacceptable with significant penalties and costs. At a tactical level, metrics such as flow time are tracked by management, but are not explicitly addressed by the production control process. 4.3.3 Roles and Tasks At ACME it was fairly easy to identify planners, schedulers, or dispatchers, because a job title of planner existed in the plant and was held by four individuals. In the job shop, there was a planner, scheduler and dispatcher, although sometimes they did each other’s job, sometimes rotated regularly through the positions on shift work, and sometimes extended their decision making into another area. They also substituted for each other when vacation or illness dictated. On a daily basis, one was charged with dispatching, one with scheduling, and one with planning. Contrarily, in the flow shop, there was one planner who always did a combination of planning, scheduling and dispatching. The tasks will be described in detail in the next section.

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4.4

Task analysis

4.4.1 Planning Horizon and Timing Since the early 1900s, the levels of granularity for planning have been influenced by a number of factors, such as: workers prefer a weekly shift pattern, management requires aggregation, fiscal reporting must be done in months, quarters, and years. These forces have often led to a so-called bucket brigade, which means that demand and supply are viewed grouped by weeks and months. Like other planners we have studied, the planners at the plant deal with horizons that are measured in years, months and weeks. However, monitoring activities that relate to the forecast and incoming orders are carried out at a day or week level. The monitoring is important with today’s Electronic Data Interchange (EDI) and pull situations: sudden shifts in demand can wreak havoc with carefully orchestrated resource allocation. However, it can also provide clues about the future. The timing of planning decisions depends on the production control situation. In a make-tostock situation, the planner needs to make a decision for each time period for each product. In a make-to-order situation, the planner needs to make a decision about any changed and new work. A daily decision making activity of the planner lies in updating the plan as a result of changes in capacity or demand. The precise frequency of decision making depends on when the customer orders appear in the system that the planner uses, and how often the system is updated with production data. It is common with firms in the automotive sector to have a mix of each type of situation. For example, prototype and service parts are more of a make-toorder situation whereas the normal pull from an assembly plant has a make-to-stock profile with multiple year forecasts provided by the customer. Decision Making The planner for each area attempts to balance demand with available capacity using inventory as a buffer. Inventory is used to stabilize production and the desired levels are allowed to float between a minimum and maximum. Major capacity issues are identified that require substantial amounts of overtime, shift changes, or subcontracting. In cases of imbalance, the planner will try to level or change demand or supply. Planners usually have more autonomy regarding supply and demand than the scheduler or dispatcher. The planner is in many cases able to change the demand volume, requested product, due dates, and shipments. The total volume may not be altered, but the mix and delivery schedule can be adjusted for what is on hand at the customer, what is in the pipeline to the customer, and what is currently on hand in finished goods inventory at the factory. It is possible to intelligently manage the inventory at each level and use one level to another level’s advantage in a temporary fashion. That is, targets are set for inventory levels but they do not have to be followed blindly in a mechanical way. The supply side can also be manipulated by the planner. The planner might negotiate additional manpower, different shifts, scheduled overtime, or resource changeovers. In previous decades when significant levels of inventory existed throughout the supply chain, the frequency and type of manipulations were less. However, the lean targets for manufacturing force the planners to be more proactive and on top of the situation daily. When the complexity of the planning domain (i.e., number of parts, periods) increases beyond the planner’s cognitive abilities, three techniques are commonly used. First, information is aggregated into larger buckets: e.g., planning for the week instead of by the day. Second, additional planners are used and the work is distributed. Third, the planner can choose to ignore the majority of the daily task and focus on critical spots.

8

Context The planners have regular interaction with managers to plan the work force, set production control policies, acquire capacity, and the like. Decision making about the future is one part of the task: the planner is also responsible for tracking and analyzing production as it compares to the plan. Various reports are fed back to management daily, weekly, and monthly. There is a regular monthly planning cycle for firming up the next period and to account for any changes in the twenty-week horizon. Periodically through the year there are major planning activities for yearly, three year, and five year plans. For the major planning events, the planner is expected to generate multiple scenarios quickly and accurately. Changes made by the planner are often not felt by the scheduler or dispatcher. The planner interacts with the scheduler and dispatcher when changes in demand or capacity cannot be dealt with in the mid or far future. 4.4.2 Scheduling Horizon and Timing The horizon of the scheduler is determined by the amount of uncertainty, but usually one week. If the amount of uncertainty increases, the horizon used by the scheduler decreases, and vice versa. The scheduling task can roughly be split into two parts: constructing the schedule and reactive scheduling. Constructing the schedule is usually done once or twice per week if the horizon is one week. This is done by moving work around, making sequences, and smoothing loads as the customer requirements are loaded in and become visible. The rest of the week is spent reacting to requested maintenance downtime, major repair plans, and any conscious build ahead or push out of demand. Decision making is almost non-stop in terms of moving, swapping, inspecting job attributes, and the like. Decision Making The scheduler tracks any resource and material issue, change in capability or capacity that can result in significant constraint changes. Ideally, the scheduler does not plan for head-to-tail relationships on all work and allows certain safety time or buffers wherever there is a potential risk situation; this is the mechanism that allows some simplifying assumptions to work. For this reason, the scheduler orchestrates resource allocation to accommodate special requirements, such as preventive maintenance, service parts, emergency builds, vacation season. A scheduler does not simply start at the beginning of the problem space. He or she will prioritize issues according to the types of constraints involved (Wiers, 1997): • work that is critically constrained, i.e., it endangers the goals of the scheduler; • work that is tightly constrained, i.e., it has little alternatives; • work that is extensively constrained, i.e., it needs to obey to many (interrelated) constraints; and • work that is stochastically constrained, i.e., history has proved that a specific set of work is often troubled by disturbances. To create a feasible load, the scheduler will focus on manipulating the demand instead of the available capacity. This is done by working with the planner and purchasers, or for example by batch slitting. If the demand pattern cannot be tweaked, the scheduler will try to use alternative resources, overtime, or negotiate relatively easy changes to the resources. It is hard for the scheduler to see the quality of the scheduling decisions because of the time delay and the number of changes that occurs. Moreover, it is hard to assess what factors have influenced a certain performance level to what extent (see also Gary et al., 1995; Stoop, 1996). 9

Context The scheduler is responsible for firming up plans and creating a feasible sequence for the dispatcher. The dispatcher is looking to the scheduler for work to consider, and the planner is concerned about impacts on the plan caused by any scheduling changes. Schedulers get pressure from the dispatcher and the planner. Schedulers rarely get much pressure from the operations people, except in cases where dispatching is being carried out by operations people. Management may also be involved in scheduling, but the intensity is less than at either the planning or dispatch level. In some ways, scheduling is almost an invisible layer or activity. The dispatcher and scheduler interaction can be tense if there is not enough work or the right work that is ready to be dispatched. For example, the dispatcher may be dealing with a bottleneck and the scheduler has not moved in or firmed up the work necessary to keep the bottleneck busy. There can also be tension with support groups if the scheduler moves work in ahead of material or tooling expectations. During periods of less-than-peak capacity utilization, changes made by the scheduler should not affect the planner to any great degree. However, when the shop is running behind and everything is ‘tight’ on the schedule, any change made by the scheduler will affect the planning. 4.4.3 Dispatching Horizon and Timing The dispatcher’s horizon is measured in minutes, hours, and days. The dispatcher usually has two jobs planned in detail for each resource: the current one and the next one. Many of the jobs will run for two to three days, and decisions beyond this are considered as scheduling in the factory. Dispatching decisions are made continuously each day. If the dispatcher is the machine operator, the decisions are made when the current job finishes and the next must be started. When the dispatching is centralized and the dispatcher has multiple resources to contend with, then multiple decisions are made throughout the day. In a medium job shop with about sixty resources, a dispatch decision can be made every five to ten minutes throughout the day. Another trigger for a decision by the dispatcher is when the workforce arrives on the scene. In this case the dispatcher might decide what jobs to start with. Moreover, if there are more machines than people in a specific shift, the dispatcher also decides what to work on. Decision Making The usual sequencing decisions that are made by the dispatcher include setups, resource availability, job duration, and due dates. The dispatcher must deal with preemption, running short, running over, machine breakdowns, personnel problems or absenteeism, current state of the tooling, and current availability of material. In making decisions, there are various costs and issues that must be considered: cost of mobilization, increased risk of quality problems, extended processing times, possible damage to the process, destabilization of existing system and processes, and the cost of reverting. Moreover, due to the time pressure to make a decision, the options will not be explored in-depth. The dispatcher must deal with very detailed data, including any information that can affect the choice of resource, the duration of the operation, and the quality of the result. The range of data used by the dispatcher is exceptionally broad covering environmental factors (e.g., weather), cultural phenomena (e.g., impact of the holiday season on absenteeism), worker morale (e.g., who is likely to give that extra effort or work overtime), recent performance (e.g., who did what the last time and how well it was done), and the normal engineering data (e.g., standards, material specifications, processing descriptions). Any piece of information, no matter how small or seemingly innocent, may have relevance. Moreover, the dispatcher is constantly gathering gossip, tidbits of conversations, and is generally being nosey. 10

The dispatcher has limited influence on the demand and some on the available capacity. In the short term, the dispatcher can do little to a demand requirement in a one-of-a-kind situation since there are no buffers or safety-stock to use. In other situations, the dispatcher may be able to contact the customer and delay or advance work. Furthermore, the dispatcher may be able to exploit elasticity in production quantities and safety-stock. In this case, there will be an impact in the future, but the day will be saved. Usually, the dispatcher has more degrees of freedom in the supply side, which are usually explored before the demand side. There might be workarounds to make a product, for example: using a previous generation machine or adding manual operations. Other manipulations include: splitting or merging operations, adding or altering equipment, using substitute material, making substitute material from other material, extending shifts for extra capacity, subcontracting operations. Context The dispatcher has the most immediate context, because the decision making of the dispatcher immediately affects the use of personnel and resources. The dispatcher works with the lead hands, supervisors, foremen and operators to review options, analyze the impact on other work scheduled, and decide on the next steps. Any loss on a bottleneck resource will delay the whole process and this responsibility falls to the dispatcher. The dispatcher must also consider the impact decisions on the scheduler and planner, especially in a tightly packed plant where decisions spread like a ricochet.

5 Planning, Scheduling, Dispatching 5.1

Classification of Differences

Planners, schedulers and dispatchers represent three levels of production control. This article has demonstrated that there are significant differences between the planning, scheduling and dispatching task. Table 1 summarizes the differences found in the task analysis of Section 4.4. Horizon

Availability of information

Interaction with lower level

Time pressure

Multiple, bucketed

High

Low

Low

Scheduler

Single, real-time

Lower

High

Higher

Dispatcher

Single, real-time

Low

n.a.

High

Planner

Table 1: Differences between planners, schedulers and dispatchers

First, while schedulers and dispatchers are faced by similar demand and supply decisions within their time horizons, the planner simultaneously tracks daily issues, prepares weekly summaries, monthly plans, yearly plans, and so forth. The decisions of the planner are formulated as buckets of work in a specific time period, whereas schedulers’ and dispatchers’ decisions are formulated as specific tasks in real-time. Second, the planner has much more information regarding demand than the lower production control levels: for example, when the customer or sales has put extra padding in or is building ahead. The planners also know when the customer is planning down time, line changes, and special try outs. The majority of this additional information comes from personal contacts that are not available to the scheduler of dispatcher because of their different contexts. 11

Third, in Section 4.4.1 it was stated that changes made by the planner are often not felt by the scheduler or dispatcher. Because the planner works with buckets, the relationship between the planner and the scheduler are defined clearly and the planner can work in relative isolation from the scheduler. However, the scheduler is more actively concerned with the dispatcher: he or she tracks any resource and material issue, change in capability or capacity that can result in significant constraint changes that are not easily handled by dispatching. The scheduler works with days and weeks, and assumes that ‘minor’ issues such as tool repairs can be ignored and left to the dispatcher. Fourth, there are large differences in the pressure that is related with the tasks. For the scheduler, the future is the focus, and hence the immediate pressure is not as great as for the dispatcher. The dispatcher must function like a fire department with everything ready for decision making continuously. The dispatcher gets faster and more vocal feedback on decisions that planners and schedulers and this changes the operational context. For example, the dispatcher must explain any ‘poor’ decisions that affected the afternoon or evening shift, explain why they were unable to see the difficulty in advance, and explain why they are changing the plans for today they made yesterday. The planner has somewhat less pressure because there is much more time to solve problems, and problems can be more easily escalated to the responsible managers. 5.2

Task delineation model

Based on the input and output characteristics of the task, it is possible to draw up a task delineation model that answers the question: “What is the difference between planning, scheduling and dispatching?” In other words, we are not delivering a comprehensive cognitive task model as a result of the research described in this paper; rather we present a simple model that researchers and practitioners in the production control task field can use to distinguish between the planning, scheduling, and dispatching task. As stated in Section 3, it can be difficult to identify the planning, scheduling or dispatching components in what the human is actually doing. Existing literature teaches us that planning is ‘more’ aggregated than scheduling, and scheduling is ‘on a higher level’ than dispatching. However, these task characteristics are of a qualitative nature and cannot be measured objectively. This is not a problem if the tasks exactly follow the production control structure in a specific company, and if the production control structure in practice is in strict accordance with theory. However, most situations do not seem to follow this stereotype, and even theory itself is inconsistent in using the terms ‘planning’ and ‘scheduling’. Hence, for most situations, there are no models that indicate at which point precisely the planning task ends and the scheduling task begins. Therefore, any interpretation of a task by using these characteristics is arbitrary. To distinguish between the planning, scheduling and dispatching tasks, task characteristics are needed that can clearly be applied and interpreted. Based on the task analysis findings presented in the previous subsection, it is possible to extract two attributes that help decompose the decision processes into well-defined categories. The two attributes are the input drivers and output representations of the task. For example, the input of the dispatcher is based on today’s facts, instead of forecasts as in the planner’s and scheduler’s case. Also, the output of the planner is represented by volumes in buckets, whereas the scheduler and the dispatcher work with jobs in real-time. A task delineation model based on these two task attributes is shown in Table 2.

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Input driven by: Planner

Expectations of the future

Scheduler Dispatcher

Reality

Output represented by: Volumes / bucket Orders, assignments, or jobs / real-time

Table 2: Delineation of planning, scheduling and dispatching task

As shown in Table 2, there is overlap between planning and scheduling on the one hand, and scheduling and dispatching on the other. However, when the two task characteristics are combined, it is possible to distinguish between the three tasks unambiguously. 5.3

Implications for decision support

The results discussed in sections 5.1 and 5.2 assist with understanding what an individual does and what is expected of them. This is useful for task and role analysis and for categorizing what a scheduler or dispatcher does daily, weekly, monthly, and so forth. Once the tasks are more clearly separated, it is possible to consider the how decision support systems can be designed to better support the various tasks and roles; especially when individuals have multitasked positions. It is obvious that the differences between planning, scheduling and dispatching result in different requirements for decision support. When the task is clarified using the model in section 5.2, the types of interactions associated with each task are isolated and can then be further analyzed. To organize the requirements and to understand them better, the requirements can be grouped according to a decision taxonomy such as introduced by Wiers (1997). In this taxonomy, there are four key task characteristics that set the requirements for decision support systems in production control tasks: • The amount of autonomy that should be supported by a decision support tool for a specific task depends on the uncertainty and flexibility of the lower production control task levels. If the lower control levels are stable and predictable, the higher control level can get more autonomy. • The transparency that a decision support system should offer results from the extent to which the task is critical and ill-defined. When the task is more critical and ill-defined, the need of the human to be in control and thus the required transparency of the tool increases. • The level of support that a decision support system can offer depends on the amount of the real-world that can actually be modeled in the system. If there are many occurrences where the human must react manually, the level of support of a system should be lower. • The information presentation by a decision support system should match the human’s representation of the problem domain. Furthermore, aggregation should be used by the system to offer an overview in complex production control domains. The four characteristics of a decision support system for production control tasks are translated to the tasks that are analyzed in the case study in the table below. Planner

Scheduler

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Dispatcher

Planner

Scheduler

Dispatcher

The dispatcher’s decision is followed exactly by the shopfloor, thereAutonomy fore their autonomy is high. The planning task is The dispatching task is The scheduling task is less critical than the more critical than the more critical than the scheduling task, so Transparency other tasks, so the planning task, so transtransparency of the transparency must be parency must be higher. DSS can be lower. higher. The level of support The level of support has The level of support has can be relatively high, to be intermediate, beto be low, because Level of because the domain is cause not all informamuch information is not support well-specified (infortion is formally availreadily available. mation is available). able. The main presentation of the DSS should a list of jobs released by the The main presentation The main presentation scheduler to choose of the DSS should show Information of the DSS should be a from. An overview is multiple buckets in a presentation Gantt-chart. usually not needed betable. cause the set of jobs considered is relatively low. As planners have relatively little interaction with scheduling, their autonomy can be high.

Schedulers have much interaction with dispatching, and their autonomy is therefore limited.

Table 3: Requirements for decision support for the planning, scheduling and dispatching task

Research in production control often implicitly assumes that the functionality of a decision support tool will follow the requirements as specified in models, such as described in the table above. This assumption usually holds in cases where software is custom built for a specific situation with unlimited funds (Wiers, 2002). However, in practice, companies implement commercially available standard software packages. Decision support for production control tasks is typically offered by Enterprise Resource Planning (ERP) systems (Markus & Tanis, 2000), which are based on the MRP II paradigm, and Advanced Planning and Scheduling (APS) systems (Stadtler & Kilger, 2000). These standard packages are configured to the characteristics of the company, and the functional flexibility of these systems is limited (Wortmann, 1992). There are countless possible applications of ERP, APS and other systems to support production control tasks. Therefore, this paper presents one typical configuration of an ERP and APS system and will discuss possible pitfalls that exist in implementing these systems in light of the research results. The figure below shows a typical configuration of an ERP and an APS system.

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MRP APS MES

Master Planning Material Requirements Planning

Production scheduling

detail

Dispatching Figure 3: Typical Configuration of ERP and APS

Planning tasks in industrial companies are often supported by ERP systems, which for production control are based on the MRP II concept. APS systems usually have a module for production scheduling. The dispatching is often done on the shop floor using a so-called Manufacturing Execution System (MES). This typical decision support structure for planning, scheduling and dispatching has the following weaknesses: • Difference between planning and scheduling is unclear. The output from planning is often fed into the MRP algorithm which performs a time-phased material explosion based on the assumption of infinite capacity. The outcome is a draft schedule that is proposed to the scheduler. However, the parameters used by MRP are often not correct (see for example den Boer, 1994). Consequently, the scheduler changes the MRP output and is thereby conducting planning task activities. This weakness is illustrated by the fact that the question “What is the difference between planning and scheduling?” is very hard to answer in practical situations. • Dispatching is underestimated. Both in ERP systems that are based on the MRP II framework as in APS systems, there are no specific modules for dispatching. Apparently, either it is assumed that the scheduler also does the dispatching, or the dispatching is done by operators on the shop floor. • Scheduling systems are not transparent and their level of support is too high. Most scheduling systems assume that the scheduler has a large autonomy. Therefore, these scheduling systems contain algorithms to construct a ‘optimal’ schedule. This leads to a schedule that is far too prescriptive for the dispatcher and by automatically generating a schedule the system becomes opaque to the human. The above mentioned weaknesses of current state-of-the-art decision support systems lead to the observation that the differences between the planning, scheduling and dispatching tasks are under estimated and that the link between the tasks is unclear. The task delineation model that has been presented in this paper aims to give researchers and practitioners that deal with production control task a clearer definition of planning, scheduling and dispatching tasks. A detailed description of an implementation of a decision support system that is based on the concepts presented in this paper is described in McKay & Wiers (2003).

6 Conclusion In this paper it has been stated that a clearer separation can be made between the planning, scheduling and dispatching tasks in production control. While job titles and organization charts may not be clear and individuals may do all three, it is possible to use time horizons, types of decisions, and the context to make the distinction. The action science and ethnographic case study described in this paper provided the mechanism for observing and participating in the situation. Software tools for planning, scheduling, and dispatching were created, some tasks were re-designed, and some production control

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structures were overhauled completely. It was from these observer and participant activities that the taxonomy proposed in this paper arose. The classification of tasks aims to deepen our understanding of production control, to help researchers in the human factors in production control, and to facilitate improvements of production control performance by supporting humans with adequate decision support tools.

Acknowledgements This research has been supported in part by NSERC grant OGP0121274 on Adaptive Production Control. The senior plant management and the individuals in the production control department are gratefully thanked for the support shown during the project.

List of Acronyms APS DSS EDI HPP MES MRP MRP II WIP

Advanced Planning & Scheduling Decision Support System Electronic Data Interchange Hierarchical Production Planning Manufacturing Execution System Material Requirement Planning Manufacturing Resource Planning Work-in-Process

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