Trends in Manufacturing: Planning & Control

INTERNATIONAL CONFERENCE ON ADVANCED AND AGILE MANUFACTURING SYSTEMS (ICAM-2015)

Trends in Manufacturing: Planning & Control Perspective Ajay Kumar Agarwal1 and Dr. Rakesh Kumar2 Abstract—While the technology of manufacturing - including processes and computer hardware and software - is improving rapidly, a basic understanding of the systems issues remains incomplete. These issues include production planning, scheduling, and control of work-in-process. They are complicated by randomness in the manufacturing environment (particularly due to machine failures and uncertainty and variability in production requirements). Production is the core process of every manufacturing organization, and so the efficiency and quality of decisions taken on the shop floor determines the performance of the organization’s quality management system. At planning level, a good production manager must be able to identify probable limitations in order to establish, prior to product realization processes, the appropriate preventive measures. The ultimate in every production scheduling solution must effect the provision and control of specified resources to accomplish the required manufacturing tasks with due considerations to right sequence of job methods and timing parameters. An optimized production scheduling process is therefore characterized by the availability of desired output of products in type and quantity within the planned time at minimum costs. The purpose of this paper is to present an interpretation of recent progress in manufacturing systems from the perspective of planning and control. Keywords: Production Planning, Scheduling and Cont

ö I. INTRODUCTION 1 The impact of automation on manufacturing and logistics can hardly be overestimated. Before focusing on planning and control, let us briefly delineate the full picture of changes in manufacturing practice that we have witnessed in the last three decades. In principle, we may distinguish three different fields: • Hardware automation (computer numerically controlled machines, flexible manufacturing and assembly systems, automatic transport systems, automated storage and retrieval systems), • Design and process planning (computer aided design, computer aided process planning, rapid prototyping techniques), • Manufacturing planning and control systems (materials requirements planning, manufacturing resources planning, optimized production technology, workload control systems). Besides, as a fourth impact factor with a primarily organizational background (although sometimes enabled by information technology), we mention: • System complexity reduction (just in time production, production flow analysis, cellular and 1Asstt.

Prof., Dep tt. o f Mechanical Engg., Manav Rachna University, Farid abad , Haryana, Ind ia 2Asso ciate Prof. & Head , Dep tt. o f Mechanical Engg., Shaheed Bhagat Singh State Technical Camp us, Fero zep ur, Punjab, Ind ia E-m ail: [email protected] u.in, 2rak esh1607@gm ail.com

team-based manufacturing, business process re-engineering). Business process re-engineering is included here although it addresses a much wider area: not only manufacturing but also many administrative processes in the service sector [1]. Some researchers debate the question whether just in time production is not merely another control procedure. that may be true but, as we will point out below, JIT control only leads to desired performance improvements in relatively simple manufacturing system structures. For that reason, we chose to list it under the fourth impact factor. More general, all approaches above have in common that they attempt to increase manufacturing capabilities, to support manufacturing planning and control, or sometimes to diminish the planning and control complexity. The ultimate goal is to improve system performance, not only in terms of efficiency and costs, but in particular on aspects of product and process quality, flexibility and speed [2], [3]. Production planning and production scheduling are often considered as two names for the same activity. This idea is erroneous, but understandable, since in many ways the two functions are quite similar. Both production planning and scheduling set the levels at which the production process will operate in the future; and both assign responsibilities for accomplishing the production job. The major differences between planning and scheduling lie in the time span covered by production plans and the amount of detail in the plan.

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International Conference on Advanced and Agile Manufacturing Systems (ICAM-2015)

Production planning involves setting production levels for several periods in the future and assigning general responsibility to provide data for making decisions on the size and composition of the labor force, capital equipment and plant additions, and planned inventory levels. The ability to meet demand levels generated by possible alternative sales programs is also a function of production planning. Production scheduling typically covers a much shorter time period than production planning. Production schedules determine how production required in the next several days or weeks will be assigned to specific departments, processes, machines, and operators in order to meet real deadlines imposed by the sales department and desired inventory levels. Whereas most production planning is concerned only with aggregate productive facilities such as "the packaging department," a production schedule must stipulate orders in more detail, using such units as "packaging line #1". In addition, in the strictest definition of the term, a production schedule should stipulate whether Mahesh or Sunil pushes the appropriate buttons on the appropriate machine. In actual practice, this decision is usually left to the foreman to make on the spot. Production control involves the constant readjustment of plans and schedules in the light of collected operating facts. Any production plan, and most production schedules, is based on some forecast of future demand, and the only certain element in any forecast is error. As new forecasts are made to account for recent sales and inventory positions, and apparent changes in future trends, production plans and schedules must be up-dated. Production control is somewhat analogous to maintaining the proper idling speed on an automobile engine. The car owner is faced with two related decisions how often to adjust the idling speed of his automobile, and, once he has decided to make an adjustment, how great an adjustment to make. A sports car enthusiast who tinkers with his car every Saturday morning need make only very minor adjustments on his carburetor since the car has had only one week to get out of adjustment. A more typical driver would readjust the carburetor only once a year, but at that time would make a fairly sizable adjustment. And so it goes with production control. A company can constantly revise and up-date forecasts and makes numerous adjustments in production plans, or it can make more sizable adjustments at less frequent intervals. II. MANUFACTURING PLANNING & CONTROL REFERENCE ARCHITECTURE Various authors have presented frameworks of a manufacturing planning and control architecture, almost exclusively hierarchical in nature (e.g. [4]; [5]; [6]). The reason for this hierarchy, representing aggregate decisions in an early stage while later disaggregating (similar to the decision structure in Hierarchical Production Planning, for

instance), is very natural; it reflects the increasing information that comes to or is gathered by the manufacturing organization as time progresses. Similarly, decomposition can often be naturally applied since many manufacturing organizations show a departmental or modular structure (often called production units [4]). However, when it comes to filling in the various modules of the architecture with algorithms or dedicated procedures, the literature is far less complete, and seems to concentrate primarily on mass production in make-tostock systems. For instance, the role of process planning that is so dominant in make-to-order companies, is almost entirely neglected in the literature. Local decision making is often based on decomposition but how to decompose a decision problem is far from clear, and depends heavily on the underlying production typology. Product and Process Design

Long Range Forecasting and Sales Planning

Facility and Resources Planning

Demand Management and Aggregate Capacity Planning Inventory Management and Materials Planning

Process Planning Job Planning and Resource Group Loading

Shop Floor Scheduling and Shop Floor Control

Purchase and Procurement Management

Fig. 1: A Manufacturing Planning & Control Architecture

III. FACTORS DETERMINING PRODUCTION PLANNING PROCEDURES The production planning used, varies from company to company. Production planning may begin with a product idea and a plan for the design of the product and the entire production/ operating system to manufacture the product. It also includes the task of planning for the manufacturing of a modified version of an existing product using the existing facilities. The wide difference between planning procedures in one company and another is primarily due the differences in the economic and technological condition under which the firms operate. The three major factors determining production-planning procedures are: A. Volume of Production The amount and intensity of production planning is determined by, the volume and character of the operation and the nature of the manufacturing processes. Production planning is expected to reduce manufacturing costs. The planning of production in case of custom order job shop is limited to planning for purchase of raw materials and components and determination of works centers, which have the capacity of manufacturing the product.

Agarwal et al.: Trends in Manufacturing: Planning & Control Perspective

B. Nature of Production Processes In job shop, the production planning may be informal and the development of work methods is left to the individual workman who is highly skilled. In high volume production, many product engineers are involved and they put enormous amount of effort in designing the product and the manufacturing processes. C. Nature of Operation Detailed production planning is required for repetitive operation. For example in case of continues production of a single standardized product. The variants in manufacturing approach are: • Manufacturing to order, this may or may not be repeated at regular intervals. • Manufacturing for stock and sell (under repetitive batch or mass production). Example: Manufacture of automobiles, watches, typewriters etc. IV. RESEARCH IN MANUFACTURING SYSTEM Early research in manufacturing systems can be found in the management science and operations research literature. Much of this was directed at production planning and scheduling problems. (Planning involves determining the aggregate resource requirements over a set of future time periods, while scheduling determines the detailed allocation of these resources to particular tasks for the immediate time period at hand. Thus planning often refers to decisions made high in the hierarchy and scheduling refers to low-level decisions). In particular, a great deal of the work on generative techniques for production scheduling and planning was concerned with the mathematical problem of fitting together the production requirements of a large number of discrete, distinct parts [7]. Such combinatorial optimization problems are very difficult in the sense that they often require an impractical amount of computer time. Furthermore, they are limited to deterministic problems so that random effects, including machine failures and demand uncertainties, cannot be analyzed. In order to deal with these practical difficulties, two alternative approaches have been explored. The first involves extensive investigation of heuristics for use in scheduling complex systems under realistic conditions. The second involves development of hierarchical approaches to solving these large problems. An excellent review of production scheduling approaches, both exact and heuristic methods, can be found in [8]. Typical hierarchical approaches are described in [9], and [10]. The early work on evaluative models was mainly an attempt to represent the random nature of the production process by using queuingtheoretic models. Most industrial engineers (IEs) today are taught the basics of single server queueing theory, such as the classic M/M/1 and M/G/1 queues [11]. Unfortunately, however, that is where most industrial engineering courses stop, and IEs today often have the impression of queuing models as esoteric, simple, and impractical.

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What is less well known in the manufacturing community is that the applicability of queuing theory to manufacturing was considerably enhanced by the development of network-of-queues theory by [12] and [13], coupled with the more recent development of efficient computational algorithms and good approximation methods. The state-of-the-art today allows for reasonable "first-cut" evaluative models of fairly complex manufacturing systems. Another early development in the area of evaluative models was the use of computer-based simulation methods, which employ a "Monte Carlo" approach to system evaluation. With the growing accessibility of computing power, the development of easy-to-use simulation packages, and the advancement of simulation theory, this area has made major strides forward recently. It is also an area "close" to control theory in many ways. V. NEW METHOD AND TRENDS IN PROCESS PLANNING Process planning and scheduling have a close link. The process plan involves the time order of manufacturing operations and information of workplace of process operation realization. The process plan is one of the significant inputs to the scheduling system. Following are three different approaches for the integration of process planning and workshop scheduling: • Dynamic process planning, • Just-in-Time process planning, • Non-linear process planning. A. Dynamic Process Planning The manufacturing of product is not realized according to forward planned process operations. The process operation sequence considering this approach is not known for the whole process at the beginning of part producing. This approach does not determine the complete operation sequence and the corresponding resource allocation at the same time. After each of finished operation the actual workshop situation is recognized and the best next operation and suitable resource is determined to continue manufacturing of this piece. Some higher level planning has to be carried out to ensure that this process planning approach does not frequently generate dead ends. Capacity Utilization of Production Machines and Tools

Data Base of Production Machine and Tools

PPS System

CAPP System

Production Planning and Control

Dynamic Process Plan

Fig. 2: Principle of Dynamic Process Planning

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B. Just-in-Time Process Planning Just-In-Time is well known and an effective and popular method for product planning and control. Just-In-Time process planning is started just before the first manufacturing step. It is a very good alternative for reusing previous process plans or creating process plans weeks before manufacturing. The new planning approach takes the actual workshop situation into account for decision making about the resources used for manufacturing a part. Advantages of this approach are that a well balanced workshop load can be achieved and it is not required to plan alternative routes in detail that are not used later. The result of this process planning is a conventional linear process plan that can easily be exchanged with existing MRP or workshop control systems. Disadvantages of this approach are that a process planning session is started for a complex part with many operations; the actual workload is hardly predictable. If the load with is a mix of complex parts and simpler parts, it is not possible to achieve a planning optimum. Also the re-use of process planning information from previous manufacturing is not possible as the individual part may be manufactured each time in a different way. C. Non-linear Process Planning A basis for the new approach is linear process planning which also includes manufacturing alternatives or possible changes in manufacturing sequence. Several alternative routings or sequences of operations are combined in a net structure. Netted process plans are called non-linear process plans. The required initial process planning effort is high. The non-linear process plan gives full flexibility to optimally load resources and also to reallocate jobs in case of unforeseen disturbances.

Manufacturing Operation

Many notions from control theory are relevant here, although their specific realization is quite different from more traditional application areas. The standard control theory techniques do not apply: we have not yet seen a manufacturing system that can be usefully represented by a linear system with quadratic objectives. This is not surprising; standard techniques have been developed for what have been standard problems. Manufacturing systems can be an important area for the future of control; new standard techniques will be developed. Some central issues in manufacturing systems include complexity, hierarchy, discipline, capacity, uncertainty, and feedback. Important notions of control theory include state and control variables, the objective function, the dynamics or plant model, and constraints. In this section, we describe the relevance of these notions to the manufacturing context for readers whose primary background is in control and systems. A. Complexity Manufacturing systems are large-scale systems. Enormous volumes of data are required to describe them. Optimization is impossible; suboptimal strategies for planning based on hierarchical decomposition are the only ones that have any hope of being practical. B. Hierarchy There are many time scales over which planning and scheduling decisions must be made. The longest term decisions involve capital expenditure or redeployment. The shortest involve the times to load individual parts, or even robot arm trajectories. While these decisions are made separately, they are related. In particular, each long-term decision presents an assignment to the next shorter term decision maker. The decision must be made in a way that takes the resources – i.e., the capacity - explicitly into account. The definition of the capacity depends on the time scale. For example, short time-scale capacity is a function of the set of machines operational at any instant. Long timescale capacity is an average of short time-scale capacity. 1) Machine-Level Control

Fig. 3: Principle of Non-linear Process Plan

VI. CONTROL PERSPECTIVE The purpose of manufacturing system control is not different in essence from many other control problems: it is to ensure that a complex system behaves in a desirable way.

At the very shortest time scale is the machine-level control. This includes the calculation and implementation of optimal robot arm trajectories; the design of “ladder diagrams” for relays, micro-switches, motors, and hydraulics in machine tools; and the control of furnaces and other steps in the fabrication of semiconductors. Other short scale issues include the detailed control of a cutting tool: in particular, adaptive machining. There is no rule that determines exactly what this shortest time scale is. A robot arm movement can take seconds while a semiconductor oxidation step can take hours. The issue at this time scale is the optimization of each individual operation. Here, one can focus on minimizing the time or other cost of each separate movement or transformation of material.

Agarwal et al.: Trends in Manufacturing: Planning & Control Perspective

One can also treat the detailed relationships among operations. Other control problems at this time scale include the detection of wear and breakage of machine tools, the control of temperatures and partial pressures in furnaces, the automatic control of the insertion of electronic components into printed circuit boards, and a vast variety of others. 2) Cell Level At the next time scale, one must consider the interactions of a small number of machines. This is cell-level control and includes the operation of small, flexible manufacturing systems. The important issues include routing and scheduling. The control problem is ensuring that the specified volumes are actually produced. At this level, the detailed specifications of the operations are taken as given. In fact, for many purposes, the operations themselves may be treated as black boxes. The issue here is to move parts to machines in a way that reduces unnecessary idle time of both parts and machines. The loading problem is choosing the times at which the parts are loaded into the system or sub-system. The routing problem is to choose the sequence of machines the parts visit, and the scheduling problem is to choose the times at which the parts visit the machines. The important considerations in routing include the set of machines available that can do the required tasks. It is often not desirable to use a flexible machine to do a job that can be done by a dedicated one, since the flexible machine may be able to do jobs for which there are no dedicated machines. In scheduling. one must guarantee that parts visit their required machines while also guaranteeing that production requirements are met. At this level, the issue is allocating system resources in an efficient way. These resources include machines. transportation elements, and storage space. A control problem at this level is to limit the effect of disruptions on factory operations. Disruptions are due to machine failures, operator absences, material unavailability, surges in demand, or other effects that may not be specified in advance but which are inevitable. This problem may be viewed as analogous to the problem of making an airplane robust to sudden wind gusts, or even to loss of power in one of three or more engines. 3) Factory Level At each higher level, the time scale lengthens and the area under concern grows. At the next higher level, one must treat several cells. For example, in printed circuit fabrication. the first stage is a set of operations that prepares the boards. Metal is removed, and holes are drilled. At the next stage, components are inserted. The next stage is the soldering operation. Later. the boards are tested and reworked if necessary. Still later, they are assembled into the product. This process takes much time and a good deal of floor space.

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Issues of routing and scheduling remain important here. However, setup times become crucial. That is, after a machine or cell completes work on one set of parts of the same or a smaller number of types. it is often necessary to change the system configuration in some way. For example, one may have to change the cutters in a machine tool. In printed circuit assembly, one must remove the remaining components from the insertion machines and replace them with a new set for the next set of part types to be made. The scheduling problem is now one of choosing the times at which these major setups must take place. This is often called the tooling problem. Other issues are important at still longer time scales. One is to integrate new production demands with production already scheduled in a way that does not disrupt the system. Another class of decisions is that pertaining to medium-term capacity, such as the number of shifts to operate and the number of contract employees to hire for the next few months. Another decision, at a still longer time scale. is the expansion of the capital equipment of the factory. At this time scale. one must consider such strategic goals as market share. sales, product quality, and responsiveness to customers. C. Discipline Specified operating rules are required for complex systems. Manufacturing, communication, transportation, and other large systems degenerate into chaos when these rules are disregarded or when the rules are inadequate. In the manufacturing context, all participants must be bound by the operating discipline. This includes the shop-floor workers. who must perform tasks when required, and managers. who must not demand more than the system can produce. It is essential that constraints on allowable control actions be imposed on all levels of the hierarchy. These constraints must allow sufficient freedom for the decision makers at each level so that choices that are good for the system as a whole can be made. but they must not be allowed to disrupt its orderly operation. D. Capacity An important element in the discipline of a system is its capacity. Demands must be within capacity or excessive queuing will occur, leading to excessive costs and possibly, to reduced effective capacity. High-level managers must not be allowed to make requirements that exceed their capacity on their subordinates; subordinates must be obliged to accurately report their capacities to those higher up. All operations at machines take a finite amount of time. This implies that the rate at which parts can be introduced into the system is limited. Otherwise. parts would be introduced into the system faster than they could be processed. These parts would then be stored in buffers (or worse, in the transportation system) while waiting for the machines to become available, resulting in undesirably large work in process and reduced effective capacity. The

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effect is that throughput (parts actually produced) may drop with increasing loading rate when loading rate is beyond capacity. Thus, defining the capacity of the system carefully is a very important first step for on-line scheduling. An additional complication is that manufacturing systems involve people. It is harder to measure human capacity than machine capacity, particularly when the work has creative aspects. Human capacity may be harder to define as well. since it can depend on circumstances such as whether the environment is undergoing rapid changes. Defining, measuring, and respecting, capacity are important at all levels of the hierarchy. No system can produce outside its capacity. and it is futile. at best, and damaging. at worst. to try. On the other hand, it may be possible to expand the capacity of a given system by a learning process. This is a goal of the Japanese just-intime (JIT) approach, which takes place over a relatively long time scale. It is essential, therefore. to determine what capacity is. then to develop a discipline for staying within it, and finally to expand it. E. Uncertainty All real systems are subject to random disturbances. The precise time or extent of such disturbances may not be known, but some statistical measures are often available. For a system to function properly, some means must be found to desensitize it to these phenomena. Control theorists often distinguish between random events and unknown parameters, and different methods have been developed to treat them. In a manufacturing system, machine failures, operator absences, material shortages, and changing demands are examples of random events. Machine reliabilities are examples of parameters that are often unknown. Desensitization to uncertainties is one of the functions of the operating discipline. In particular, the system’s capacity must be computed while taking disturbances into account, and the discipline must restrict requirements to within that capacity. The kinds of disturbances that must be treated differ at different levels of the time-scale hierarchy: at the shortest time scale, a machine failure influences which part is loaded next; at the longest scale, economic trends and technological changes influence marketing decisions and capital investments. It is our belief that such disturbances can have a major effect on the operation of a plant. Scheduling and planning must take these events into account, in spite of the evident difficulty in doing so.

Control engineers know that designing good feedback strategies is generally a hard problem. It is essential, especially at the short time scale, that these decisions are calculated quickly and be relevant to long-term goals. The trade-off between optimality and computation gives rise to many interesting research directions. VII. CONCLUSION We have described a framework for many of the important problems in manufacturing systems that need the attentions of people trained in planning, control and systems theory. We have shown how existing practical methods solve those problems, and where they fall short. We have also shown how recent and on-going research fits into that framework. An important goal of this effort has been to encourage planning and control theorists to make the modeling and analysis efforts that will lead to substantial progress in this very important field. ACKNOWLEDGMENT This research was supported by S.B.S.S.T.C, Ferozepur (Punjab). I thank our colleagues from Manav Rachna University, Faridabad who provided insight and expertise that greatly assisted the research, although they may not agree with all of the interpretations/conclusions of this paper. I thank Dr. Rakesh Kumar, Associate Prof. & Head of ME Deptt. at S.B.S.S.T.C, Ferozepur (Punjab) for assistance, for their so-called insights of reviewer and for comments that greatly improved the manuscript. REFERENCES [1] [2] [3]

[4]

[5]

[6] [7]

[8] [9]

[10]

F. Feedback In order to make good decisions under uncertainty, it is necessary to know something about the current state of the system and to use this information effectively. At the shortest time scale, this includes the conditions of the machines and the amount of material already processed.

[11] [12] [13]

Hammer M, Champy J (1993) Reengineering the Corporation. HarperCollins, New York. Deming, W. E., Quality, Productivity, and Competitive Position, MIT Center for Advanced Engineering Study, Cambridge, MA, 1982. Blackburn, J. D., Time-Based Competition, The Next Battle Ground in American Manufacturing, Richard D. Irwin, Homewood, IL, 1991 Bertrand JWM,Wortmann JC,Wijngaard J (1990) Production control: a structural and design oriented approach. Elsevier, Amsterdam Vollmann TE, Berry WL, Whybark DC (1997) Manufacturing planning and control systems, 4th edn. Irwin/McGraw-Hill, New York Hopp WJ, Spearman ML (1996) Factory physics: foundations of manufacturing management. Irwin, Homewood, IL Dzielinski, B. P. and Gomory, R. E., "Optimal Programming of Lot Sizes, Inventory and Labor Allocations", Manag. Sci. Vol. 11, No. 9, 874-890, July 1965. Graves, S. C., "A Review of Production Scheduling, " Operations Research, Vol. 29, No. 4, 1981, pages 646-675. Hax, A. C. and Meal, H. C., "Hierarchical Integration of Production Planning and Scheduling, "In: Geisler MA (ed) Logistics (Studies in the Management Sciences, Vol 1). Elsevier, North-Holland, 975 Graves, S. C., "Using Lagrangean Techniques to Solve Hierarchical Production Planning Problems, " Management Science, Vol. 28, No. 3, March 1982, pages 260-275. Kleinrock, L., Queueing Systems, Vol. I, John Wiley, New York 1975. Jackson, J. R., "Jobshop-like Queueing Systems", Manag. Sci., 10, No. 1, October 1963, 131-142. Gordon, W. J. and G. F. Newell, "Closed Queueing Systems with Exponential Servers", Op. Res. 15, No. 2, April 1967, pp. 254 -265