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A SIMULATION TESTBED FOR DECISION SYSTEM EVALUATION IN A FURNITURE MANUFACTURING GROUP
A SIMULATION TESTBED FOR DECISION SYSTEM EVALUATION IN A FURNITURE MANUFACTURING GROUP Thomas Klein, André Thomas Research Center for Automatic Control, Nancy, France {Thomas.Klein | Andre.Thomas}@cran.uhp-nancy.fr
Abstract: In this paper, we present the result of a cooperative work between a furniture manufacturer and the Tracilog1 team belonging to the Research Center for Automatic Control, in Nancy. This work led to develop a simulation test bed, enabling both validation and improvement of the currently used planning process, and testing of new control structures, emerging from research works. A cutting plan evaluation tool and a workshop scheduling evaluation tool are presented. The main objective of the developed testbed was to obtain a better control of lead times and work in process. Finally, the modelling structure, enabling to use different control system, is evoked. Copyright © 2006 IFAC Keywords: simulation, production control, decision support system, centralised control, distributed control.
1. CONTEXT OF THIS WORK This work relies on a partnership between one of the most important French furniture manufacturer, on the one hand, and the Research Centre for Automatic Control in Nancy, on the other, more particularly the TRACILOG1 team, which focuses on implementing traceability and improving supply chain management in fiber industries. One of the objectives of this team is to provide a benchmarking tool enabling a comparative study of different control systems, like Holonic Manufacturing Systems (HMS) or ProductDriven Manufacturing systems. The involved industrial group, which is quite fully vertically integrated, aims at optimising flow control along its supply chains, by an efficient management of its decoupling points. First, the production system, including physical workshop and the planning system, has been developed to answer to a mass production context, that is to say to produce large quantities per lot for 1
Traceability and management of the supply chain in fibers industries (wood, paper, textile)
few part numbers. Secondly, clients ask for more and more customized products, at lowest cost and in shortening delays. To face these growing requirements, the company deploys several new tools, like autonomous teams management or simulation tools, in order to improve flexibility and reactivity of the production system. The studied workshop produces sizeable volume of many different products. A decoupling point has been created to face management problems, and the production control is different before or after this point: • Before the decoupling point, the bottleneck Cutto-Size machine is a batch process, and the optimisation implies significant volumes, • After the decoupling point, the workshop transformation process is managed by orders, using a classical MRP2 planning process. The control of such a decoupling point faces two problems, which are to bring the Work In Process (WIP) between two predefined critical limits and to control lead times. The studied system is a panel board, ready-toassemble furniture production system. Furniture’s manufacturing rely on a composition of three main
functions: first, large panels are cut into pieces; secondly, pieces receive some transformations, like drilling or grooving, and then finished pieces are assembled into a package, which will be brought to the final customer through the distribution system. The cutting operations are particularly awkward to manage because of the important cadence variability. This fact could be explained by the following reasons. The panel price being one of the most significant costs constituting the unit cost, a special process optimises the raw material use (ProfiCoupe2). So as to reduce cutting scraps, the produced quantity varies for each batch: with the aim of optimising material use, the production of 400 desks could lead to produce 401 desktop and 404 left bases. The next session for this reference will take the remainder into account, which changes quantities for every components, so cutting plans will be really different. Moreover, the Cut-toSize productivity curve is non-linear: the operating time to produce 800 pieces is not equal to the double of the time to produce 400 pieces. After this cutting process, pieces are manufactured to become finished pieces. The workshop is constituted of several machines, each one able to perform some operations. The variety of different pieces, the significant volume produced and the important number of lots being processed simultaneously generate complexity of physical flow, which makes their control difficult. In that context, the production management follows traditional MRP2 rules (Vollman et al, 1988): a Sales and Operation Planning is generated according to commercial forecasts by family of products, and then is disaggregated into Master Production Schedules. The next step consists in computing the Material Requirements Plan and in balancing workloads on work centres by establishing a predictive finite capacity planning for each of them. This step enables to establish raw materials orders and finished furniture’s delivery dates, which appears as constraints to be respected for the lower level. The recently acquired scheduling tool (Ortems Production Scheduler3) aims at optimising resources usage rate. But its implementation reveals some imperfections: • To be pertinent, a scheduling must be based on accurate technical data, • This scheduling tool does not reflect variability and dynamics of the actual process, • This scheduling tool does not take into account WIP level and its impact on productivity. Consequently, we decide to focus on WIP and lead time control. Studying these two major objectives imply to take the system dynamic into account. In that sense, simulation appeared like a really adapted tool to improve physical flow management. In this paper, we will present the use of simulation done by a furniture-manufacturing group. In the next part, we will present classical simulation uses in 2
Proficoupe is distributed by Homag. Ortems Production Scheduler is distributed by Ortems. 3
industry. Part three focuses on the Cut-to-Size tool simulator, then, part four illustrates the use of simulation in workshop control. The fifth part explains how the modelling framework allows testing different control systems, with more or less distribution of the decision power, using the same case study. Finally, we conclude and invoke open issues and possible improvement of these uses. 2. SIMULATION AND INDUSTRIAL USES In this section, we briefly describe some industrial uses of simulation in order to place our work. Simulation has already been used for a long time (Zeigler, 1976) in most industrial cases and shown its ability to solve some locking problems, hard or impossible to compute with classical tools. The basic principle of simulation is the construction and use of a computer-based representation, or model, of some part of the real world as a substitute vehicle for experiment and behaviour prediction (Hollocks, 1995). Simulation tools present several applications in the manufacturing domain: • Design and sizing of new structures, • Study of physical modifications impact, • Study of new management rules, their viability and impact, • Production facilities control. Simulation techniques have been used for a long time in designing of new structures (Reitz, 1985)(Kuwada et al, 1986) or in studying the impact of some modifications on an existing one. One of the major interests in simulation is in the ability to test some new control structures in order to validate them before a possible real use. For example, the introducing of a Kanban subsystem into a MRP Controlled manufacturing Environment (De Smet et al, 1998) or the reducing of lot-sizes (Habchi et al, 1995). Simulation is also used to control production facilities. We can consider two major kinds of approaches: in the first case, the simulation is used to validate a Master Production Schedule (Thomas et al, 2005)(Pritsker et al, 1994)(Belz et al, 1996), generated by hand or by another scheduling tool, and in the second one, the simulation tool is finely coupled with the scheduling tool and the elaborated solution could have done many loops between optimiser and simulator (Allaoui et al, 2004). In this domain, we could note some benchmark practices, like the TEXSIM project, which consisted in the development of an integrated tool able to generate and exploit simulation model function of input data from the user, about woven fabric manufacture (Rotab Khan et al, 1999). Objectives of our models are, on the one hand, to improve planning process by adjusting and expanding technical data, and on the other hand, to build a tool enabling to highlight current control system drawbacks and to test, tune and validate some others approaches. To meet such objectives implies to integrate simulation tools with the industrial information system, and also to use flexible
modelling rules, allowing modifying control rules without changing the physical process model. To develop our model, we used the piece of software Arena4, because its discrete-event simulation is based on the well-tried SIMAN language, which is used as industrially than academically (Al-Ahmari et al, 1999)(Perera et al 2000)(Kovács et al, 1999).
of Cut-to-Size simulator in the global enterprise information system is shown on Fig. 4. Particle Panels Loading Particle Panels Panels Stacking
Stacks of Panels
3. CUT-TO-SIZE SIMULATION As explained in section 2, the Cut-to-Size tool has variable operating times, which depend on many parameters. Indeed, the cutting plan influences the operating time due to the machine architecture, when the theoretic time, used for scheduling and for raw material ordering, is given by a formula based on constant throughput. The machine is seen as a "black box". Nevertheless, there is a decoupling point into the internal process: Stacks of big panels are longitudinally cut into strips. Every strip of one packet doesn' t have the same cutting plan, consequently there is an internal inventory enabling to group them. After that, they are transversally cut by packet to obtain pieces at the final size. This process is explained on Fig. 1 This figure shows also the synoptic of the simulation model. Due to complexity of the grouping rules of strips and stacks of strips, we had to use Visual Basic code Blocks to model the system. As an input for the simulator, we used four files, generated by the raw material optimisation piece of software ProfiCoupe (Proficoupe transmit data about cutting plan to the machine using several files, four of them are useful for us). To ensure the independence face to this optimisation tool, we chose to use these files sent to the machine. Indeed, files sent to the machine will always stay identical even if the optimisation tool is upgraded and its working way changes. These files contain all relevant information about the cutting operation: • Numbers and types of panels in input, • Dimensions and quantities for each strip type, • Dimensions and quantities for each piece type, • Longitudinal machining times, • Particular operations. All information are aggregated using an Excel file and VBA Macros, and are used to generate the Arrival module. Each entity generated is representative of a panel, with its pre-affected cutting plan: that is to say we know at the beginning of the process how many strips and how many pieces are going to be done in each panel. As an output, another Excel file is generated, including for each run its identifier, the total expected time, and the number of pieces stacks evacuated. This last parameter is useful to evaluate the load of output operators. This output file is used to update the technical database, which enables to compute the detailed schedule using realistic data. The integration 4
Arena is distributed by Rockwell Software.
Longitudinal sawing
Stacks of strips Intermediate Grouping point Stacks of strips Strips unstacking
Strips Longitudinal machining
Machined strips
To control Cut-to-Size Center
Cutting Plans
Strips grouping point
Stacks of strips Transverse sawing
Stacks of Pieces Output operator
Fig. 1. Cut-to-Size Centre Synoptic In terms of validation, the major problem came from the natural variability of the process. As evoked in section 1, two replications of a same cutting plan could lead to obtain variable operating times. But a statistical study shown that this operating times distribution follows a normal law, and the simulation results are less than five percent different from the real time, with an average difference of 2.4 %. Fig. 2 shows for a panel of runs the difference between times and throughputs realised on the Cut-toSize Centre, times given by the simulation and times used before to compute the schedule. We could notice that the simulation time is very often more realistic than the scheduled one. Such an observation lead us to affirm that using the simulated time to compute the schedule will be more relevant than to carry on the use of the previous time computing method. Variations are due to the stacking cadence, which is a man made operation, and consequently subject to uncontrollable parameters, like operator tiredness for example. The major impact of using such a simulation tool is the ability to obtain realistic technical data, which enable several new practices in the production management: • The use of the scheduling tool has been improved by the accuracy of input data, • The raw material responsible has now a vision of the cutting plan impact on productivity. Moreover, the control of the Cut-to-Size machine, which is the bottleneck, has been modified : for the more frequent products, typical cutting plan have been established, taking into account raw material scraps, but also the machine productivity.
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average batch size is about 400 units, which implies several important batches at the same time in the workshop. The end of the process is the packaging line, where pieces have to be grouped by furniture reference. Of course, all the components belonging to the finished product bill of materials have to be completed before packaging. Three intermediate stocks enable to buffer physical flow between machines. These inventories are “rolling stocks”, equivalent to drawer stocks. The nature of these stocks leads to more complex control. Indeed, there are several FIFO stacks, and it is only possible to catch the first lot of each drawer. The Inventory 1 contains items that are just cut (Capacity = 164 lots), when Inventory 2 stores in progress items (Capacity = 180 lots) and Inventory 3 stores finished and ready to package pieces (Capacity = 510 lots).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
(b)
Production Cell 1
Run identifier
Machine 1 Machine 2
Fig. 2. Difference between real, simulation and old scheduled times (a) and outflows (b). Previously, the machine' s productivity couldn’t be quantified, due to the inaccuracy of technical data. The use of simulation enables us to compute it, and so, to evaluate the impact of some productivity improvement actions. The new accuracy of every technical data about the workshop, and the benefits from the Cut-to-Size simulator, let us envisage the positive impact of a global workshop simulator. 4. WORKSHOP SIMULATION The main objective of the implementation of the scheduling tool was to optimise the use of resources. We explained in section 3 how the development of a Cut-to-Size simulator enabled to use accurate data, and so, to establish relevant detailed schedules. But, in spite of the accuracy of technical data, the interval between schedule and reality was not satisfying. One of the identified causes of this difference comes from uncontrollable parameters, as failures or scraps. Some others causes could include potentially controllable parameters, like WIP level or process time variability. Such parameters are linked to process dynamic, which isn’t taken into account in the static scheduling tool. Simulation is a typical answer for this kind of problems. In this context, a simulation model representative of the whole workshop has been developed. The case study is based upon the workshop of the same company (Fig. 3). This workshop is composed of eight machines, organised in production cells. The first machine is the Cut-to-Size centre presented in previous section. What particularly interests us is the following drilling and grooving machines. Each one has particular features, and is able to achieve several operations. Obviously, each resource could have a different calendar according to its planned load for the week. Consequently, there are many possible operation sequences, and the product diversity makes physical flows and their control complex. Moreover,
Big panels
Machine 3
Cutting up
Inv.1
Inv.2
Inv.3
Packaging line
Kit Furnitures
Machine 4
Production Cell 2
Machine 5 Machine 6
Fig. 3. Physical flow in the workshop This workshop is managed using a predictive scheduling, that is to say a schedule is generated according to the finished goods need, and is periodically actuated in accordance with production progress and workshop state (Master Production Schedule – MPS). This scheduling tool is fed by results from the centralised technical database, updated by the Cut-to-Size simulator. The aim of developing a workshop simulation model is to evaluate ‘a priori’ the evolution of Work-InProgress (WIP) inventory level, to maintain it in an area that will minimize its negative impact on resource productivity. To generate such information, the simulation model needs some input data: • The predictive machine planning, • The machine’s calendars, • Performance parameters. These information are provided by the information system. The performance parameters are stored in the technical database, when the predictive machine planning and calendars are given in the detailed scheduling. The workshop simulator plays the detailed schedule, given by the scheduling tool, integrating failure and variability rule, in order to validate the feasibility, and to have a foreseen on WIP level evolution. Corrective actions are not automated, the loop is closed by the scheduler responsive. The integration of the simulation tool in the global information system is shown on Fig.4 (The detail of workshop simulator structure is given on Fig.6).
!
Fig. 4. Simulation tools integration
Inventory 1 Simulated
600 500
Inventory 1 Real
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Inventory 2 Simulated
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On the other hand, the workshop simulator is used like a training tool, what enables to considerably cut back the training time for a new scheduling operator, and to reduce problems while releasing new orders. The daily use of the workshop simulator let foresee an enlarged use of it, by validating some modifications of control rules before to implement them in the workshop if their impact are relevant. One of the concrete uses of the simulation was to decide how much operators have to be allocated to transport operations, or to choose the best location to install some new stocking structures. Moreover, this initially unforeseen use needs, ,the intervention of a specialist, when quickly educated users could do the daily use. The impact of major management rules could be studied too. For example, currently, the typical lot size is about four hundred pieces, but the growing need for reactivity incites to reduce this lot size. Before to act some projects like SMED5, the workshop simulator will enable to validate the impact of such a modification on the WIP volume. But the ability to test some new management rules drove to new needs, more and more complex and fastidious to apply for the model designer. This observation led us to develop a new modelling architecture, separating, on the one hand, the physical controlling system and, on the other hand, the control system, interfaced by an informational system according to the system theory (Le Moigne, 1984). 5. DISTRIBUTED DECISION SYSTEM REPRESENTATION
Inventory 2 Real
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The simulation model has been validated following a two steps process: first, functional properties have been validated, and in a second time, stochastic parameters have been identified using a statistic study. Functional properties integrate: • Any lot follows the way corresponding to the real machining sequence (routings), • When more than one lot is available in front of a machine, the schedule is respected, • Machine calendars are observed. Moreover, some alerts have been developed. These alerts generate a log file each time an error appears, which enable us to track the error. Machining process being more standard than the cutting one, the characterization of their stochastic parameters has been done analysing previously recorded times for each machine. According to a 24 to 48 hours horizon, we were able to show the reliability of our system. Effectively, if the schedule is not or few changed, the forecast is relevant, but if the schedule is completely changed, due to commercial constraints for example, the forecast error grows up quickly (See comparative between real and simulated inventory on Fig.5).
Inventory 3 Simulated Inventory 3 Real
Fig. 5. Real and simulated inventory evolution Opposite to the Cut-to-size simulation tool, the reverse loop from the simulator to the informational system is manual: that is to say the simulator generates a report, which is analysed and used by the scheduling operator. This way of establishing a schedule enables it to react in order to maintain the inventory level between efficiency boundaries. The daily use allowed improving the scheduling rules. The results are generated with a period of four hours: the global evolution is presented with curves, like on 5. This global evolution enables us to establish what are the interesting areas and then to obtain the detailed content of WIP to engage some corrective actions.
In the one hand, we want to represent actual situation, where operators in the workshop take many decisions with a distributed way. On the other, we want to validate some innovative way of controlling a workshop. Consequently, we specified a modelling framework enabling to clearly separate physical flow model (called Emulation model) from control model (called Control model) (Habchi et al, 2003). The structure used is shown on Fig.6. According to the system theory, the production system is shown like a set of operations, each applying a transformation to the product, which could be space (moving), shape (machining) or time (storage).
5
Single Minute Exchange of Die
REFERENCES
Fig. 6. Simulation Model Structure Every transformation done to the product has to be represented in emulation model and controlled in decision model. To develop a global model representative of the real system, we built a library of components for each transformation type. Any component is composed of an emulation part, which is implemented by an Arena block, and a decision (control) part, which could be implemented in any Arena-friendly language. We developed this part using Visual Basic for Application and some Excel workbooks. Any operation in the emulation model is preceded and followed by synchronisation points, which are in charge of linking both models. The originality of this conception is that no decision is taken in the emulation level: on each point where a decision has to be taken, a synchronisation procedure is launched, and the decision process is computed in the control model, before to be transposed on the emulation model via tuning methods. Using such a structure will enable us to test several control systems, where the decision power could be mainly centralised or mainly distributed. More details about the modelling structure could be found in further works. 6. CONCLUSION AND OPEN ISSUES In this paper we showed several uses of simulation done in a furniture-manufacturing firm. When the first Cut-to-Size simulation tool is quite specific to the machine used, the workshop simulator is useful for any other industrial case with similar structure. We can conclude by telling that the major simulation impact is the ability to foresee what will happen in order to anticipate on events or to avoid them. Today, the planner-scheduler uses our tools to evaluate and establish the MPS. Following improvements have already been observed: • We couldn' t, at that time, give a quantitative evaluation of the impact on lead-time, but the company could now more accurately forecast them before to accept an order. • Critical WIP levels are less frequently reached. The Cut-to-Size simulator computing time is about one hour for one week of work, when the workshop simulator computes about one or two days of work in about ten minutes. The next steps for this work are to specify and develop a new control system, with the aim of associating global optimisation from centralised control system and reactivity from distributed ones. The simulation model will enable us to tune and to validate such an approach before any industrial use.
Al-Ahmari A.M.A and Ridgway K. (1999). An integrated modelling method to support manufacturing system analysis and design. Computers in Industry 38, pp. 225–238. Allaoui H., Artiba A. (2004), Integrating simulation and optimization to schedule a hybrid flow shop with maintenance constraints, Computers & Industrial Engineering,47, pp 431–450. Belz R., Mertens P. (1996), Combining knowledgebased systems and simulation to solve rescheduling problems, Decision Support Systems 17 pp 141–157. De Smet R., Gelders L. (1998). “Using simulation to evaluate the introduction of a Kanban subsystem within an MRP-controlled manufacturing environment”, Int. J. Production Economics 56-57 111-122. Kovács G.L., S. Kopácsi, J. Nacsa, G. Haidegger and P. Groumpos (1999). Application of software reuse and object-oriented methodologies for modelling and control of manufacturing systems. Computers in Industry 39 (3), pp 177-189. Kuwada H, Akira H (1986). Simulation of weaving operation and determination of optimal number of operating machines. Journal of the Textile Machinery Society of Japan; 32(1)27. Habchi G., Labrune C. (1995). Study of lot-sizes on job shop systems performance using simulation, Simulation Practice and Theory 2, pp 277-289. Habchi G., Berchet C. (2003). A model for manufacturing systems simulation with a control dimension, Simulation Modelling Practice and Theory 11, pp 21-44. Hollocks, B.W. (1995), The impact of simulation in manufacturing decision making, Control Engineering Practice, Vol. 3, No. 1, pp. 106-112. Le Moigne J-L., (1984). « La théorie du système général », Presses Universitaires de France. Perera T. and Liyanage K. (2000). Methodology for rapid identification and collection of input data in the simulation manufacturing systems. Simulation Practice and Theory 7, pp. 645–656. Pritsker A., Snyder K. (1994). Simulation for planning and scheduling, APICS. Reitz, F. (1985). Better Facilities Quicker with Simulation. Manufacturing Systems, October, pp 24-27. Rotab Khan M.R., Harlock S.C., Leaf G.A.V, (1999). “Computer simulation of production systems for woven fabric manufacture” Computers & Industrial Engineering 37, 745-756. Thomas A., Charpentier P. (2005), Reducing simulation model for scheduling manufacturing facilities, European Journal of Operational Research, 161, pp 111-125. Vollman, T., Berry, W., Whybark, D. (1988). Manufacturing Planning and Control Systems, Irwin,. Zeigler B.P. (1976), Theory of Modelling and Simulation, New York, Wiley.
Abstract: In this paper, we present the result of a cooperative work between a furniture manufacturer and the Tracilog1 team belonging to the Research Center for Automatic Control, in Nancy. This work led to develop a simulation test bed, enabling both validation and improvement of the currently used planning process, and testing of new control structures, emerging from research works. A cutting plan evaluation tool and a workshop scheduling evaluation tool are presented. The main objective of the developed testbed was to obtain a better control of lead times and work in process. Finally, the modelling structure, enabling to use different control system, is evoked. Copyright © 2006 IFAC Keywords: simulation, production control, decision support system, centralised control, distributed control.
1. CONTEXT OF THIS WORK This work relies on a partnership between one of the most important French furniture manufacturer, on the one hand, and the Research Centre for Automatic Control in Nancy, on the other, more particularly the TRACILOG1 team, which focuses on implementing traceability and improving supply chain management in fiber industries. One of the objectives of this team is to provide a benchmarking tool enabling a comparative study of different control systems, like Holonic Manufacturing Systems (HMS) or ProductDriven Manufacturing systems. The involved industrial group, which is quite fully vertically integrated, aims at optimising flow control along its supply chains, by an efficient management of its decoupling points. First, the production system, including physical workshop and the planning system, has been developed to answer to a mass production context, that is to say to produce large quantities per lot for 1
Traceability and management of the supply chain in fibers industries (wood, paper, textile)
few part numbers. Secondly, clients ask for more and more customized products, at lowest cost and in shortening delays. To face these growing requirements, the company deploys several new tools, like autonomous teams management or simulation tools, in order to improve flexibility and reactivity of the production system. The studied workshop produces sizeable volume of many different products. A decoupling point has been created to face management problems, and the production control is different before or after this point: • Before the decoupling point, the bottleneck Cutto-Size machine is a batch process, and the optimisation implies significant volumes, • After the decoupling point, the workshop transformation process is managed by orders, using a classical MRP2 planning process. The control of such a decoupling point faces two problems, which are to bring the Work In Process (WIP) between two predefined critical limits and to control lead times. The studied system is a panel board, ready-toassemble furniture production system. Furniture’s manufacturing rely on a composition of three main
functions: first, large panels are cut into pieces; secondly, pieces receive some transformations, like drilling or grooving, and then finished pieces are assembled into a package, which will be brought to the final customer through the distribution system. The cutting operations are particularly awkward to manage because of the important cadence variability. This fact could be explained by the following reasons. The panel price being one of the most significant costs constituting the unit cost, a special process optimises the raw material use (ProfiCoupe2). So as to reduce cutting scraps, the produced quantity varies for each batch: with the aim of optimising material use, the production of 400 desks could lead to produce 401 desktop and 404 left bases. The next session for this reference will take the remainder into account, which changes quantities for every components, so cutting plans will be really different. Moreover, the Cut-toSize productivity curve is non-linear: the operating time to produce 800 pieces is not equal to the double of the time to produce 400 pieces. After this cutting process, pieces are manufactured to become finished pieces. The workshop is constituted of several machines, each one able to perform some operations. The variety of different pieces, the significant volume produced and the important number of lots being processed simultaneously generate complexity of physical flow, which makes their control difficult. In that context, the production management follows traditional MRP2 rules (Vollman et al, 1988): a Sales and Operation Planning is generated according to commercial forecasts by family of products, and then is disaggregated into Master Production Schedules. The next step consists in computing the Material Requirements Plan and in balancing workloads on work centres by establishing a predictive finite capacity planning for each of them. This step enables to establish raw materials orders and finished furniture’s delivery dates, which appears as constraints to be respected for the lower level. The recently acquired scheduling tool (Ortems Production Scheduler3) aims at optimising resources usage rate. But its implementation reveals some imperfections: • To be pertinent, a scheduling must be based on accurate technical data, • This scheduling tool does not reflect variability and dynamics of the actual process, • This scheduling tool does not take into account WIP level and its impact on productivity. Consequently, we decide to focus on WIP and lead time control. Studying these two major objectives imply to take the system dynamic into account. In that sense, simulation appeared like a really adapted tool to improve physical flow management. In this paper, we will present the use of simulation done by a furniture-manufacturing group. In the next part, we will present classical simulation uses in 2
Proficoupe is distributed by Homag. Ortems Production Scheduler is distributed by Ortems. 3
industry. Part three focuses on the Cut-to-Size tool simulator, then, part four illustrates the use of simulation in workshop control. The fifth part explains how the modelling framework allows testing different control systems, with more or less distribution of the decision power, using the same case study. Finally, we conclude and invoke open issues and possible improvement of these uses. 2. SIMULATION AND INDUSTRIAL USES In this section, we briefly describe some industrial uses of simulation in order to place our work. Simulation has already been used for a long time (Zeigler, 1976) in most industrial cases and shown its ability to solve some locking problems, hard or impossible to compute with classical tools. The basic principle of simulation is the construction and use of a computer-based representation, or model, of some part of the real world as a substitute vehicle for experiment and behaviour prediction (Hollocks, 1995). Simulation tools present several applications in the manufacturing domain: • Design and sizing of new structures, • Study of physical modifications impact, • Study of new management rules, their viability and impact, • Production facilities control. Simulation techniques have been used for a long time in designing of new structures (Reitz, 1985)(Kuwada et al, 1986) or in studying the impact of some modifications on an existing one. One of the major interests in simulation is in the ability to test some new control structures in order to validate them before a possible real use. For example, the introducing of a Kanban subsystem into a MRP Controlled manufacturing Environment (De Smet et al, 1998) or the reducing of lot-sizes (Habchi et al, 1995). Simulation is also used to control production facilities. We can consider two major kinds of approaches: in the first case, the simulation is used to validate a Master Production Schedule (Thomas et al, 2005)(Pritsker et al, 1994)(Belz et al, 1996), generated by hand or by another scheduling tool, and in the second one, the simulation tool is finely coupled with the scheduling tool and the elaborated solution could have done many loops between optimiser and simulator (Allaoui et al, 2004). In this domain, we could note some benchmark practices, like the TEXSIM project, which consisted in the development of an integrated tool able to generate and exploit simulation model function of input data from the user, about woven fabric manufacture (Rotab Khan et al, 1999). Objectives of our models are, on the one hand, to improve planning process by adjusting and expanding technical data, and on the other hand, to build a tool enabling to highlight current control system drawbacks and to test, tune and validate some others approaches. To meet such objectives implies to integrate simulation tools with the industrial information system, and also to use flexible
modelling rules, allowing modifying control rules without changing the physical process model. To develop our model, we used the piece of software Arena4, because its discrete-event simulation is based on the well-tried SIMAN language, which is used as industrially than academically (Al-Ahmari et al, 1999)(Perera et al 2000)(Kovács et al, 1999).
of Cut-to-Size simulator in the global enterprise information system is shown on Fig. 4. Particle Panels Loading Particle Panels Panels Stacking
Stacks of Panels
3. CUT-TO-SIZE SIMULATION As explained in section 2, the Cut-to-Size tool has variable operating times, which depend on many parameters. Indeed, the cutting plan influences the operating time due to the machine architecture, when the theoretic time, used for scheduling and for raw material ordering, is given by a formula based on constant throughput. The machine is seen as a "black box". Nevertheless, there is a decoupling point into the internal process: Stacks of big panels are longitudinally cut into strips. Every strip of one packet doesn' t have the same cutting plan, consequently there is an internal inventory enabling to group them. After that, they are transversally cut by packet to obtain pieces at the final size. This process is explained on Fig. 1 This figure shows also the synoptic of the simulation model. Due to complexity of the grouping rules of strips and stacks of strips, we had to use Visual Basic code Blocks to model the system. As an input for the simulator, we used four files, generated by the raw material optimisation piece of software ProfiCoupe (Proficoupe transmit data about cutting plan to the machine using several files, four of them are useful for us). To ensure the independence face to this optimisation tool, we chose to use these files sent to the machine. Indeed, files sent to the machine will always stay identical even if the optimisation tool is upgraded and its working way changes. These files contain all relevant information about the cutting operation: • Numbers and types of panels in input, • Dimensions and quantities for each strip type, • Dimensions and quantities for each piece type, • Longitudinal machining times, • Particular operations. All information are aggregated using an Excel file and VBA Macros, and are used to generate the Arrival module. Each entity generated is representative of a panel, with its pre-affected cutting plan: that is to say we know at the beginning of the process how many strips and how many pieces are going to be done in each panel. As an output, another Excel file is generated, including for each run its identifier, the total expected time, and the number of pieces stacks evacuated. This last parameter is useful to evaluate the load of output operators. This output file is used to update the technical database, which enables to compute the detailed schedule using realistic data. The integration 4
Arena is distributed by Rockwell Software.
Longitudinal sawing
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Fig. 1. Cut-to-Size Centre Synoptic In terms of validation, the major problem came from the natural variability of the process. As evoked in section 1, two replications of a same cutting plan could lead to obtain variable operating times. But a statistical study shown that this operating times distribution follows a normal law, and the simulation results are less than five percent different from the real time, with an average difference of 2.4 %. Fig. 2 shows for a panel of runs the difference between times and throughputs realised on the Cut-toSize Centre, times given by the simulation and times used before to compute the schedule. We could notice that the simulation time is very often more realistic than the scheduled one. Such an observation lead us to affirm that using the simulated time to compute the schedule will be more relevant than to carry on the use of the previous time computing method. Variations are due to the stacking cadence, which is a man made operation, and consequently subject to uncontrollable parameters, like operator tiredness for example. The major impact of using such a simulation tool is the ability to obtain realistic technical data, which enable several new practices in the production management: • The use of the scheduling tool has been improved by the accuracy of input data, • The raw material responsible has now a vision of the cutting plan impact on productivity. Moreover, the control of the Cut-to-Size machine, which is the bottleneck, has been modified : for the more frequent products, typical cutting plan have been established, taking into account raw material scraps, but also the machine productivity.
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average batch size is about 400 units, which implies several important batches at the same time in the workshop. The end of the process is the packaging line, where pieces have to be grouped by furniture reference. Of course, all the components belonging to the finished product bill of materials have to be completed before packaging. Three intermediate stocks enable to buffer physical flow between machines. These inventories are “rolling stocks”, equivalent to drawer stocks. The nature of these stocks leads to more complex control. Indeed, there are several FIFO stacks, and it is only possible to catch the first lot of each drawer. The Inventory 1 contains items that are just cut (Capacity = 164 lots), when Inventory 2 stores in progress items (Capacity = 180 lots) and Inventory 3 stores finished and ready to package pieces (Capacity = 510 lots).
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Fig. 2. Difference between real, simulation and old scheduled times (a) and outflows (b). Previously, the machine' s productivity couldn’t be quantified, due to the inaccuracy of technical data. The use of simulation enables us to compute it, and so, to evaluate the impact of some productivity improvement actions. The new accuracy of every technical data about the workshop, and the benefits from the Cut-to-Size simulator, let us envisage the positive impact of a global workshop simulator. 4. WORKSHOP SIMULATION The main objective of the implementation of the scheduling tool was to optimise the use of resources. We explained in section 3 how the development of a Cut-to-Size simulator enabled to use accurate data, and so, to establish relevant detailed schedules. But, in spite of the accuracy of technical data, the interval between schedule and reality was not satisfying. One of the identified causes of this difference comes from uncontrollable parameters, as failures or scraps. Some others causes could include potentially controllable parameters, like WIP level or process time variability. Such parameters are linked to process dynamic, which isn’t taken into account in the static scheduling tool. Simulation is a typical answer for this kind of problems. In this context, a simulation model representative of the whole workshop has been developed. The case study is based upon the workshop of the same company (Fig. 3). This workshop is composed of eight machines, organised in production cells. The first machine is the Cut-to-Size centre presented in previous section. What particularly interests us is the following drilling and grooving machines. Each one has particular features, and is able to achieve several operations. Obviously, each resource could have a different calendar according to its planned load for the week. Consequently, there are many possible operation sequences, and the product diversity makes physical flows and their control complex. Moreover,
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Fig. 3. Physical flow in the workshop This workshop is managed using a predictive scheduling, that is to say a schedule is generated according to the finished goods need, and is periodically actuated in accordance with production progress and workshop state (Master Production Schedule – MPS). This scheduling tool is fed by results from the centralised technical database, updated by the Cut-to-Size simulator. The aim of developing a workshop simulation model is to evaluate ‘a priori’ the evolution of Work-InProgress (WIP) inventory level, to maintain it in an area that will minimize its negative impact on resource productivity. To generate such information, the simulation model needs some input data: • The predictive machine planning, • The machine’s calendars, • Performance parameters. These information are provided by the information system. The performance parameters are stored in the technical database, when the predictive machine planning and calendars are given in the detailed scheduling. The workshop simulator plays the detailed schedule, given by the scheduling tool, integrating failure and variability rule, in order to validate the feasibility, and to have a foreseen on WIP level evolution. Corrective actions are not automated, the loop is closed by the scheduler responsive. The integration of the simulation tool in the global information system is shown on Fig.4 (The detail of workshop simulator structure is given on Fig.6).
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Fig. 4. Simulation tools integration
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On the other hand, the workshop simulator is used like a training tool, what enables to considerably cut back the training time for a new scheduling operator, and to reduce problems while releasing new orders. The daily use of the workshop simulator let foresee an enlarged use of it, by validating some modifications of control rules before to implement them in the workshop if their impact are relevant. One of the concrete uses of the simulation was to decide how much operators have to be allocated to transport operations, or to choose the best location to install some new stocking structures. Moreover, this initially unforeseen use needs, ,the intervention of a specialist, when quickly educated users could do the daily use. The impact of major management rules could be studied too. For example, currently, the typical lot size is about four hundred pieces, but the growing need for reactivity incites to reduce this lot size. Before to act some projects like SMED5, the workshop simulator will enable to validate the impact of such a modification on the WIP volume. But the ability to test some new management rules drove to new needs, more and more complex and fastidious to apply for the model designer. This observation led us to develop a new modelling architecture, separating, on the one hand, the physical controlling system and, on the other hand, the control system, interfaced by an informational system according to the system theory (Le Moigne, 1984). 5. DISTRIBUTED DECISION SYSTEM REPRESENTATION
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The simulation model has been validated following a two steps process: first, functional properties have been validated, and in a second time, stochastic parameters have been identified using a statistic study. Functional properties integrate: • Any lot follows the way corresponding to the real machining sequence (routings), • When more than one lot is available in front of a machine, the schedule is respected, • Machine calendars are observed. Moreover, some alerts have been developed. These alerts generate a log file each time an error appears, which enable us to track the error. Machining process being more standard than the cutting one, the characterization of their stochastic parameters has been done analysing previously recorded times for each machine. According to a 24 to 48 hours horizon, we were able to show the reliability of our system. Effectively, if the schedule is not or few changed, the forecast is relevant, but if the schedule is completely changed, due to commercial constraints for example, the forecast error grows up quickly (See comparative between real and simulated inventory on Fig.5).
Inventory 3 Simulated Inventory 3 Real
Fig. 5. Real and simulated inventory evolution Opposite to the Cut-to-size simulation tool, the reverse loop from the simulator to the informational system is manual: that is to say the simulator generates a report, which is analysed and used by the scheduling operator. This way of establishing a schedule enables it to react in order to maintain the inventory level between efficiency boundaries. The daily use allowed improving the scheduling rules. The results are generated with a period of four hours: the global evolution is presented with curves, like on 5. This global evolution enables us to establish what are the interesting areas and then to obtain the detailed content of WIP to engage some corrective actions.
In the one hand, we want to represent actual situation, where operators in the workshop take many decisions with a distributed way. On the other, we want to validate some innovative way of controlling a workshop. Consequently, we specified a modelling framework enabling to clearly separate physical flow model (called Emulation model) from control model (called Control model) (Habchi et al, 2003). The structure used is shown on Fig.6. According to the system theory, the production system is shown like a set of operations, each applying a transformation to the product, which could be space (moving), shape (machining) or time (storage).
5
Single Minute Exchange of Die
REFERENCES
Fig. 6. Simulation Model Structure Every transformation done to the product has to be represented in emulation model and controlled in decision model. To develop a global model representative of the real system, we built a library of components for each transformation type. Any component is composed of an emulation part, which is implemented by an Arena block, and a decision (control) part, which could be implemented in any Arena-friendly language. We developed this part using Visual Basic for Application and some Excel workbooks. Any operation in the emulation model is preceded and followed by synchronisation points, which are in charge of linking both models. The originality of this conception is that no decision is taken in the emulation level: on each point where a decision has to be taken, a synchronisation procedure is launched, and the decision process is computed in the control model, before to be transposed on the emulation model via tuning methods. Using such a structure will enable us to test several control systems, where the decision power could be mainly centralised or mainly distributed. More details about the modelling structure could be found in further works. 6. CONCLUSION AND OPEN ISSUES In this paper we showed several uses of simulation done in a furniture-manufacturing firm. When the first Cut-to-Size simulation tool is quite specific to the machine used, the workshop simulator is useful for any other industrial case with similar structure. We can conclude by telling that the major simulation impact is the ability to foresee what will happen in order to anticipate on events or to avoid them. Today, the planner-scheduler uses our tools to evaluate and establish the MPS. Following improvements have already been observed: • We couldn' t, at that time, give a quantitative evaluation of the impact on lead-time, but the company could now more accurately forecast them before to accept an order. • Critical WIP levels are less frequently reached. The Cut-to-Size simulator computing time is about one hour for one week of work, when the workshop simulator computes about one or two days of work in about ten minutes. The next steps for this work are to specify and develop a new control system, with the aim of associating global optimisation from centralised control system and reactivity from distributed ones. The simulation model will enable us to tune and to validate such an approach before any industrial use.
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