EP1518197A2 - Procede et systeme de simulation pour simuler des processus de deroulement de travaux, et produit de programme informatique correspondant et support de memorisation correspondant, lisible par ordinateur - Google Patents

Procede et systeme de simulation pour simuler des processus de deroulement de travaux, et produit de programme informatique correspondant et support de memorisation correspondant, lisible par ordinateur

Info

Publication number
EP1518197A2
EP1518197A2 EP03738007A EP03738007A EP1518197A2 EP 1518197 A2 EP1518197 A2 EP 1518197A2 EP 03738007 A EP03738007 A EP 03738007A EP 03738007 A EP03738007 A EP 03738007A EP 1518197 A2 EP1518197 A2 EP 1518197A2
Authority
EP
European Patent Office
Prior art keywords
simulation
production
demand
delivery
forecast
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
EP03738007A
Other languages
German (de)
English (en)
Inventor
Stephan Hase
Jan Hickmann
Ulrich Wendt
Axel Wagenitz
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Volkswagen AG
Fraunhofer Gesellschaft zur Foerderung der Angewandten Forschung eV
Original Assignee
Volkswagen AG
Fraunhofer Gesellschaft zur Foerderung der Angewandten Forschung eV
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from DE10302433A external-priority patent/DE10302433A1/de
Application filed by Volkswagen AG, Fraunhofer Gesellschaft zur Foerderung der Angewandten Forschung eV filed Critical Volkswagen AG
Publication of EP1518197A2 publication Critical patent/EP1518197A2/fr
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the invention relates to a method and a simulation system for simulating order processing processes for the production of a complex product, in particular a motor vehicle, and a corresponding computer program product and a corresponding computer-readable storage medium with the features mentioned in the preambles of claims 1, 16 and 18 to 21.
  • the known tools do not allow the complicated interdependencies of the vehicle equipment to be depicted with one another, to stay with the example of the automotive industry. There are therefore no tools with which it would be possible to generate vehicles in a model in which these dependencies are correctly taken into account (buildable vehicles).
  • the invention is therefore based on the object of providing a method and a simulation system for simulating order processing processes for producing a complex product, in particular a motor vehicle, as well as a corresponding computer program product and a corresponding computer-readable storage medium, by means of which the above-mentioned disadvantages are eliminated and in particular Holistic modeling and simulation of all planning processes in the logistics chain is made possible.
  • step c) execution of a simulation of production and / or delivery for production based on the assignment made in step c),
  • a simulation system for simulating order processing processes for the production of a complex product advantageously comprises the modules “forecast”, “fixed orders”, settlement “,“ production ”and“ distribution ”, the modules being controlled by a computer System-implemented computer program, interact in such a way that the following steps can be carried out:
  • step c) execution of a simulation of production and / or delivery for production based on the assignment made in step c),
  • the simulation system has interfaces to databases of real systems, such as databases from dealers and / or production sites.
  • a computer program product for simulating order processing processes for the production of a complex product, in particular a motor vehicle comprises a computer-readable storage medium on which a program is stored which enables a computer, after it has been loaded into the memory of the computer, a method for simulating To carry out order processing processes, the simulation comprising the method steps according to one of claims 1 to 15.
  • a computer-readable storage medium is advantageously used, on which a program is stored, which enables a computer after it has been loaded into the memory of the computer, a method to simulate order processing processes, the simulation comprising the method steps according to one of claims 1 to 15.
  • the data records used in the automatic comparison of the required numbers in step b) according to claim 1 include restrictions of the production sites and / or suppliers.
  • the demand figures are determined in step a) according to claim 1 by specifying a first demand forecast for a first forecast period and determining a second demand forecast for a second forecast period using stochastic methods from the first forecast and the demand numbers are determined according to predefinable algorithms which evaluate the first and / or second demand forecast.
  • step b) includes a correction of the required numbers to adapt to the manufacturing and / or (manufacturing) supplier capacities.
  • method steps a) to c) according to claim 1 comprise the following steps:
  • provisional demand numbers for a first forecast period, preferably for a sales year
  • a further advantage of the method according to the invention is that the automatic allocation of the demand numbers to the production sites results in a division of the demand numbers of the specified period into daily settlements, or that the automatic assignment of the demand numbers to the manufacturing sites is the compilation of daily programs for the manufacturing sites or the dissolution of the products specified in the daily subscriptions in their modules.
  • the model on which the simulation is based depicts a number of manufacturing locations
  • the model on which the simulation is based include the parameters which characterize a production facility, the limit capacities, working time models, personnel base and / or in the model on which the simulation is based a differentiation is made between dealers, in particular between dealers on the domestic market and importers, and / or in the model on which the simulation is based, distribution channels are subdivided into sub-distribution channels.
  • Another advantage of the invention is that the data generated by steps a) to e) according to claim 1, quantitative evaluations of process designs, evaluations of strategies such as fault management, freezing times of orders, delivery times, delivery reliability, means of transport utilization and / or costs include.
  • a product model is included that allows the complex dependencies of the vehicle equipment to be represented by rules. This enables vehicles to be created in the model that meet these rules (buildable vehicles).
  • Planning processes can be assessed qualitatively and quantitatively.
  • OTD Order to Delivery
  • Operative systems can be integrated so that the effects of decisions based on actual data can be examined in advance.
  • the method according to the invention integrates and extends concepts from material flow simulation, business process simulation and systems of supply chain management (SCM).
  • SCM supply chain management
  • the method according to the invention is based on the event-controlled, discrete simulation of business processes with a high level of detail in relation to product resolution, planning algorithms and mapping area. This result can also be used to advantage in logistics.
  • the invention simulates essential planning processes for the product representation and the order processing process that affect the entire logistics chain.
  • the high quality of statements that can be achieved by the invention / should be emphasized as a particular advantage; this quality makes it possible to support the introduction of new processes on a strategic, tactical and operational level. This achieves a new holistic modeling and simulation of all planning processes in the logistics chain and enables the construction of complex models.
  • process designs can be assessed quantitatively.
  • planners can evaluate production programs in terms of effects on delivery times, delivery reliability, utilization of means of transport or ultimately costs.
  • the order processing process can be examined both as a whole and in its sub-aspects.
  • Planning algorithms can be examined and evaluated.
  • the formulation of planning procedures is clear.
  • the simulation model can serve as a reference.
  • the process design is supported seamlessly from the first draft through the implementation of IT systems to operational operations.
  • Figure 1 is a flow diagram of a simulation study
  • FIG. 2 shows a verification and validation of simulation models
  • Figure 3 is a comparison of a sequential and a simultaneous
  • FIG. 4 is an illustration of possible process stages in the implementation of
  • Figure 5 shows an example of a possible structure of a model for simulating the
  • FIG. 6 shows an exemplary representation of levels of the vehicle description
  • Figure 7 is a representation of input and output data of a simulation study for the
  • FIG. 8 shows a distribution of the production throughput times for the vehicle types X and
  • FIG. 9 shows a diagram for the real production lead time for vehicle type Y
  • FIG. 10 shows a diagram for the real production lead time for vehicle type X
  • FIG. 11 shows a representation of the reduction in stochastics in the production lead time of vehicle type Y
  • Figure 12 is an illustration of the reduction in stochastics in the
  • FIG. 13 shows a comparative representation of the customer arrival time for a scenario in which the customer is only informed about the delivery date when the vehicle is finished (top) and a scenario in which the customer is already informed about the delivery date in the vehicle planning (bottom);
  • FIG. 14 shows a table of the space required when delivering the vehicle to a vehicle
  • FIG. 15 shows a table of the results of the sensitivity analysis
  • FIG. 16 shows a table of the space requirement for scenario S2 and the distribution Vc of the production cycle time
  • Figure 17 is a table of the potential savings at parking spaces in the transition from
  • Figure 18 is a table with information on reducing the capital commitment costs per
  • Figure 19 is a table with information on reducing the capital commitment costs per
  • FIG. 20 shows a schematic representation of an annual forecast
  • FIG. 21 shows a schematic representation of the forecast update
  • FIG. 22 shows a schematic illustration of the capacity adjustment
  • FIG. 23 shows a schematic illustration of the generation of “approved
  • FIG. 24 shows a schematic representation of the “weekly settlement”
  • FIG. 25 shows a schematic representation of the “daily settlements”
  • FIG. 26a shows a composition of the US and Canada markets in one exemplary embodiment
  • FIG. 26b shows the markets and dealers in one exemplary embodiment
  • Figure 27a PR number families for engines, transmissions and air conditioning
  • Figure 27b PR number families for radios, paints and hoods
  • FIG. 28 shows a product tree
  • FIG. 29 shows a diagram to illustrate the sales forecast for the vehicle
  • FIG. 30 is the basis of the diagram shown in FIG. 29
  • FIG. 31a shows a course of the proportional vehicle sales for North America and Europe used in one exemplary embodiment
  • FIG. 31b the sales data on which the diagram shown in FIG. 31a is based;
  • FIG. 32a shows a course of the proportionate vehicle sales used in one exemplary embodiment for the USA, Canada and the “other regions”;
  • FIG. 32b the sales data on which the diagram shown in FIG. 31a is based;
  • Figure 33 is a table of days off in plant A in one
  • FIG. 34 shows a probability distribution for the swirl at
  • Figure 35 average, minimum and maximum delivery times;
  • Figure 36 average delivery and order lead time;
  • Figure 50 average delivery and order lead time
  • the task associated with the design of the order processing process results from the complexity of the system under consideration, in which the process runs, as well as the various options for consciously influencing system behavior, for example by changing the control principles or unconsciously due to disruptions.
  • one or more simulations of the order processing process are carried out with the method according to the invention.
  • a modular simulation model was set up, with the help of which the system behavior can be assessed under various boundary conditions.
  • the model shows optimization potential in order to be able to continuously improve the business process of order processing in the planning and implementation stages.
  • weaknesses e.g. bottleneck capacities
  • the benefits of, for example, expanding capacity at a specific site can be quantified with regard to the success factors and possible investment decisions can be secured.
  • the exemplary simulation model shows, in addition to the dealer network of the Federal Republic, the central sales and planning areas, selected plant locations and the distribution of the manufacturer in an abstract form.
  • the core processes of the suppliers, important order modules and the interfaces to the production areas of the automobile plants are roughly mapped.
  • the simulation model accordingly represents, on the one hand, a modeling environment for the adaptation of the open reference model, and, on the other hand, it represents a model memory for those already configured based on location and brand
  • the lead time, inventory and adherence to delivery dates largely depend on a balanced relationship between the available capacity and the required capacity. This comparison must already be carried out in the annual production program planning based on sales forecasts in order to align the company to the seasonal fluctuations (so-called breathing company). Long-term capacity planning must be coordinated with personnel planning in particular in order to take annual working time models into account.
  • the exemplary simulation model can also be used to test various methods of medium and short-term capacity matching in order to obtain statements such as the
  • Planning functions have been integrated that require interaction between the dealers and the manufacturer. These planning functions include the creation of an annual sales forecast, the coordination of a sales target matched to the available capacities, the triggering of firm orders by the dealers as well as the implementation of settlements (conversion of firm orders into individual orders, the
  • simulation tool it is possible to evaluate the usefulness of the information that the suppliers can derive from the sales forecast, the content of the firm orders and the changes in the settlement in order to optimize their production and distribution processes.
  • simulation With the help of the simulation, it can also be checked to what extent the objective of the distribution can be taken into account in the sequence planning in production and what advantages or disadvantages are associated with it.
  • the dynamic process of order processing is simulated in a model in order to arrive at knowledge that can be transferred to reality.
  • simulation is a tool for dealing with reality.
  • Simulation is therefore an auxiliary tool to generate an image of the dynamic behavior of systems of reality in a model.
  • Properties describing the system are its state variables. Changes in the state variables reflect changes in the system environment.
  • Systems to be examined can be both facilities, such as factories, and processes. Specifically, it is about the investigation of system behavior in the event of changing environmental conditions.
  • the areas of application for simulation are in particular where it is difficult to model complex, real world developments. The aim is to understand this complexity and consequently to master it. Uncertainties regarding the behavior of dynamic systems under changing environmental conditions can be eliminated more quickly and easily using simulation.
  • Modeling as part of a simulation study enables a reduction in complexity by means of comparative-static analysis, which means that when comparing alternative process configurations, only one parameter is ever varied.
  • the quantity of influence on the object under investigation is successively measured by the influencing parameters considered.
  • the functionality of simulation instruments proves to be advantageous in viewing and evaluating the degree of influence in isolation. Furthermore, an improvement can result from the variation of influences that affect the system in parallel.
  • simulation technology is of great importance as an auxiliary tool in the decision-making of such complex processes.
  • Decision alternatives can be simulated in advance. Simulation results provide information about the consequences of the respective decision in relation to the entire system. Possible wrong decisions can thus be avoided ex ante.
  • simulation tools are usually used where relatively reliable statements about the behavior of complex, dynamic systems can be made with comparatively little effort.
  • reference models were used for the simulation tool under consideration. These reference models contain, for example, prefabricated components. Examples of building blocks are machines, means of transport or warehouses. This considerably reduces the effort required to carry out the actual simulation experiments.
  • the building blocks provided can be combined depending on the particular question.
  • the modeling effort of the user is then limited to the parameterization of the functionalities of the blocks used.
  • model creation can be significantly accelerated.
  • the significance of the model regarding reality based on the chosen level of abstraction must be checked. The latter has a decisive influence on the assessment of the simulation results and thus the benefits of the simulation method in general.
  • Figure 1 shows schematically the sequence when carrying out a simulation study.
  • the scheme shown in FIG. 1 takes into account the problem that a model may have to be adapted after verification and validation.
  • the relevant input variables have to be systematized. It is also necessary to discuss which parameters significantly influence the system. The relevant influencing parameters are then classified. In the context of system analysis, the dynamic parameters are of particular interest. The determination of the quantity of system influence of an individual parameter is usually supported by a sensitivity analysis. Dependencies between the input and output variables must be taken into account as a characteristic of the system behavior when developing the model. The input data required is collected in parallel with the model development.
  • Verification is the review of the individual steps in the modeling process. In other words, it is checked whether the model relationships formally developed as part of the model concept have actually been implemented in the computer model.
  • the validation of the model includes the question of whether reality, based on the objective of the simulation, is appropriately and correctly represented in the model.
  • Validation is simple in cases where a real existing system has to be modeled. First, the state of the real system has to be shown in a model. The model is suitable if the simulation results match those of the real system provided that comparable parameter settings are made. The model must map the behavior of the real system precisely enough and without errors.
  • verification means checking whether the right things are mapped in the model, while validation checks whether things are mapped correctly.
  • Figure 2 illustrates the relationship between model verification and model validation.
  • the modeled system needs a certain time of "settling” until the stability of the model is given (the values from the phase of the so-called “warming up” period are to be excluded from the overall evaluation).
  • n replications of simulation experiments are carried out with the same parameter setting and an average value over n is determined.
  • Another method is to set a confidence or trust interval. This interval limits the range in which the true value to be determined lies with a certain probability.
  • Simulation experiments carried out in a company usually serve as decision aids for management decisions.
  • the results of operationally used simulation tools in particular influence real decisions.
  • the quality of the results is of course an essential prerequisite for user acceptance.
  • the modeling for the specific example of the simulation of an order processing process for the production of motor vehicles is to be explained in detail below.
  • the order processing process comprises all sub-processes from the customer order to the delivery of the vehicle to the customer.
  • the following steps can be specified as subprocesses of order processing:
  • Order acceptance Dealers and customers agree on the vehicle type, equipment and delivery date. If the customer orders the vehicle under the agreed conditions, the order is forwarded to the manufacturer's sales department.
  • the vehicle After completion and final acceptance of the vehicle in the factory, the vehicle is loaded and transported to an interim storage facility or directly to the appropriate dealer. The vehicle is then handed over to the dealer.
  • the market demand is forecast beforehand. Based on this forecast, the quantity planning is carried out after checking the available capacities. This planning is done for both vehicles and properties. The parts requirement can be determined on the basis of the quantity planning. Quantity planning also defines the ranges for the scheduling of orders. The aim of optimizing order processing is to significantly reduce delivery times. This is achieved through the use of the invention.
  • a solution for the optimization of order processing is the implementation of a new process structure.
  • One example is the substitution of a sequential one
  • Process architecture through a simultaneous process architecture a possibility of Redesign of the process structure.
  • the individual chain links - forecasting, program planning, manufacturing, and distribution - systematically interlock.
  • a key aspect of this effort is the introduction of a process in which a vehicle in order control can be assigned to a specific customer as late as possible.
  • the specification of the order may be changed until shortly before production begins.
  • the delivery time is also gradually reduced through the gradual, evolutionary introduction of the respective process level.
  • the throughput time is reduced by one week.
  • the retailer thus orders a vehicle from "2 + 2" at least four weeks before the planned production date (ZP8 week). Order changes are then possible up to two weeks before the ZP8 appointment. In this case, changing an order does not lead to a postponement.
  • the retailer can also place an order in the systems without a specific customer order. The property specifications of a dealer order can then be changed according to customer requirements up to the time of freezing.
  • the order processing process in the automotive industry is a very complex system with numerous subordinate system components and cross-connections, so that it is difficult to master the functioning of the individual chain links of the process in the context of an overall view of the real process. For this reason, the simulation tool described in more detail below is used.
  • the order processing process is transferred to a model and simulated. Based on the efforts to optimize the overall process, changes in parameter settings can be used to determine the effects in all sections of the process chain.
  • An idealized system state is first modeled in the example simulation.
  • the result of the simulation of this reference model forms, so to speak, the benchmark for subsequent simulation runs, whereby the system status is now varied by the fact that events that can be observed in reality hinder the continuity of the process flow.
  • so-called “worst case” studies can be carried out. This determines the constellation in which the system becomes unstable with regard to the parameter setting, that is to say the functionality of the system is endangered.
  • FIG. 5 shows an overview of the complete structure of a simulation model and the logical links between the system components.
  • the above-mentioned components of the model structure - vehicle description, sales, process control, markets / distribution, factories - and their components are briefly explained in general and then explained in two more detailed execution stages.
  • the first case shows how the laws of the real system are implemented in the model structure. Following this, the procedure for modeling is explained using a concrete simulation study as an example.
  • a vehicle is fully described by a six-digit model key.
  • Components of this key include information about the vehicle class (platform and series), the designation of the body shape (sedan, variant and so on), the equipment level (base, Trendline, Comfortline, Highline and so on), as well as the designations for the engine and transmission.
  • PR numbers Hierarchically subordinate to the model key, the detailed resolution of the vehicle takes place via a list of so-called "PR numbers".
  • the unique description of a property is given via PR numbers.
  • Each property is assigned to exactly one PR number.
  • PR numbers are assigned to " PR number families "summarized. For example, the PR numbers "without airbag” and “airbag for driver” are assigned to the PR number family "airbag (short name AIB)".
  • Each vehicle is uniquely described by exactly one PR number from each PR number family. This structure is also taken into account in the construction of the model.
  • the vehicle body takes different levels into account. This structure is shown in FIG. 6.
  • the levels group root, 1st level
  • vehicle type or class 3rd level
  • body shape (4th level)
  • equipment 5th level
  • country code (6th Level)
  • the number of levels depends on the model scope. If, for example, only one platform or only one vehicle class of a platform is considered, the relevant levels can be combined accordingly.
  • the level of detail can be increased by modeling further levels. As part of the modeling process, it is to be determined ex ante to what extent the real structure of the vehicle levels can be abstracted.
  • EWC installation rates
  • Property defines the proportion of this property in relation to the family of properties. If only one vehicle description is modeled for each level, the EWC corresponds to 100% for each level.
  • Other attributes at each level are "PR numbering", "PR numbering specifications” and "PR numbering groups”.
  • PR numbering means all those properties that are already installed in the basic equipment of the respective vehicle description, whereby one property of a PR number family is to be modeled as a setting.
  • the combination of properties is called a PR number group.
  • This functionality could be used to map constraints and prohibitions, for example. On the one hand, the need to form combinations of properties could be due to technical reasons. On the other hand, sales functions can also be mapped using this functionality. For this reason, in some cases the customer can only choose from a large number of so-called equipment packages, which reduces the number of variants and facilitates the property forecast.
  • PR number specifications are all properties taken into account in the model for each family of properties, from which the customer can individually configure his vehicle. Installation rates must be defined for both the PR number groups and the PR number specifications. It is not necessary to enter an EWC for the properties set. The EWC of the property set in each case results from the difference of the sum of the EWC of the properties not set of a PR number family to one.
  • the time horizon for the forecasts and the planning of the vehicles is determined.
  • the various process stages, as described above in connection with the changeover to a modified process structure, can be parameterized using appointment series.
  • the rolling planning rhythm is mapped via the appointment series. For this, times and the corresponding time intervals of the events during the planning process must be defined.
  • Information regarding the dates for volume agreements and vehicle orders of a dealer / importer are part of the specific dealer planning parameters. Another feature is the classification of customers by segmentation. In the exemplary model, customers are differentiated according to their preferences regarding the delivery time.
  • a plant is fully described by the name, the capacity, the throughput time, closures, the destination station and the vehicle classes installed in the plant.
  • Locking is the temporary impairment of the availability of the planned capacity of one or more properties.
  • the capacity of a plant is determined by
  • a degree of flexibility over time can be defined for both throughput and weekly working hours.
  • the planned throughput time and the throughput time distribution are to be specified as value curves in the exemplary embodiment under consideration.
  • the scheduling of the order in production takes place on the basis of the planning lead time.
  • there are corresponding effects on the ZP8 fidelity there are corresponding effects on the ZP8 fidelity.
  • An improvement in ZP8 fidelity can be achieved, for example, by reducing the stochastic influences.
  • Property locks are also specified at the factories in the exemplary embodiment.
  • the description of the block is based on its definition.
  • the definition includes information regarding the warning time, the duration and the start of the block as well as the proportionate capacity of the property that is affected by the block.
  • a shift in the operating date could also be modeled.
  • a shift in the operating date is referred to when a property cannot be installed due to a variety of problems at the original date (synonymous with "Operating date", "Start of Production” (SOP) is also used This is particularly problematic in cases in which the failure to meet the planned appointment becomes known very late and can no longer be taken into account in the scheduling.
  • the exemplary embodiment of the simulation of the order processing process allows a wide range of questions to be examined.
  • the effects of strategic as well as operational-tactical decision alternatives can be simulated.
  • the following examples are intended to give an impression of the universal use of the invention. It is analyzed in all cases how the described key figures delivery time, delivery reliability, capacity utilization and stocks vary if certain factors negatively influence the continuity of the process.
  • the effects of strategic decisions can be examined regarding
  • the customer is offered the option of handing over the new vehicle at a delivery point of the manufacturer or the manufacturers in the examined model.
  • vehicle orders are also accepted by local dealers.
  • the difficulty associated with delivering the vehicles to a delivery point of the manufacturer or the manufacturers is that the vehicles that are to be handed over to the customer at a specific point in time must also be available at that point. Specifically, this claim results in 100% delivery reliability. This requires binding planning of the customer-specific vehicles and stable process flows. As part of the When planning, the personal preferences of the customer with regard to the time of delivery must be taken into account.
  • the logistical claim regarding delivery reliability described above could be realized. Based on the status quo of the delivery, it is determined which system adjustments are to be made in order to notify the customer of a binding delivery date at the time the customer receives the order from the dealer. In order to be able to guarantee the delivery of vehicles to customers on the agreed date, the customer is currently only informed of the earliest possible date of delivery when the vehicle is completed.
  • the aim of the simulation is to compare alternative process configurations.
  • a model has to be created that shows the relevant properties of the real system. Not all sub-processes of the order processing process are relevant in relation to the special circumstances to be examined here as an example. Accordingly, the abstraction can be made appropriately.
  • AOO and A vehicle classes, - market / country code (domestic / market Germany).
  • the scope of the properties recorded in the exemplary model is also limited to the property families that are regularly classified as critical.
  • an important criterion is considered to be that the model approximates the complexity of the vehicle description in reality.
  • the complexity depicted in the exemplary model relates to the properties installed in Plant A for the German market in the case of the thirteen property families considered as examples. Accordingly, a total of thirteen property families with, for example, 68 properties or PR numbers must be taken into account.
  • the PR number specifications and PR number setting are also mapped.
  • the actual or target installation rates of the properties or vehicle classes are taken from the respective planning systems. Since only vehicle control is considered in this example, modeling suppliers is irrelevant for answering the question.
  • the deadlines of process level "2 + 2" implemented in plant A are also implemented in the model.
  • the distribution of the production lead time assumed in the model must correspond to the actual production time in plant A. For this purpose, for example, the net production lead times must be shown. Machine downtimes (for example the Weekends) are subtracted from the gross production lead times. Since the production lead times for the individual vehicle types have different distributions, the assembly lines are modeled separately. At this point, no abstraction from reality can be made.
  • Figure 7 shows an overview of the input and output data required for this model.
  • the model concept is first implemented as a reference model in the simulation model.
  • a period of two years was defined as the simulation period with a vehicle volume of around 500,000 vehicles.
  • the distribution of the production lead time is shown in the model via a histogram, that is, intervals of lead times are determined and the percentage of vehicles whose production lead time has a value within the limits of these intervals is determined.
  • Figure 8 shows the real lead time distributions for the production of the two vehicle types X and Y in plant A.
  • the throughput time distributions for the two vehicle types X and Y are shown cumulatively in FIG. 9 and FIG. 10.
  • the reference model in scenario S1 is modified by taking into account the special features that occur in connection with the vehicle delivery at a delivery point of the manufacturer or the manufacturer.
  • a binding earliest possible delivery date is only set when the vehicle is completed (ZP8).
  • ZP8 a binding earliest possible delivery date
  • the vehicle In the period between the completion of the vehicle and the handover of the vehicle to the customer at a delivery point of the manufacturer or the manufacturer, the vehicle must be temporarily stored in the designated parking spaces.
  • an average service life of fourteen to sixteen calendar days was expected. However, this guideline can vary.
  • a time-delaying element within the distribution channel was modeled in order to appropriately map the customer arrival time.
  • customer arrival time corresponds to the time span between the information of the customer about the earliest possible delivery of the vehicle and the actual time of delivery of the vehicle.
  • the average customer arrival time is around sixteen calendar days.
  • a capacity of around 8,700 parking spaces on the factory premises was provided for the temporary storage of the vehicles.
  • alternative places of indefinite capacity can be taken into account.
  • a first simulation step the space required for the three expansion stages described is determined, the real distribution of the production lead time of the two vehicle types X and Y being assumed (distribution Va in FIGS. 11 and 12, respectively). Subsequently, the variance in the distribution of the production cycle time is successively reduced (distribution Vb and Vc in FIGS. 11 and 12, respectively). This measure increases ZP8 loyalty. As part of this sensitivity analysis, the influence of stochastics in the distribution of the production cycle time on the ZP8 fidelity is quantified.
  • FIG. 11 and FIG. 12 show the distributions Va, Vb and Vc of the production throughput time for the manufacture of vehicle type X and for the manufacture of vehicle type Y. In both cases, the course of the production throughput time distribution shows the reduction in the stochastic influences.
  • a further embodiment of the simulation tool according to the invention is described below, a second scenario S2 having been implemented in the configuration.
  • the scenario S2 of the second embodiment is based on the findings of the sensitivity analysis with regard to reducing the variance in the production lead time.
  • a stable manufacturing process enables the timing of the transmission of the information of the earliest possible delivery date to the customer.
  • the average service life of the vehicle is reduced according to ZP8.
  • the delivery date preferred by the customer is already taken into account when planning the vehicle.
  • the distribution of the customer arrival time is not implemented in the distribution, but rather as a customer request date distribution in the order planning.
  • scenario S2 for the customer request date as for the customer arrival time in scenario S1.
  • the model was verified and validated. As already described in the introduction, a comparison between the model and the model concept is carried out during the verification, i.e. it is checked whether all relevant system relationships that were defined in the concept are mapped in the model.
  • the evaluation is based on only one simulation run. This approach is justified in connection with the present question, for example, because in a first step, only a rough estimate of the space requirement is to be made. In the event that exact values have to be determined, statistical methods must be used. This includes performing multiple replications, among other things. It must be ensured that the starting value is varied by the random generator when generating the random numbers. Otherwise the results are reproduced with each replication.
  • a rough estimate is usually sufficient to determine the duration of the "warming-up" period. This procedure is generally permissible since only a maximum value has to be determined for the specific question. However, approximation methods would be used, for example, if an average value is to be determined over a certain output variable. When evaluating the data from the exemplary simulation runs, a duration of three months was determined that the modeled system needs to settle.
  • FIG. 14 shows the results of the determination of the space requirement on the basis of the real distribution of the production lead time (distribution Va) for the expansion stage 1 (delivery of 300 vehicles per day), expansion stage 2 (600 vehicles per day) and expansion stage 3 (1,000 vehicles per day).
  • FIG. 15 the results of the sensitivity analysis described above with regard to the effect of stochastic production throughput times on the ZP8 fidelity were processed.
  • the values for the standard deviation in the production lead time shown in FIG. 15 were determined from a sample of 1,000 vehicle orders for each of the three assumed distributions.
  • the expected value for the production lead time corresponds to the plan lead time.
  • FIG. 16 shows the space requirement for the case of the distribution Vc of the production cycle time.
  • Figure 17 shows the savings potential with regard to the space requirements compared to the current process in scenario S1.
  • the simulation tool is divided into different program blocks, which implement the steps described above, which are required in the simulation for examining the respective problem.
  • These program blocks have the following functional scope:
  • the system load generator generates the demand forecasts of the dealers and the orders of the buyers in a simple form.
  • the forecasts are continuously (for example monthly) adjusted to the generated (actual) needs of the dealers.
  • the system load generator generates a simplified forecast of the number of cars that could be sold over the following year individually for the dealers and once in total at the beginning of a sales year. In addition to the number of vehicles that are likely to be required, their main features ("heavy items") are characterized.
  • the annual forecast can, for example, have the form shown in FIG. 20:
  • this is a simplified forecast that approximates the forecast progression of the retailers.
  • the load generator uses the simulated annual process to generate purchase orders for each dealer, whose curve display has a similarly simplified form that deviates from the annual forecast.
  • the forecasts for the next period are updated per week.
  • the output data of the system load generator include the annual forecasts of the dealers, the buyers' requirements as well as the updated weekly requirements (number and "heavy items" of the required vehicles) of the dealers for the next forecast period. They simplify the input load for the following program blocks and can also be graphically illustrated become.
  • the needs of the dealers are collected and accumulated.
  • the requirements (number, items) are compared with the actual capacities of the plants and the suppliers.
  • the capacity leveling can be represented as shown in FIG. 22.
  • the output of the program block for capacity balancing includes the approved firm orders and module quotas for the dealers. They form the input for the program block "settlement generator”.
  • Program block settlement generator "
  • the settlement generator generates specific settlements (fixed orders and order modules) for each delivery week from each of the approved firm orders and module contingents (see Figure 24).
  • the specification of the fixed orders for settlement depends on the purchaser orders received so far by the dealer and the requirements forecast for the delivery week. This specification is modeled in the model by means of probability distributions with mean values and scatter.
  • Settlement generator outputs are the settlements per dealer and delivery week. These individual settings are assigned to the plants by the following program block and converted into daily settings.
  • the output data of the program block "Plant assignment" include specific settings with information about the manufacturing plant, the suppliers involved and the name of the delivery day.
  • the settled concretizations are communicated to the dealers and form the input for the following program block.
  • the program block "settlement manipulator” carries out the entire outlined process of inventory and settlement comparison with the purchaser orders and the locating and updates the individual settlement of the dealer.
  • Results or issues of the settlement manipulator are updated settlements with buyer orders as well as buy orders with assigned stocks or vehicles specified in the settlements or fixed orders.
  • This program block works with the daily settlements of the dealers. Depending on the plant-specific requirements, for example the latest possible starting points for assembly, the settings are compiled into daily programs. In addition, the vehicles specified in the subscriptions are broken down into their modules. This is communicated to the suppliers as specified call quantities.
  • the outputs of the program block "daily program" are daily production programs for the factories as well as fixed call quantities that are communicated to the suppliers.
  • Program block "Production and suppliers” In this program block, the individual production facilities, the suppliers' plants and the times for assembly or delivery are reproduced in an abstract form using several model modules.
  • Average throughput times and throughput time fluctuations as well as the daily production capacities for the production facilities and the supplying plants are given as parameters.
  • model elements have descriptions of characteristics, for example, about limit capacities, working time models, personnel numbers or other specifics of the plants, which are necessary for the forecasting and planning processes of the previously described program blocks.
  • the production plants simulated in rough model blocks give simplified calls to the model blocks that simulate the supply in a simplified form.
  • the daily programs serve as the input for this program block.
  • the program block creates vehicles in the model that are transferred to the following program block "Distribution".
  • This basic model exemplifies the start of series production and the subsequent year (for example, the year 2003) without disruptions such as strikes or supplier bottlenecks.
  • the model is intended to consider the US, Canada, Western Europe markets and a market that supplies the other areas.
  • the USA and Canada markets are composed of the PPCs shown in FIGS. 26a and 26b.
  • the markets of Western Europe and "other areas" are each represented by an importer with a 100% market share.
  • Equipment variant A is sold on the European market.
  • the equipment variants B, C and D are sold on the other markets USA, Canada and "other areas”.
  • the vehicle body includes the modeled vehicle classes and equipment.
  • a vehicle is described in the model described here by a family of PR numbers:
  • Air conditioning, radio and hood Air conditioning, radio and hood.
  • Every vehicle also has an exterior color.
  • the individual PR number families are composed, for example, as indicated in FIGS. 27a and 27b.
  • the product tree consists of a basic vehicle description, a vehicle description "Vehicle X, US", which summarizes the common features of the equipment B, C and D, a vehicle description "Vehicle X, Europe” and the equipment variants A, B, C and D (compare figure 28).
  • vehicle description "Vehicle X, Europe” or the subordinate vehicle description A is sold 100% on the European market.
  • vehicle description "Vehicle X, US” and the subordinate equipment variants B, C and D are reproduced with that in FIGS. 32a and 32b Distributed to the US, Canada and "other territories" markets.
  • the pro rata plant output in the model was set so that the plant utilization in the model related to vehicle X averaged 85%.
  • the transports take place daily in the model.
  • the capacity of the transports can be limited if necessary.
  • Figure 35 Average, minimum and maximum delivery time: Figure 36, average delivery and order processing time: Figure 36, delivery time for vehicles with a specific engine: Figure 37, schedule reliability: Figure 38, weekly schedule reliability: Figure 39, - ZP8 loyalty: Figure 40,
  • Scenario S3 for example, is characterized by an overly pessimistic sales forecast.
  • the assumption is made that the forecast sales actually arrive as actual sales; in scenario S3, which assesses sales too pessimistic, this assumption is rejected. It is then examined what effects a too pessimistic sales forecast has on the process.
  • model for this scenario S3 can be expanded, for example, in such a way that the actual sales exceed the forecast sales by a total of 20%.
  • Scenario S4 is characterized by an overly optimistic forecast for sales.
  • Another scenario S5 shows, for example, a strike in production.
  • the strike began on March 1st, 2003 and lasted a total of ten days.
  • Two variants can be examined. In the first variant, the effects of the strike are examined without countermeasures. In a second variant, fewer orders are scheduled early because the strike was known in good time.
  • the strike is mapped by a disturbance in the factory capacity.
  • Option two after ten days, 1,165 vehicles are delayed. In response to the strike, the target output is reduced by these 1,165 vehicles in March.
  • a bottleneck in the delivery of diesel units can be examined in a simulation.
  • the model is expanded to include a supplier of this engine, for example, and the capacity is set so that the need for engines can be met exactly.
  • the bottleneck is then mapped to a disruption in the supplier's capacity.
  • the embodiment of the invention is not limited to the preferred exemplary embodiments specified above. Rather, a number of variants are conceivable which make use of the arrangement and method according to the invention even in the case of fundamentally different types.

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Abstract

L'invention concerne un procédé de simulation de processus de déroulement de travaux, pour fabriquer un produit complexe, notamment une automobile, ainsi qu'un système de simulation, un produit de programme informatique correspondant et un support de mémorisation correspondant, lisible par ordinateur. A cet effet, il est prévu les étapes suivantes : a) introduire des nombres requis pour au moins une catégorie du produit, pour au moins une période prédéterminable, dans un dispositif de traitement de données ; b) équilibrer de manière automatique ces nombres requis avec des jeux de données prédéterminables décrivant les capacités de fabrication et/ou de sous-traitance de fabrication, par un programme informatique installé sur le dispositif de traitement de données ; c) affecter de manière automatique des nombres requis ou des proportions de nombres requis, aux points de production (usines) ; d) effectuer une simulation de la fabrication et/ou de la sous-traitance pour la production fondée sur l'affectation effectuée à l'étape c) ; e) déterminer de manière automatique les voies de distribution et effectuer une simulation de la (des) distribution(s) des produits finis effectuée(s) par les usines aux points de distribution; f) mémoriser et/ou sortir au moins une partie des données produites de l'étape a) à l'étape e).
EP03738007A 2002-06-25 2003-06-06 Procede et systeme de simulation pour simuler des processus de deroulement de travaux, et produit de programme informatique correspondant et support de memorisation correspondant, lisible par ordinateur Ceased EP1518197A2 (fr)

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DE10228358 2002-06-25
DE10228358 2002-06-25
DE10302433A DE10302433A1 (de) 2002-06-25 2003-01-17 Verfahren und Simulationssystem zur Simulation von Auftragsabwicklungsprozessen sowie entsprechendes Computerprogramm-Erzeugnis und entsprechendes computerlesbares Speichermedium
DE10302433 2003-01-17
PCT/EP2003/006025 WO2004001633A2 (fr) 2002-06-25 2003-06-06 Procede et systeme de simulation pour simuler des processus de deroulement de travaux, et produit de programme informatique correspondant et support de memorisation correspondant, lisible par ordinateur

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