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Data-driven simulation of the supply-chain—Insights from the aerospace sector
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Int. J. Production Economics 110 (2007) 70–84 www.elsevier.com/locate/ijpe
Data-driven simulation of the supply-chain—Insights from the aerospace sector James Tannock, Bing Cao, Richard Farr, Mike Byrne Nottingham University Business School, Nottingham NG8 1BB, UK Available online 2 March 2007
Abstract With the increasing complexity of supply chains in aerospace manufacturing, simulation has become a powerful tool to assess performance. Conventional simulation models are constructed by experts, limiting their usefulness for non-experts. Here, the concept of data-driven simulation is used, where the simulation model is constructed automatically using input data from company IT systems. The paper describes the concept and operation of a supply-chain model builder that has been developed. An example model is provided from the civil aerospace sector. The study shows that data-driven simulation can be useful to support the design and improvement of supply chains. The nature of the conceptual model, model verification and validation in data-driven simulations are discussed. Data-driven simulation is not a replacement for general-purpose simulation tools, but an adjunct valuable in certain circumstances. It can allow a variety of scenarios to be explored relatively quickly without a high level of simulation expertise. r 2007 Elsevier B.V. All rights reserved. Keywords: Data-driven modelling; Supply-chain simulation; Extended enterprise
1. Introduction Over the last few decades, the nature of competition in many business environments has been changing, from competition between companies to competition between supply chains. A single company often cannot satisfy all customer requirements, including rapidly developing technologies, a variety of product and service requirements and shortened product life-cycles. Such developing business environments have made companies look to develop the Corresponding author. Tel.: +44 0115 951 4023;
fax: +44 0115 846 6341. E-mail address: [email protected] (J. Tannock).
supply-chain as an extended enterprise, to meet the expectations of customers (Childe, 1998). Christopher (1998) defined the supply chain as: ‘‘a network of connected and interdependent organisations mutually and co-operatively working together to control, manage and improve the flow of material and information from suppliers to end users’’. The supply chain thus comprises a number of suppliers interconnected by information and material flows. Unfortunately, supply chains do not always behave as expected or desired, due to the complexity of interconnections and variability in both the performance of suppliers and the links between them. For example, excessive demand variability—due to information distortion in the supply chain between one organisation and the next—can become a
0925-5273/$ - see front matter r 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2007.02.018
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serious problem. Davis (1993) identified three sources of uncertainty:
Supplier uncertainty in terms of on-time performance, lateness and degree of inconsistency. Manufacturing uncertainty that arises due to process performance, machine breakdown, etc. Demand or customer uncertainty arising from forecasting errors, irregular orders, etc.
Lee and Billington (1992) note that one of the potential pitfalls in managing supply chains is failing to understand the likelihood of occurrence and the consequential impact of such uncertainties. Coupled with greater complexity and globalisation of supply chains, uncertainty brings greater supply-chain risk. It is thus very important for organisations and supply chains to have the ability, both at the supply-chain design and operation stages, to be aware of and be responsive to such risks, in order to achieve supply-chain robustness and resilience. Thus, there is a need for problemsolving methods in supply-chain management that address these uncertainties (Reiner and Trcka, 2004). Simulation is an important tool to analyse supply-chain behaviour; typically in terms of throughput, cost, delivery reliability and variability and risk. At the supply-chain design stage, simulation allows possible supply-chain configurations to be tested against different future supply-chain scenarios. It may allow the dynamic performance, risks and limitations of a proposed extended enterprise to be explored rapidly. In more general research terms, simulation can enhance understanding of supply-chain management theory and concepts. Kleijnen (2003) provides a general survey of supply-chain simulation work, identifying four useful types of simulation: spreadsheet, system dynamics (continuous), discrete-event and business games. Discrete-event simulation is the method adopted for this research. In conventional discrete-event simulation approaches, the construction and use of a model is a complex, multi-stage, iterative process. This process has been thoroughly described by many authors, for example, Law and Kelton (2000) and Robinson (2004). The main stages of the process may be summarised as conceptual modelling, model coding, experimentation and implementation (Robinson, 2004). Within each stage, and between stages, it is vital that the appropriate quality control processes
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of validation and verification are undertaken thoroughly so that the users can be confident that the model, and the results obtained from it, adequately represent the behaviour of the real or proposed system. Once a valid model and valid data are available, the model can be run to generate results on the behaviour and performance of the real or proposed system of interest i.e. on a supply-chain scenario. Much of this process has traditionally required a simulation expert to be closely involved with the project. In conventional simulation practice, if the model configuration needs to be changed significantly, the whole multi-stage process must be repeated, again with involvement from a simulation expert. Such model configuration changes are particularly likely when it is desired to explore design options, for example, those of a hypothetical supply-chain or extended enterprise. Any supply-chain is also likely to change over time: supply-chain partners may come and go as different types and levels of input are required at different stages of a project. Different supply-chain processes and logistic controls may become viable at different volumes of manufacture. The consequent requirement for frequent modification to the models means that the usefulness of supply-chain simulation to nonexpert users may, in practice, be somewhat limited. To enhance the value of simulation when applied to the supply-chain domain, a data-driven approach to modelling and simulation has been developed by the authors. In a data-driven simulation, the simulation computer model is constructed (coded) automatically by a model-builder software program based on pre-existing user data. The model-builder program that codes the model from the input data therefore replaces to a great extent the expert analyst, who would perform this task in the conventional simulation approach. The approach has been applied in the European aerospace manufacturing sector. This paper describes the application and considers some of the implications of a data-driven approach, both for simulation theory and practice. 2. Data-driven simulation Various concepts of data-driven modelling and simulation have been discussed in the literature for many years, but as the associated technology, both hardware and software, has developed, so the meaning of data-driven simulation has also evolved.
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In a relatively early contribution, Franz (1989) noted that the main advantages of data-driven simulation were the ability of users, rather than operations research specialists, to prepare and run simulations, and the ability to reconfigure the models to assess changed or alternative scenarios. Pidd (1992) states that a model can be considered to be data driven if ‘‘any instance of a system may be fully specified to the model without any need for programming.’’ Similarly, Clark and Cash (1993) suggest that a model may be considered data driven, ‘‘when users can apply the model to different situations by changing input that only requires problem-domain knowledge with a minimal modelling knowledge requirement’’. As simulation software has developed, such broad definitions would suggest that most models developed with recent commercial packages are data driven, and hence such definitions are no longer very useful. These definitions also imply that a simulation model of some sort has first to be built (presumably by a simulation expert) and can then be used and modified by simply changing the input data. Several more recent research contributions have focussed more upon higher levels of automation in the model-building process. In such approaches, a key feature is that the information that specifies the model’s details must be represented in such a way that it can be used directly by a model-building program to generate the appropriate models. A common means of data representation for this purpose are relational databases such as SQL (for example, Harrison et al., 2004), with XML schemas used to transfer the data to the simulation package (for example, Qiao and Riddick, 2004). Automation of the process can be further enhanced if the required data for the models can be obtained from existing data sources, such as a company’s enterprise requirements planning (ERP) system (Lee et al., 2003). Such data could include bills of materials (BOM), resource capacities, process times and customer and supplier information. Examples of data-driven simulation applications have been described that make use of various commercial simulation packages, including Rockwell Software’s Arena (Harrison et al., 2004; McLean et al., 2002) and DELMIA QUEST (Qiao et al., 2003; Qiao and Riddick, 2004). In the case of Arena, the XML schema is interpreted by a coded application using the built-in Microsoft Visual Basic (VBA) interface, while with QUEST an interpreter translates the XML into Batch Control Language
(BCL) and Simulation Control Language (SCL), from which the QUEST models are generated. The concept of data-driven simulation presented in this paper has some similarities with other recent approaches; however, the mechanism of data transfer does not involve the use of XML schemas, but was achieved directly by an application, the supply-chain model builder (SCMB), which generates an Arena model. The authors’ initial research aim was to provide simulation models of supplychain and extended enterprise scenarios, either at the supply-chain design stage or later, in order to improve knowledge about their dynamic performance in operation. Considering the practical issues discussed above, a data-driven approach was chosen, because it would allow the model to be reconfigured rapidly by changing the input data. Thus, a user could potentially explore the implications of radical changes to a simulated extended enterprise, with little knowledge of the simulation software itself. Data-driven modelling and simulation was envisaged as a decision-support tool in the supply-chain design phase, as well as a technique for detailed refinement that could be used on an entire existing supply-chain, or any user-selected segment of a supply-chain. 3. Supply-chain performance in the aerospace sector The aerospace industry offers particular opportunities to test the usefulness of a data-driven simulation approach due to the cost and complexity of the highly structured products, the high cost of holding inventory and the intricate interrelationships between businesses that can operate at several levels in the supply-chain. Upstream supply-chain processes have a great bearing on the overall performance of the supply chain and were the focus of this research. Risk and revenue-sharing in new-product development and subsequent manufacture is a feature of the aerospace industry, which tends to lock businesses into long-term partnerships and increasingly, consciously designed extended enterprises, often including international partners, many from Asian countries such as Japan and China. Some of these international partner companies are joint ventures aiming to combine lower costs and advanced technical capability. The European aerospace industry, in particular, has made considerable use of the extended enterprise concept to both develop and manufacture
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advanced products, such as airliners and their gasturbine engines. Supply-chain issues influencing the industry include cyclical demand, problems of timely new-product development, technical capability and manufacturing capacity, often leading to delivery arrears and delays in production. Hence, aerospace manufacturing companies must pay particular attention to the design and continuing effectiveness of their supply chains. Large engineering companies—the powerful ‘‘primes’’—stand at the top of global supply chains for both airframes and engines, acting as systems integrators for major modules or sub-assemblies, which themselves may constitute highly complex and advanced products. Supply-chain order-passing in the aerospace industry often uses traditional methods such as e-mail and Electronic Data Interchange (EDI), but increasingly the demand signals are transmitted using web-based portal systems such as Exostar,1 integrated with the prime’s ERP system and updated frequently. This supply-chain control approach is combined with a focus on inventory reduction, and might be described as a ‘‘lean push’’ system. Such approaches can be vulnerable to disruption and dynamic effects and may require considerable manual intervention and progress chasing. However, suppliers may be required to hold stocks of finished goods, sometimes at the prime company’s facility. Under these conditions, effective modelling and simulation should yield benefits in terms of competitiveness for both individual companies and the extended enterprise as a whole. To demonstrate the value of the approach, several use case models were developed by the authors, in collaboration with two well-known European aerospace manufacturing companies. Both these companies made use of proprietary SAP ERP systems. The data-driven simulation application was developed to use data from such ERP systems, to reduce or eliminate the need for manual data entry. The case study described in Section 4 represents the upstream manufacturing supply-chain of a complex, high-value component for a gas-turbine aero-engine. For this paper, in the interests of clarity, a relatively small-scale supply-chain model was used. The approach is capable of modelling much larger supplychain, and has been used to model complete aeroengines, although some degree of filtering or segmentation is currently required for large or complex products
involving many thousands of parts. The purposes for which the simulations may be used include:
See web site at: www.exostar.com/company.
General high-level design of an extended enterprise/supply chain for a new product. Improve general knowledge of the dynamic performance of an existing supply chain. Examine detailed issues in particular areas of the supply chain where problems occur. Examine delivery arrears and review/validate associated ERP lead times.
4. The data-driven simulation approach The SCMB data-driven application developed by the authors uses a database of information to directly create a set of entities within an Arena simulation model, representing a potential supplychain configuration, each with properties relevant to the capabilities of a specific supplier. The SCMB database includes both product and supplier-related information, and is best described as an extended BOM, such as might typically be held in the ERP system of a company engaged in the final assembly of a complex product. 4.1. Input data The input data must contain structure sufficient to adequately define the required supply-chain model. Data are needed to describe all the modules, assemblies, sub-assemblies, components, etc. and their provenance. Some may be manufactured in-house, others by various supply-chain partners. The data might reside in the prime company, or alternatively in a ‘‘collaboration hub’’, an independent portal entity allowing interaction between partners in an extended enterprise. The required input data are:
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Product BOM information showing hierarchical dependencies of components, sub-assemblies, assemblies, etc. Supplier information for each externally-sourced BOM item. Demand information, comprising of scheduled order delivery, emergency orders for new production, spare parts demand, etc. Order processing lead times (usually fixed, for a given model). Transportation times (including any receiving inspection time).
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Inventory information, consisting of re-order point, minimum order quantity and target inventory level. Standard production lead times (from ERP system). Standard costs for products/components (at aggregated level). Throughput capacity limitations (at aggregated level).
The above structural and operational data should be readily available within an individual company, but may also be estimated if the model scope includes suppliers who are not willing to share internal data such as costs, lead times and throughput limitations. 4.2. Data-driven modelling and simulation sequence A data-driven supply-chain modelling and simulation sequence is as follows:
preliminary design activities, of creating the generic supply-chain conceptual model and coding the required functionality, are shown at the top of the diagram, with their associated generic validation and verification tasks. Input data are obtained from the user company’s ERP system, via Excel macros which can also assist with data selection, by filtering or segmentation. Product and supplier data are stored in an MS Access relational database, created and populated using functionality provided by the SCMB. The user then selects model configuration options, such as supply-chain control mechanism and inventory policy. The specific model is then created automatically by the verified SCMB code. Specific model validation then follows, as necessary, followed by the desired experimentation and reporting. Issues related to specific model validation are addressed in more detail below. The example presented here models, for clarity, a relatively small upstream supply chain for a major component of a gas-turbine engine. The component
(1) Modelling and simulation objectives are specified, and appropriate input data are selected (product, suppliers, standard lead times, supplier performance, etc.). These data are downloaded from the ERP system, placed in a suitable file format and input to the SCMB application. This process creates a new data set within the SCMB relational database. (2) The required model scope (all or part of the supply chain) is selected using the SCMB. SCMB functionality allows the supply chain data to be selected, reviewed, edited and additional data to be manually entered, if necessary. Simulation parameters and configuration options (such as inventory level and supplychain control method) are selected by the user. (3) An Arena simulation model is automatically created, the product and supplier information being used to create and link elements and specify parameters in the simulation model. A degree of data validation is carried out simultaneously with model creation, and any construction problems (e.g. duplicate supplier data) are reported. (4) Working with Arena simulation software, the required simulation(s) are run and the results are analysed in the normal way. Fig. 1 shows an overview flow chart for datadriven simulation, using the SCMB system. The
Fig. 1. The data-driven modelling and simulation system.
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Fig. 2. Sample screen display from the supply-chain model builder (SCMB).
manufacturer was Volvo Aero Corporation, of Sweden, but the upstream suppliers’ names are disguised. Because of the confidential and proprietary nature of much of the information in this area, it was also necessary to alter the case study data using scaling. Supplier and manufacturing performance data were derived (with appropriate changes to preserve confidentiality) from Volvo Aero records. Supplier and in-house processing and transportation lead times were captured using historical performance data, summarised in triangular distributions, in order to model the uncertainty in the supply-chain. Fig. 2 shows the main screen of the SCMB, populated with the example data. This screen is designed to allow the user to select and review various types of supply chain and product data in detail, prior to building the simulation model. At the top is the menu bar, where the application functions are accessed. The left pane is a representation of the data model for a selected subset of the whole product supply chain, based on the desired
level of detail. An entity on any level of the product structure can be selected by the user. Product hierarchy details can be collapsed or expanded using the familiar MS Windows ‘‘TreeView’’ approach. On the right, the top pane lists component details for the selected entity (product, assembly, sub-assembly, etc.), while the bottom pane shows supplier performance data for the selected entity. The indented product BOM, as shown in Fig. 2, represents the hierarchy of components, sub-assemblies and assemblies that make up the final product. This is the primary data framework for the supplychain modelling process. BOM and supplier data are obtained from the ERP system (SAP2). Once data have been imported and validated, a subset of the data can be chosen for generation of a data model of the required supply-chain. This data model is a representation of a supply-chain (or part of a supply-chain) using appropriate data queries generated by the SCMB application from user 2
See web site at: www.sap.com/solutions/business-suite/erp.
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selections. After the user has selected the required supply-chain, or part supply-chain, a simulation model based on user-specified model configuration options is created automatically by the SCMB (with VBA code using the Arena VBA object model). The Arena model created here is a hierarchical supplychain model with three levels. The top level view represents the selected supply-chain (Fig. 3) having both material flow and order-passing mechanisms (described below). Second level sub-models show each individual supplier and transport between suppliers (one such is shown in Fig. 4), while the third level consists of sub-models representing basic assembly and routing processes within suppliers (Fig. 5 shows one example). Every supplier is represented as a set of one or more assembly/processing operations, each of which has a sub-model with associated throughput capacity, lead time and cost expressed at an aggregated level. No attempt was made in the prototype application to model detailed manufacturing opera-
tions or component flows within supplier manufacturing facilities. Transport is modelled as a specified delay, with associated cost, for each product and supplier pair, and includes any delay due to receiving goods inspection. Inventory or raw material and finished product can be held by suppliers, but the modelling of inventory and ordering for inventory are simplified. Each sub-model incorporates an error-trapping mechanism to handle incorrect arrivals, so as to increase robustness of the simulation. This is one of the mechanisms found to be valuable in a datadriven simulation system, where the linked issues of model data validation and robustness must be carefully considered. There is danger of inconsistencies such as ‘‘orphan’’ components arriving at a sub-model, having no defined means of handling, which, if not trapped, might lead to termination of the simulation. After model creation the user works directly with Arena, where the model can be run just as if it had
Fig. 3. The top level view of a data-driven supply-chain model, constructed by the SCMB.
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Fig. 4. A supplier sub-model showing enlarged detail.
been created manually, employing one or more replications to obtain supply-chain performance data. 4.3. Supply-chain control mechanisms The first version of the SCMB created supplychain simulation models without specific mechanisms for control of product flow. By default, these operated with a basic ‘‘push’’ system driven by the input of raw materials and constrained by processing and transportation times. This approach was later superseded by a version of the SCMB that incorporated a supply-chain control mechanism into every model. The primary control mechanism for this supply-chain simulation is the order-passing method. Two order-passing modes are available as model configuration options: either traditional (sequential inter-supplier ordering with associated order processing lead times), or the collaboration hub (an integration entity which simulates a web portal system transmitting demand to suppliers). In either case, the SCMB automatically creates the
appropriate elements for order-passing. An additional sub-model is created to undertake the functions of the hub. Parameters of the model control mechanism include inventory policy (zero stock, stock for finished goods only, raw materials only or both) and inventory replenishment policy. 4.4. Supply-chain performance evaluation Having created a model and run simulations of a potential or actual supply chain, it is necessary to assess the performance of the simulated system, and compare it with alternative configurations. Beamon (1998) noted that the establishment of appropriate performance measures is an important element of supply-chain design and analysis. An ability to effectively measure supply-chain performance will be critical to any extended enterprise, and to organisations within it. Tangible, quantitative metrics assessed at an aggregated (i.e. supply-chain) level are most suitable for the purposes of simulation, aiming to evaluate and compare different configurations of supply-chain,
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Fig. 5. The final assembly sub-model showing enlarged detail.
different control mechanisms and the influence of model parameters such as inventory holding policy, in both strategic and tactical contexts. The SCMB metric scorecard included the following:
Throughput capacity (per time period). Cost (including production, transportation and inventory holding cost). Fill rate by order. Order fulfilment lead time (from order receipt to customer delivery). Cycle time robustness (CTR).
The last of these requires that a variation in schedule or some operational constraint is introduced to the simulation. Supply-chain CTR is defined as the ratio of average supply-chain cycle time under variation to average cycle time without variation. This measure of flexibility and robustness is aimed at assessing the effect of sudden demand surges and disruptions in supply (e.g. a transportation problem or throughput limitation at a supplier).
5. Discussion 5.1. The conceptual model An issue which should be addressed at this point is the nature of the conceptual model in the type of data-driven simulation used in this application. The conceptual model is a non-software specific description of the simulation model to be developed, describing objectives, scope, inputs, outputs, content, assumptions and simplifications of the model (Robinson, 2004). In the approach developed by the authors, a generic conceptual model was first created and validated, during the design phase of the SCMB software and associated database (see Fig. 1). The nature of this model was constrained by the restricted nature of the supply-chain domain and determined by the functionality designed into the model-builder software and the associated database structures. The additional (non-generic) content of any specific conceptual model exists largely in the form of sets of data held in a relational database, which are
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created by the SCMB software from the user’s input data. As regards inputs, outputs, assumptions and simplifications, the user has some limited configuration options provided by the model-builder software. The user or analyst is required to set the modelling objectives and specify the model scope and content (by selecting all or part of the available input data). Data selection functionality—to select all or part of a supply chain—is provided by the SCMB, but may also be performed outside the system if necessary. For this purpose the authors developed techniques using MS Excel macros, which could be used to filter and segment very large BOM files downloaded from ERP systems. By these means the user can readily restrict the model to the specific part of the supply-chain, that it is desired to study. Robinson (2004) suggests that the chief requirements of a conceptual model are validity, credibility, utility and feasibility. The input data, which will typically be obtained from existing company systems such as ERP, are already largely validated as they are used for many other functions in the company. Further data validation can be carried out automatically, while the model is being created from the data. Rather than having to be carried out repetitively whenever a new model is built, the validity of the coded model depends only on the previously validated input data and on initial (design-time) verification of the database and SCMB software, which it should thereafter perform in a consistent and repeatable manner. Verification of the model-builder code itself remains an important (and potentially difficult) task, but once completed it does not have to be repeated for each model generated. Validation and verification will be described in the next section. The design features of the SCMB software are the critical issues with regard to the credibility and utility of the conceptual model. For example, does the model-builder software provide an appropriate means of modelling product inventory at each supplier? Feasibility (the ability to develop a computer model from the conceptual model) is primarily a matter of model size and complexity. 5.2. Verification and validation Verification is concerned with determining whether the conceptual simulation model has been correctly translated into a computer program model
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(Law and Kelton, 2000). Verification of a datadriven simulation includes all the usual verification considerations, and in addition the verification of the VBA code used to generate the Arena simulation model. A single error in critical sections of this code—for example, the code which creates an entity in Arena from specific data—could propagate throughout the simulation model and lead to serious problems. The techniques used to achieve confidence in the modelling process were:
modular program design, detailed code checking and testing, code execution tracing in debug mode, running models under simplified conditions where model output can be predicted and observing animation outputs when running models under different scenarios.
Validation is the process of determining whether a simulation model (as opposed to the computer program) is an accurate representation of the system, for the particular objectives of the study (Law and Kelton, 2000). Robinson (2004) describes various kinds of simulation model validation: conceptual model validation, data validation, white-box validation, black-box validation, experimentation validation and solution validation. Carson (2004) suggests that various techniques, similar to those used during verification, may be used during model validation, including:
Use of animations and other visual displays to communicate model assumptions. Output measures of performance for a model configuration representing an existing system or initial design, so that users may judge model reasonableness.
If sufficient data have been collected on a realworld system that matches one of the model’s possible configurations, more formal results validation may be conducted, by comparing the model to this real system. Three types of validation were carried out for the simulation models produced by the SCMB: conceptual model validation, data validation and results (black-box) validation. Conceptual model validation aims to determine whether the model specifies the required aspects (objectives, scope, content, inputs, etc.) with sufficient clarity and precision and in adequate detail, to meet the requirements of the study. Where an expert
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analyst develops a conceptual model, experience and judgement will play a key role in model design and configuration, as well as validation. The nature of the domain is also an issue: domains with limited design alternatives (such as the supply-chain) may be more suitable for an automated approach. For example, interfaces between suppliers are well defined and generic in nature. The SCMB user is provided with a limited range of standard prevalidated configuration options to define the conceptual model, before the simulation model is automatically created. The configuration options for the SCMB were supplier selection (where multiple suppliers exist for the same item) inventory level, quality conformance level and supply-chain control mechanism. The distribution of processing and transportation variations could also be specified by the user from among the options provided by Arena. Sudden interruptions to the system (uncertainty) could be specified by user-specified change in delay time with associated probability. Techniques for conceptual model validation undertaken by the authors include checking with existing theory, making use of modeller’s experience, literature surveys of similar simulation studies and continuing interaction with industrial (case study) partners. Law (2003) recommends an activity for conceptual model validation in which a structured walk-through of the conceptual model is performed before an audience that includes project manager, analysts and subject matter experts. The authors undertook this process at a series of detailed technical meetings with industrial partners at which supply-chain experts were present. Data validation is the process of determining that the contextual data and the data required for model realisation and validation are suitable for the purposes at hand (Robinson, 2004). An iterative data validation process was adopted, consisting of:
Initial data collection and model building. Data checking and correction after initial simulation results showed differences between model outputs and the real system outputs.
After completing this iterative process, the authors were confident that the input data used for the validation and experimentation were valid, such that model outputs could be compared with real system outputs. Validation of results assists with validating the simulation model through comparison of model
outputs with real system outputs (or outputs from other models). If there is an existing system, then performance measures from a simulation model of this system can be compared with those collected from the actual system. If results validation is successful, then it lends significant credibility to the simulation model. To perform results validation properly, it needs to be established first that results from the simulation model are correct to an appropriate statistical significance level. This involves the determination of number of replications and the length of warmup periods. During validation and experimentation, transient effects occurred during the simulation warm-up period, in which marked fluctuations in performance metrics occurred. This is typical of simulation for both manufacturing and supplychain scenarios. In these experiments, a steady state was reached (by inspection) before metrics were recorded. Multiple replications were employed to develop statistical confidence in the values of the performance metrics in the steady state. When perturbations were deliberately introduced into the simulation, these were introduced from the steady state. Pre-processing of product BOM and supplier data for errors (e.g. redundancy) may be valuable. So as far as possible, inconsistencies within the database should be handled without requiring human intervention, since even automated model construction may be a lengthy process. However, SCMB messages were generated where data integrity is suspect, for example, where data are missing, or where redundant elements (such as duplicate entries) were found in the database. Thus, the automatic model-building activity forms part of the model verification and validation process. A log file is produced by the SCMB containing all such error messages, and other model-building information. Preliminary simulations were carried out using the model automatically generated from the test data set; primarily to develop a greater understanding of the verification and validation issues associated with data-driven supply-chain simulation. These issues are of particular importance when the simulation model is automatically prepared, because there is less user involvement in the modelling process. There is no ‘‘expert simulation analyst’’ to consider each entity and connection, although, as described above, the modelling software incorporates code to conduct basic integrity checks on the data. The absence of the analyst,
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need not be modelled to the same level of detail as the higher-value parts, so their suppliers’ performance is represented in a simplified form). The customer (a prime engine manufacturer) was also represented in the model, as the simulation objective was to study delivery performance to this customer. Volvo held finished goods inventory, physically located at the prime company’s facility. Within the Volvo factory itself, four elements were created, representing four stages of assembly operations. The validation scenario had the following features: sequential order-passing, 100% quality conformance, inventory held for material and finished goods and the model was populated with initial inventory. Production at Volvo was driven by historical master production schedule (MPS) data (push system). The demand was modelled using actual historical demand patterns. Upstream ordering was driven by the standard inventory model (periodical review). The warm-up period was first determined before validation. Various approaches have been proposed in the literature to deal with warm-up period, each having advantages and disadvantages (Robinson, 2004). The time-series inspection approach was used here due to its simplicity and widespread use. It was implemented by inspecting a time series of weekly throughputs. A problem with inspecting a time series of a single run is that the data can be very ‘‘noisy’’, making it difficult to spot any initialisation bias. Multiple replications were employed to estimate the length of the warm-up period. The mean value of five replications for each period was plotted on a time series (Fig. 6). The regular dips in mean throughput shown in Fig. 6 correspond to vacation
however, may also be beneficial to the model as it eliminates mistakes arising from human error. Verification of the model requires testing to determine that the operation of the simulation model is logically consistent and reasonable, as assessed by outputs from simulations carried out under specified conditions. The authors consider that a reliable, standardised verification process is important for the credibility of data-driven supplychain simulation. However, the time consumed in more comprehensive verification might outweigh the gains from using the data-driven technique, so a limited set of metrics was chosen for this purpose. Similarly, complete model validation is only possible when comprehensive real-world data exist for comparison purposes, and inevitably much ‘‘what-if’’ simulation can only be partially validated. Data-driven simulation modelling suffers from this problem, as does a manual modelling process. However, the likely user (a logistics or purchasing specialist) is considered to be competent to conduct face validation of the model, based on the selected viewpoint, scope and supplier choices made. Running the generated model in the Arena environment produces the supply-chain performance data described above. Throughput is a key supply-chain metric, which was selected as the primary verification criterion. A simplified scenario, involving two levels of suppliers, was used for results validation. The first tier was Volvo Aero as the ‘‘focal company’’ and the second tier represented the upstream component suppliers, some of which were aggregated using data selection in the SCMB (class ‘‘C’’ components such as fasteners
Units of the major component
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To further explore the validity of the simulation model and the dynamic resemblance between model and planned outputs, a visual observation approach (Kleijnen, 1995) was also used, to compare time series of simulation model outputs with the historical time series of planned outputs. The output data of the simulated system and the MPS data were plotted against time. Fig. 7 shows a comparison between the planned outputs, historical performance data and the performance of the simulation (for clarity, only a single replication is shown). Users may inspect the time series to decide whether the simulation model ‘‘accurately’’ reflects the phenomena of interest. This allows the comparison, not only of the magnitude, but also of the pattern of supply-chain throughput. Given the observed correspondence in terms of general level and behaviour between the model output and planned values, it can again be claimed that the model is valid for the types of supply-chain investigation for which it was designed.
periods near the middle and end of each year, when no production was scheduled. To determine the warm-up period, the point at which the outputs appear to settle into a steady state was identified by inspection. The point at which the data are neither consistently higher nor lower than their later ‘‘normal’’ levels and where there is no apparent upward or downward trend in the data was identified. In Fig. 6, the data appear to settle by week 42. Therefore, a warm-up period of 42 weeks was used for the results validation that follows. To validate the simulation model, its outputs were compared with real system outputs in two ways— visual and statistical. First, a confidence interval was calculated for the difference between model outputs and planned real system outputs. It should be noted that actual factory weekly throughput figures sometimes varied significantly from the planned weekly numbers, often due to trivial week-ending reporting effects. Since the simulation model was run using the planning numbers as demand, simulated throughput was compared with planned output, based on the Volvo Aero MPS. Due to possible correlations between weekly figures in successive weeks, both simulated throughputs and planned outputs were batched into monthly (4 weekly) figures. A confidence interval was then calculated, using a paired-t test to estimate the mean difference between the simulated throughputs and planned outputs. The 95% confidence interval thus obtained is ð1:5; 2:16Þ with a mean value of 0.333. Since this interval contains the value zero, the throughputs from the simulation model were not statistically different from planned output, at this significance level.
6. Conclusions This paper has introduced a concept of datadriven modelling and simulation aimed at the supply-chain and extended enterprise for the aerospace industry. Upstream supply-chains for complex aerospace products have a generally similar configuration, a framework for which can be established from data available in the product’s extended BOM. However, their behaviours and business results differ widely, influenced by issues such as supplier performance, transportation times,
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costs, etc., and also as a result of the control and integration mechanisms employed. The conclusions of the development process and of the initial testing of the models developed suggest that data-driven simulation can be a useful tool in this domain, producing models that support the design and improvement of supply-chain operations. A data-driven approach to simulation raises a number of issues with respect to simulation theory. Some of the most significant of these issues: the nature of the conceptual model, determination of model scope and content and model verification and validation have been addressed and discussed in this paper. There are various other practical issues in the development of data-driven supply-chain simulations. The availability of extended BOM data from an ERP system is, effectively, a pre-requisite for practical modelling, as a manual data entry process would be as time consuming as manual simulation. In addition, many manufacturing companies are existing users of discrete-event simulation software. Data-driven simulation, using the direct method described in the paper, is impossible unless this simulation tool has a model-building macro-language capable of dynamically creating and configuring complex models. Ideally, the macro-language should also be able to integrate readily with database and reporting tools. For this research, the selection of Arena as the simulation tool, for which VBA is the macro-language, fulfilled these requirements and brought considerable development benefits. As a general comment on data-driven simulation, the authors consider that despite the significant effort required to develop such an approach in a new domain, a persuasive case for a data-driven approach can be made. Data-driven simulation is not a replacement for general-purpose simulation tools, but an adjunct that is valuable in certain circumstances, where it is proposed to create many simulation models, allowing a variety of scenarios to be explored relatively quickly and without the level of simulation expertise that would normally be required. The model configuration and control mechanisms should also be reasonably stable and standard. It is only practicable if the structured data required to develop the model are readily available. The authors are currently conducting practical experimentation using the data-driven simulation approach. Simulations are being developed in collaboration with industrial partners, using fully
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detailed cases from the European aerospace industry. In due course, the authors intend to publish further papers describing the results of these activities, and assessing the capability and effectiveness of the approach. Acknowledgements The work presented is part of the EU Framework Six VIVACE project (contract number AIP3-CT2003-502917). The authors acknowledge the funding from the EU and collaboration from our industrial partners Volvo Aero Corporation, particularly Dr Torgny Almgren and Ms Helena Schultz. The first prototype supply-chain model builder was developed by Chang-Seop Kim, a former MSc student of the University of Nottingham.
References Beamon, B.M., 1998. Supply chain design and analysis: models and methods. International Journal of Production Economics 55, 281–294. Carson II, J.S., 2004. Introduction to modeling and simulation. In: Ingalls, R.G., Rossetti, M.D., Smith, J.S., Peters, B.A. (Eds.), Proceedings of the 2004 Winter Simulation Conference, pp. 9–16. Childe, S.J., 1998. The extended enterprise—a concept of cooperation. Production Planning and Control 9 (4), 320–327. Christopher, M., 1998. Logistics and supply chain management, second ed. Financial Times Press, Prentice-Hall, Englewood Cliffs, NJ. Clark, G.M., Cash, C.R., 1993. Data-driven simulation of networks with manufacturing blocking. In: Evans, G.W., Mollaghasemi, M., Russell, E.C., Biles, W.E. (Eds.), Proceedings of the 1993 Winter Simulation Conference, pp. 662–669. Davis, T., 1993. Effective supply chain management. Sloan Management Review 34 (4), 35–46. Franz, L.S., 1989. Data-driven modeling: an application in scheduling. Decision Sciences 20 (2), 359–377. Harrison, G.A., Maynard, D.S., Pollak, E., 2004. Automated database and schema-based data interchange for modeling and simulation. In: Ingalls, R.E., Rossetti, M.D., Smith, J.S., Peters, B.A. (Eds.), Proceedings of the 2004 Winter Simulation Conference, pp. 191–197. Kleijnen, J.P.C., 1995. Verification and validation of simulation models. European Journal of Operational Research 82 (1), 145–162. Kleijnen, J.P.C., 2003. Supply chain simulation: a survey. Discussion paper 103, Center for Economic Research, Tilburg University. Law, A.M., 2003. How to conduct a successful simulation study. In: Chick, S., Sa´nchez, P.J., Ferrin, D., Morrice, D.J. (Eds.), Proceedings of the 2003 Winter Simulation Conference, pp. 66–70. Law, A.M., Kelton, W.D., 2000. Simulation Modelling and Analysis, third ed. McGraw-Hill, New York.
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Lee, H.L., Billington, C., 1992. Managing supply chain inventory: pitfalls and opportunities. Sloan Management Review 33 (3), 65–73. Lee, Y.T., McLean, C., Shao, G., 2003. A neutral information model for simulating machine shop operations. In: Chick, S., Sa´nchez, P.J., Ferrin, D., Morrice, D.J. (Eds.), Proceedings of the 2003 Winter Simulation Conference, pp. 1296–1304. McLean, C., Jones, A., Lee, T., Riddick, F., 2002. An architecture for a generic data-driven machine shop simulator. In: Yucesan, E., Chen, C.H., Snowdon, J.L., Charnes, J.M. (Eds.), Proceedings of the 2002 Winter Simulation Conference, pp. 1108–1116. Pidd, M., 1992. Guidelines for the design of data-driven generic simulators for specific domains. Simulation 59 (4), 237–243.
Qiao, G., Riddick, F., 2004. Modelling information for manufacturing-oriented supply-chain simulations. In: Ingalls, R.E., Rossetti, M.D., Smith, J.S., Peters, B.A. (Eds.), Proceedings of the 2004 Winter Simulation Conference, pp. 1184–1188. Qiao, G., Riddick, F., McLean, C., 2003. Data-driven design and simulation system based on XML. In: Chick, S., Sa´nchez, P.J., Ferrin, D., Morrice, D.J. (Eds.), Proceedings of the 2003 Winter Simulation Conference, pp. 1143–1148. Reiner, G., Trcka, M., 2004. Customized supply-chain design: problems and alternatives for a production company in the food industry: a simulation based analysis. International Journal of Production Economics 89, 217–229. Robinson, S., 2004. Simulation: the Practice of Model Development and Use. Wiley, Chichester, UK.
Int. J. Production Economics 110 (2007) 70–84 www.elsevier.com/locate/ijpe
Data-driven simulation of the supply-chain—Insights from the aerospace sector James Tannock, Bing Cao, Richard Farr, Mike Byrne Nottingham University Business School, Nottingham NG8 1BB, UK Available online 2 March 2007
Abstract With the increasing complexity of supply chains in aerospace manufacturing, simulation has become a powerful tool to assess performance. Conventional simulation models are constructed by experts, limiting their usefulness for non-experts. Here, the concept of data-driven simulation is used, where the simulation model is constructed automatically using input data from company IT systems. The paper describes the concept and operation of a supply-chain model builder that has been developed. An example model is provided from the civil aerospace sector. The study shows that data-driven simulation can be useful to support the design and improvement of supply chains. The nature of the conceptual model, model verification and validation in data-driven simulations are discussed. Data-driven simulation is not a replacement for general-purpose simulation tools, but an adjunct valuable in certain circumstances. It can allow a variety of scenarios to be explored relatively quickly without a high level of simulation expertise. r 2007 Elsevier B.V. All rights reserved. Keywords: Data-driven modelling; Supply-chain simulation; Extended enterprise
1. Introduction Over the last few decades, the nature of competition in many business environments has been changing, from competition between companies to competition between supply chains. A single company often cannot satisfy all customer requirements, including rapidly developing technologies, a variety of product and service requirements and shortened product life-cycles. Such developing business environments have made companies look to develop the Corresponding author. Tel.: +44 0115 951 4023;
fax: +44 0115 846 6341. E-mail address: [email protected] (J. Tannock).
supply-chain as an extended enterprise, to meet the expectations of customers (Childe, 1998). Christopher (1998) defined the supply chain as: ‘‘a network of connected and interdependent organisations mutually and co-operatively working together to control, manage and improve the flow of material and information from suppliers to end users’’. The supply chain thus comprises a number of suppliers interconnected by information and material flows. Unfortunately, supply chains do not always behave as expected or desired, due to the complexity of interconnections and variability in both the performance of suppliers and the links between them. For example, excessive demand variability—due to information distortion in the supply chain between one organisation and the next—can become a
0925-5273/$ - see front matter r 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2007.02.018
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serious problem. Davis (1993) identified three sources of uncertainty:
Supplier uncertainty in terms of on-time performance, lateness and degree of inconsistency. Manufacturing uncertainty that arises due to process performance, machine breakdown, etc. Demand or customer uncertainty arising from forecasting errors, irregular orders, etc.
Lee and Billington (1992) note that one of the potential pitfalls in managing supply chains is failing to understand the likelihood of occurrence and the consequential impact of such uncertainties. Coupled with greater complexity and globalisation of supply chains, uncertainty brings greater supply-chain risk. It is thus very important for organisations and supply chains to have the ability, both at the supply-chain design and operation stages, to be aware of and be responsive to such risks, in order to achieve supply-chain robustness and resilience. Thus, there is a need for problemsolving methods in supply-chain management that address these uncertainties (Reiner and Trcka, 2004). Simulation is an important tool to analyse supply-chain behaviour; typically in terms of throughput, cost, delivery reliability and variability and risk. At the supply-chain design stage, simulation allows possible supply-chain configurations to be tested against different future supply-chain scenarios. It may allow the dynamic performance, risks and limitations of a proposed extended enterprise to be explored rapidly. In more general research terms, simulation can enhance understanding of supply-chain management theory and concepts. Kleijnen (2003) provides a general survey of supply-chain simulation work, identifying four useful types of simulation: spreadsheet, system dynamics (continuous), discrete-event and business games. Discrete-event simulation is the method adopted for this research. In conventional discrete-event simulation approaches, the construction and use of a model is a complex, multi-stage, iterative process. This process has been thoroughly described by many authors, for example, Law and Kelton (2000) and Robinson (2004). The main stages of the process may be summarised as conceptual modelling, model coding, experimentation and implementation (Robinson, 2004). Within each stage, and between stages, it is vital that the appropriate quality control processes
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of validation and verification are undertaken thoroughly so that the users can be confident that the model, and the results obtained from it, adequately represent the behaviour of the real or proposed system. Once a valid model and valid data are available, the model can be run to generate results on the behaviour and performance of the real or proposed system of interest i.e. on a supply-chain scenario. Much of this process has traditionally required a simulation expert to be closely involved with the project. In conventional simulation practice, if the model configuration needs to be changed significantly, the whole multi-stage process must be repeated, again with involvement from a simulation expert. Such model configuration changes are particularly likely when it is desired to explore design options, for example, those of a hypothetical supply-chain or extended enterprise. Any supply-chain is also likely to change over time: supply-chain partners may come and go as different types and levels of input are required at different stages of a project. Different supply-chain processes and logistic controls may become viable at different volumes of manufacture. The consequent requirement for frequent modification to the models means that the usefulness of supply-chain simulation to nonexpert users may, in practice, be somewhat limited. To enhance the value of simulation when applied to the supply-chain domain, a data-driven approach to modelling and simulation has been developed by the authors. In a data-driven simulation, the simulation computer model is constructed (coded) automatically by a model-builder software program based on pre-existing user data. The model-builder program that codes the model from the input data therefore replaces to a great extent the expert analyst, who would perform this task in the conventional simulation approach. The approach has been applied in the European aerospace manufacturing sector. This paper describes the application and considers some of the implications of a data-driven approach, both for simulation theory and practice. 2. Data-driven simulation Various concepts of data-driven modelling and simulation have been discussed in the literature for many years, but as the associated technology, both hardware and software, has developed, so the meaning of data-driven simulation has also evolved.
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In a relatively early contribution, Franz (1989) noted that the main advantages of data-driven simulation were the ability of users, rather than operations research specialists, to prepare and run simulations, and the ability to reconfigure the models to assess changed or alternative scenarios. Pidd (1992) states that a model can be considered to be data driven if ‘‘any instance of a system may be fully specified to the model without any need for programming.’’ Similarly, Clark and Cash (1993) suggest that a model may be considered data driven, ‘‘when users can apply the model to different situations by changing input that only requires problem-domain knowledge with a minimal modelling knowledge requirement’’. As simulation software has developed, such broad definitions would suggest that most models developed with recent commercial packages are data driven, and hence such definitions are no longer very useful. These definitions also imply that a simulation model of some sort has first to be built (presumably by a simulation expert) and can then be used and modified by simply changing the input data. Several more recent research contributions have focussed more upon higher levels of automation in the model-building process. In such approaches, a key feature is that the information that specifies the model’s details must be represented in such a way that it can be used directly by a model-building program to generate the appropriate models. A common means of data representation for this purpose are relational databases such as SQL (for example, Harrison et al., 2004), with XML schemas used to transfer the data to the simulation package (for example, Qiao and Riddick, 2004). Automation of the process can be further enhanced if the required data for the models can be obtained from existing data sources, such as a company’s enterprise requirements planning (ERP) system (Lee et al., 2003). Such data could include bills of materials (BOM), resource capacities, process times and customer and supplier information. Examples of data-driven simulation applications have been described that make use of various commercial simulation packages, including Rockwell Software’s Arena (Harrison et al., 2004; McLean et al., 2002) and DELMIA QUEST (Qiao et al., 2003; Qiao and Riddick, 2004). In the case of Arena, the XML schema is interpreted by a coded application using the built-in Microsoft Visual Basic (VBA) interface, while with QUEST an interpreter translates the XML into Batch Control Language
(BCL) and Simulation Control Language (SCL), from which the QUEST models are generated. The concept of data-driven simulation presented in this paper has some similarities with other recent approaches; however, the mechanism of data transfer does not involve the use of XML schemas, but was achieved directly by an application, the supply-chain model builder (SCMB), which generates an Arena model. The authors’ initial research aim was to provide simulation models of supplychain and extended enterprise scenarios, either at the supply-chain design stage or later, in order to improve knowledge about their dynamic performance in operation. Considering the practical issues discussed above, a data-driven approach was chosen, because it would allow the model to be reconfigured rapidly by changing the input data. Thus, a user could potentially explore the implications of radical changes to a simulated extended enterprise, with little knowledge of the simulation software itself. Data-driven modelling and simulation was envisaged as a decision-support tool in the supply-chain design phase, as well as a technique for detailed refinement that could be used on an entire existing supply-chain, or any user-selected segment of a supply-chain. 3. Supply-chain performance in the aerospace sector The aerospace industry offers particular opportunities to test the usefulness of a data-driven simulation approach due to the cost and complexity of the highly structured products, the high cost of holding inventory and the intricate interrelationships between businesses that can operate at several levels in the supply-chain. Upstream supply-chain processes have a great bearing on the overall performance of the supply chain and were the focus of this research. Risk and revenue-sharing in new-product development and subsequent manufacture is a feature of the aerospace industry, which tends to lock businesses into long-term partnerships and increasingly, consciously designed extended enterprises, often including international partners, many from Asian countries such as Japan and China. Some of these international partner companies are joint ventures aiming to combine lower costs and advanced technical capability. The European aerospace industry, in particular, has made considerable use of the extended enterprise concept to both develop and manufacture
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advanced products, such as airliners and their gasturbine engines. Supply-chain issues influencing the industry include cyclical demand, problems of timely new-product development, technical capability and manufacturing capacity, often leading to delivery arrears and delays in production. Hence, aerospace manufacturing companies must pay particular attention to the design and continuing effectiveness of their supply chains. Large engineering companies—the powerful ‘‘primes’’—stand at the top of global supply chains for both airframes and engines, acting as systems integrators for major modules or sub-assemblies, which themselves may constitute highly complex and advanced products. Supply-chain order-passing in the aerospace industry often uses traditional methods such as e-mail and Electronic Data Interchange (EDI), but increasingly the demand signals are transmitted using web-based portal systems such as Exostar,1 integrated with the prime’s ERP system and updated frequently. This supply-chain control approach is combined with a focus on inventory reduction, and might be described as a ‘‘lean push’’ system. Such approaches can be vulnerable to disruption and dynamic effects and may require considerable manual intervention and progress chasing. However, suppliers may be required to hold stocks of finished goods, sometimes at the prime company’s facility. Under these conditions, effective modelling and simulation should yield benefits in terms of competitiveness for both individual companies and the extended enterprise as a whole. To demonstrate the value of the approach, several use case models were developed by the authors, in collaboration with two well-known European aerospace manufacturing companies. Both these companies made use of proprietary SAP ERP systems. The data-driven simulation application was developed to use data from such ERP systems, to reduce or eliminate the need for manual data entry. The case study described in Section 4 represents the upstream manufacturing supply-chain of a complex, high-value component for a gas-turbine aero-engine. For this paper, in the interests of clarity, a relatively small-scale supply-chain model was used. The approach is capable of modelling much larger supplychain, and has been used to model complete aeroengines, although some degree of filtering or segmentation is currently required for large or complex products
involving many thousands of parts. The purposes for which the simulations may be used include:
See web site at: www.exostar.com/company.
General high-level design of an extended enterprise/supply chain for a new product. Improve general knowledge of the dynamic performance of an existing supply chain. Examine detailed issues in particular areas of the supply chain where problems occur. Examine delivery arrears and review/validate associated ERP lead times.
4. The data-driven simulation approach The SCMB data-driven application developed by the authors uses a database of information to directly create a set of entities within an Arena simulation model, representing a potential supplychain configuration, each with properties relevant to the capabilities of a specific supplier. The SCMB database includes both product and supplier-related information, and is best described as an extended BOM, such as might typically be held in the ERP system of a company engaged in the final assembly of a complex product. 4.1. Input data The input data must contain structure sufficient to adequately define the required supply-chain model. Data are needed to describe all the modules, assemblies, sub-assemblies, components, etc. and their provenance. Some may be manufactured in-house, others by various supply-chain partners. The data might reside in the prime company, or alternatively in a ‘‘collaboration hub’’, an independent portal entity allowing interaction between partners in an extended enterprise. The required input data are:
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Product BOM information showing hierarchical dependencies of components, sub-assemblies, assemblies, etc. Supplier information for each externally-sourced BOM item. Demand information, comprising of scheduled order delivery, emergency orders for new production, spare parts demand, etc. Order processing lead times (usually fixed, for a given model). Transportation times (including any receiving inspection time).
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Inventory information, consisting of re-order point, minimum order quantity and target inventory level. Standard production lead times (from ERP system). Standard costs for products/components (at aggregated level). Throughput capacity limitations (at aggregated level).
The above structural and operational data should be readily available within an individual company, but may also be estimated if the model scope includes suppliers who are not willing to share internal data such as costs, lead times and throughput limitations. 4.2. Data-driven modelling and simulation sequence A data-driven supply-chain modelling and simulation sequence is as follows:
preliminary design activities, of creating the generic supply-chain conceptual model and coding the required functionality, are shown at the top of the diagram, with their associated generic validation and verification tasks. Input data are obtained from the user company’s ERP system, via Excel macros which can also assist with data selection, by filtering or segmentation. Product and supplier data are stored in an MS Access relational database, created and populated using functionality provided by the SCMB. The user then selects model configuration options, such as supply-chain control mechanism and inventory policy. The specific model is then created automatically by the verified SCMB code. Specific model validation then follows, as necessary, followed by the desired experimentation and reporting. Issues related to specific model validation are addressed in more detail below. The example presented here models, for clarity, a relatively small upstream supply chain for a major component of a gas-turbine engine. The component
(1) Modelling and simulation objectives are specified, and appropriate input data are selected (product, suppliers, standard lead times, supplier performance, etc.). These data are downloaded from the ERP system, placed in a suitable file format and input to the SCMB application. This process creates a new data set within the SCMB relational database. (2) The required model scope (all or part of the supply chain) is selected using the SCMB. SCMB functionality allows the supply chain data to be selected, reviewed, edited and additional data to be manually entered, if necessary. Simulation parameters and configuration options (such as inventory level and supplychain control method) are selected by the user. (3) An Arena simulation model is automatically created, the product and supplier information being used to create and link elements and specify parameters in the simulation model. A degree of data validation is carried out simultaneously with model creation, and any construction problems (e.g. duplicate supplier data) are reported. (4) Working with Arena simulation software, the required simulation(s) are run and the results are analysed in the normal way. Fig. 1 shows an overview flow chart for datadriven simulation, using the SCMB system. The
Fig. 1. The data-driven modelling and simulation system.
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Fig. 2. Sample screen display from the supply-chain model builder (SCMB).
manufacturer was Volvo Aero Corporation, of Sweden, but the upstream suppliers’ names are disguised. Because of the confidential and proprietary nature of much of the information in this area, it was also necessary to alter the case study data using scaling. Supplier and manufacturing performance data were derived (with appropriate changes to preserve confidentiality) from Volvo Aero records. Supplier and in-house processing and transportation lead times were captured using historical performance data, summarised in triangular distributions, in order to model the uncertainty in the supply-chain. Fig. 2 shows the main screen of the SCMB, populated with the example data. This screen is designed to allow the user to select and review various types of supply chain and product data in detail, prior to building the simulation model. At the top is the menu bar, where the application functions are accessed. The left pane is a representation of the data model for a selected subset of the whole product supply chain, based on the desired
level of detail. An entity on any level of the product structure can be selected by the user. Product hierarchy details can be collapsed or expanded using the familiar MS Windows ‘‘TreeView’’ approach. On the right, the top pane lists component details for the selected entity (product, assembly, sub-assembly, etc.), while the bottom pane shows supplier performance data for the selected entity. The indented product BOM, as shown in Fig. 2, represents the hierarchy of components, sub-assemblies and assemblies that make up the final product. This is the primary data framework for the supplychain modelling process. BOM and supplier data are obtained from the ERP system (SAP2). Once data have been imported and validated, a subset of the data can be chosen for generation of a data model of the required supply-chain. This data model is a representation of a supply-chain (or part of a supply-chain) using appropriate data queries generated by the SCMB application from user 2
See web site at: www.sap.com/solutions/business-suite/erp.
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selections. After the user has selected the required supply-chain, or part supply-chain, a simulation model based on user-specified model configuration options is created automatically by the SCMB (with VBA code using the Arena VBA object model). The Arena model created here is a hierarchical supplychain model with three levels. The top level view represents the selected supply-chain (Fig. 3) having both material flow and order-passing mechanisms (described below). Second level sub-models show each individual supplier and transport between suppliers (one such is shown in Fig. 4), while the third level consists of sub-models representing basic assembly and routing processes within suppliers (Fig. 5 shows one example). Every supplier is represented as a set of one or more assembly/processing operations, each of which has a sub-model with associated throughput capacity, lead time and cost expressed at an aggregated level. No attempt was made in the prototype application to model detailed manufacturing opera-
tions or component flows within supplier manufacturing facilities. Transport is modelled as a specified delay, with associated cost, for each product and supplier pair, and includes any delay due to receiving goods inspection. Inventory or raw material and finished product can be held by suppliers, but the modelling of inventory and ordering for inventory are simplified. Each sub-model incorporates an error-trapping mechanism to handle incorrect arrivals, so as to increase robustness of the simulation. This is one of the mechanisms found to be valuable in a datadriven simulation system, where the linked issues of model data validation and robustness must be carefully considered. There is danger of inconsistencies such as ‘‘orphan’’ components arriving at a sub-model, having no defined means of handling, which, if not trapped, might lead to termination of the simulation. After model creation the user works directly with Arena, where the model can be run just as if it had
Fig. 3. The top level view of a data-driven supply-chain model, constructed by the SCMB.
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Fig. 4. A supplier sub-model showing enlarged detail.
been created manually, employing one or more replications to obtain supply-chain performance data. 4.3. Supply-chain control mechanisms The first version of the SCMB created supplychain simulation models without specific mechanisms for control of product flow. By default, these operated with a basic ‘‘push’’ system driven by the input of raw materials and constrained by processing and transportation times. This approach was later superseded by a version of the SCMB that incorporated a supply-chain control mechanism into every model. The primary control mechanism for this supply-chain simulation is the order-passing method. Two order-passing modes are available as model configuration options: either traditional (sequential inter-supplier ordering with associated order processing lead times), or the collaboration hub (an integration entity which simulates a web portal system transmitting demand to suppliers). In either case, the SCMB automatically creates the
appropriate elements for order-passing. An additional sub-model is created to undertake the functions of the hub. Parameters of the model control mechanism include inventory policy (zero stock, stock for finished goods only, raw materials only or both) and inventory replenishment policy. 4.4. Supply-chain performance evaluation Having created a model and run simulations of a potential or actual supply chain, it is necessary to assess the performance of the simulated system, and compare it with alternative configurations. Beamon (1998) noted that the establishment of appropriate performance measures is an important element of supply-chain design and analysis. An ability to effectively measure supply-chain performance will be critical to any extended enterprise, and to organisations within it. Tangible, quantitative metrics assessed at an aggregated (i.e. supply-chain) level are most suitable for the purposes of simulation, aiming to evaluate and compare different configurations of supply-chain,
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Fig. 5. The final assembly sub-model showing enlarged detail.
different control mechanisms and the influence of model parameters such as inventory holding policy, in both strategic and tactical contexts. The SCMB metric scorecard included the following:
Throughput capacity (per time period). Cost (including production, transportation and inventory holding cost). Fill rate by order. Order fulfilment lead time (from order receipt to customer delivery). Cycle time robustness (CTR).
The last of these requires that a variation in schedule or some operational constraint is introduced to the simulation. Supply-chain CTR is defined as the ratio of average supply-chain cycle time under variation to average cycle time without variation. This measure of flexibility and robustness is aimed at assessing the effect of sudden demand surges and disruptions in supply (e.g. a transportation problem or throughput limitation at a supplier).
5. Discussion 5.1. The conceptual model An issue which should be addressed at this point is the nature of the conceptual model in the type of data-driven simulation used in this application. The conceptual model is a non-software specific description of the simulation model to be developed, describing objectives, scope, inputs, outputs, content, assumptions and simplifications of the model (Robinson, 2004). In the approach developed by the authors, a generic conceptual model was first created and validated, during the design phase of the SCMB software and associated database (see Fig. 1). The nature of this model was constrained by the restricted nature of the supply-chain domain and determined by the functionality designed into the model-builder software and the associated database structures. The additional (non-generic) content of any specific conceptual model exists largely in the form of sets of data held in a relational database, which are
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created by the SCMB software from the user’s input data. As regards inputs, outputs, assumptions and simplifications, the user has some limited configuration options provided by the model-builder software. The user or analyst is required to set the modelling objectives and specify the model scope and content (by selecting all or part of the available input data). Data selection functionality—to select all or part of a supply chain—is provided by the SCMB, but may also be performed outside the system if necessary. For this purpose the authors developed techniques using MS Excel macros, which could be used to filter and segment very large BOM files downloaded from ERP systems. By these means the user can readily restrict the model to the specific part of the supply-chain, that it is desired to study. Robinson (2004) suggests that the chief requirements of a conceptual model are validity, credibility, utility and feasibility. The input data, which will typically be obtained from existing company systems such as ERP, are already largely validated as they are used for many other functions in the company. Further data validation can be carried out automatically, while the model is being created from the data. Rather than having to be carried out repetitively whenever a new model is built, the validity of the coded model depends only on the previously validated input data and on initial (design-time) verification of the database and SCMB software, which it should thereafter perform in a consistent and repeatable manner. Verification of the model-builder code itself remains an important (and potentially difficult) task, but once completed it does not have to be repeated for each model generated. Validation and verification will be described in the next section. The design features of the SCMB software are the critical issues with regard to the credibility and utility of the conceptual model. For example, does the model-builder software provide an appropriate means of modelling product inventory at each supplier? Feasibility (the ability to develop a computer model from the conceptual model) is primarily a matter of model size and complexity. 5.2. Verification and validation Verification is concerned with determining whether the conceptual simulation model has been correctly translated into a computer program model
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(Law and Kelton, 2000). Verification of a datadriven simulation includes all the usual verification considerations, and in addition the verification of the VBA code used to generate the Arena simulation model. A single error in critical sections of this code—for example, the code which creates an entity in Arena from specific data—could propagate throughout the simulation model and lead to serious problems. The techniques used to achieve confidence in the modelling process were:
modular program design, detailed code checking and testing, code execution tracing in debug mode, running models under simplified conditions where model output can be predicted and observing animation outputs when running models under different scenarios.
Validation is the process of determining whether a simulation model (as opposed to the computer program) is an accurate representation of the system, for the particular objectives of the study (Law and Kelton, 2000). Robinson (2004) describes various kinds of simulation model validation: conceptual model validation, data validation, white-box validation, black-box validation, experimentation validation and solution validation. Carson (2004) suggests that various techniques, similar to those used during verification, may be used during model validation, including:
Use of animations and other visual displays to communicate model assumptions. Output measures of performance for a model configuration representing an existing system or initial design, so that users may judge model reasonableness.
If sufficient data have been collected on a realworld system that matches one of the model’s possible configurations, more formal results validation may be conducted, by comparing the model to this real system. Three types of validation were carried out for the simulation models produced by the SCMB: conceptual model validation, data validation and results (black-box) validation. Conceptual model validation aims to determine whether the model specifies the required aspects (objectives, scope, content, inputs, etc.) with sufficient clarity and precision and in adequate detail, to meet the requirements of the study. Where an expert
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analyst develops a conceptual model, experience and judgement will play a key role in model design and configuration, as well as validation. The nature of the domain is also an issue: domains with limited design alternatives (such as the supply-chain) may be more suitable for an automated approach. For example, interfaces between suppliers are well defined and generic in nature. The SCMB user is provided with a limited range of standard prevalidated configuration options to define the conceptual model, before the simulation model is automatically created. The configuration options for the SCMB were supplier selection (where multiple suppliers exist for the same item) inventory level, quality conformance level and supply-chain control mechanism. The distribution of processing and transportation variations could also be specified by the user from among the options provided by Arena. Sudden interruptions to the system (uncertainty) could be specified by user-specified change in delay time with associated probability. Techniques for conceptual model validation undertaken by the authors include checking with existing theory, making use of modeller’s experience, literature surveys of similar simulation studies and continuing interaction with industrial (case study) partners. Law (2003) recommends an activity for conceptual model validation in which a structured walk-through of the conceptual model is performed before an audience that includes project manager, analysts and subject matter experts. The authors undertook this process at a series of detailed technical meetings with industrial partners at which supply-chain experts were present. Data validation is the process of determining that the contextual data and the data required for model realisation and validation are suitable for the purposes at hand (Robinson, 2004). An iterative data validation process was adopted, consisting of:
Initial data collection and model building. Data checking and correction after initial simulation results showed differences between model outputs and the real system outputs.
After completing this iterative process, the authors were confident that the input data used for the validation and experimentation were valid, such that model outputs could be compared with real system outputs. Validation of results assists with validating the simulation model through comparison of model
outputs with real system outputs (or outputs from other models). If there is an existing system, then performance measures from a simulation model of this system can be compared with those collected from the actual system. If results validation is successful, then it lends significant credibility to the simulation model. To perform results validation properly, it needs to be established first that results from the simulation model are correct to an appropriate statistical significance level. This involves the determination of number of replications and the length of warmup periods. During validation and experimentation, transient effects occurred during the simulation warm-up period, in which marked fluctuations in performance metrics occurred. This is typical of simulation for both manufacturing and supplychain scenarios. In these experiments, a steady state was reached (by inspection) before metrics were recorded. Multiple replications were employed to develop statistical confidence in the values of the performance metrics in the steady state. When perturbations were deliberately introduced into the simulation, these were introduced from the steady state. Pre-processing of product BOM and supplier data for errors (e.g. redundancy) may be valuable. So as far as possible, inconsistencies within the database should be handled without requiring human intervention, since even automated model construction may be a lengthy process. However, SCMB messages were generated where data integrity is suspect, for example, where data are missing, or where redundant elements (such as duplicate entries) were found in the database. Thus, the automatic model-building activity forms part of the model verification and validation process. A log file is produced by the SCMB containing all such error messages, and other model-building information. Preliminary simulations were carried out using the model automatically generated from the test data set; primarily to develop a greater understanding of the verification and validation issues associated with data-driven supply-chain simulation. These issues are of particular importance when the simulation model is automatically prepared, because there is less user involvement in the modelling process. There is no ‘‘expert simulation analyst’’ to consider each entity and connection, although, as described above, the modelling software incorporates code to conduct basic integrity checks on the data. The absence of the analyst,
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need not be modelled to the same level of detail as the higher-value parts, so their suppliers’ performance is represented in a simplified form). The customer (a prime engine manufacturer) was also represented in the model, as the simulation objective was to study delivery performance to this customer. Volvo held finished goods inventory, physically located at the prime company’s facility. Within the Volvo factory itself, four elements were created, representing four stages of assembly operations. The validation scenario had the following features: sequential order-passing, 100% quality conformance, inventory held for material and finished goods and the model was populated with initial inventory. Production at Volvo was driven by historical master production schedule (MPS) data (push system). The demand was modelled using actual historical demand patterns. Upstream ordering was driven by the standard inventory model (periodical review). The warm-up period was first determined before validation. Various approaches have been proposed in the literature to deal with warm-up period, each having advantages and disadvantages (Robinson, 2004). The time-series inspection approach was used here due to its simplicity and widespread use. It was implemented by inspecting a time series of weekly throughputs. A problem with inspecting a time series of a single run is that the data can be very ‘‘noisy’’, making it difficult to spot any initialisation bias. Multiple replications were employed to estimate the length of the warm-up period. The mean value of five replications for each period was plotted on a time series (Fig. 6). The regular dips in mean throughput shown in Fig. 6 correspond to vacation
however, may also be beneficial to the model as it eliminates mistakes arising from human error. Verification of the model requires testing to determine that the operation of the simulation model is logically consistent and reasonable, as assessed by outputs from simulations carried out under specified conditions. The authors consider that a reliable, standardised verification process is important for the credibility of data-driven supplychain simulation. However, the time consumed in more comprehensive verification might outweigh the gains from using the data-driven technique, so a limited set of metrics was chosen for this purpose. Similarly, complete model validation is only possible when comprehensive real-world data exist for comparison purposes, and inevitably much ‘‘what-if’’ simulation can only be partially validated. Data-driven simulation modelling suffers from this problem, as does a manual modelling process. However, the likely user (a logistics or purchasing specialist) is considered to be competent to conduct face validation of the model, based on the selected viewpoint, scope and supplier choices made. Running the generated model in the Arena environment produces the supply-chain performance data described above. Throughput is a key supply-chain metric, which was selected as the primary verification criterion. A simplified scenario, involving two levels of suppliers, was used for results validation. The first tier was Volvo Aero as the ‘‘focal company’’ and the second tier represented the upstream component suppliers, some of which were aggregated using data selection in the SCMB (class ‘‘C’’ components such as fasteners
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To further explore the validity of the simulation model and the dynamic resemblance between model and planned outputs, a visual observation approach (Kleijnen, 1995) was also used, to compare time series of simulation model outputs with the historical time series of planned outputs. The output data of the simulated system and the MPS data were plotted against time. Fig. 7 shows a comparison between the planned outputs, historical performance data and the performance of the simulation (for clarity, only a single replication is shown). Users may inspect the time series to decide whether the simulation model ‘‘accurately’’ reflects the phenomena of interest. This allows the comparison, not only of the magnitude, but also of the pattern of supply-chain throughput. Given the observed correspondence in terms of general level and behaviour between the model output and planned values, it can again be claimed that the model is valid for the types of supply-chain investigation for which it was designed.
periods near the middle and end of each year, when no production was scheduled. To determine the warm-up period, the point at which the outputs appear to settle into a steady state was identified by inspection. The point at which the data are neither consistently higher nor lower than their later ‘‘normal’’ levels and where there is no apparent upward or downward trend in the data was identified. In Fig. 6, the data appear to settle by week 42. Therefore, a warm-up period of 42 weeks was used for the results validation that follows. To validate the simulation model, its outputs were compared with real system outputs in two ways— visual and statistical. First, a confidence interval was calculated for the difference between model outputs and planned real system outputs. It should be noted that actual factory weekly throughput figures sometimes varied significantly from the planned weekly numbers, often due to trivial week-ending reporting effects. Since the simulation model was run using the planning numbers as demand, simulated throughput was compared with planned output, based on the Volvo Aero MPS. Due to possible correlations between weekly figures in successive weeks, both simulated throughputs and planned outputs were batched into monthly (4 weekly) figures. A confidence interval was then calculated, using a paired-t test to estimate the mean difference between the simulated throughputs and planned outputs. The 95% confidence interval thus obtained is ð1:5; 2:16Þ with a mean value of 0.333. Since this interval contains the value zero, the throughputs from the simulation model were not statistically different from planned output, at this significance level.
6. Conclusions This paper has introduced a concept of datadriven modelling and simulation aimed at the supply-chain and extended enterprise for the aerospace industry. Upstream supply-chains for complex aerospace products have a generally similar configuration, a framework for which can be established from data available in the product’s extended BOM. However, their behaviours and business results differ widely, influenced by issues such as supplier performance, transportation times,
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costs, etc., and also as a result of the control and integration mechanisms employed. The conclusions of the development process and of the initial testing of the models developed suggest that data-driven simulation can be a useful tool in this domain, producing models that support the design and improvement of supply-chain operations. A data-driven approach to simulation raises a number of issues with respect to simulation theory. Some of the most significant of these issues: the nature of the conceptual model, determination of model scope and content and model verification and validation have been addressed and discussed in this paper. There are various other practical issues in the development of data-driven supply-chain simulations. The availability of extended BOM data from an ERP system is, effectively, a pre-requisite for practical modelling, as a manual data entry process would be as time consuming as manual simulation. In addition, many manufacturing companies are existing users of discrete-event simulation software. Data-driven simulation, using the direct method described in the paper, is impossible unless this simulation tool has a model-building macro-language capable of dynamically creating and configuring complex models. Ideally, the macro-language should also be able to integrate readily with database and reporting tools. For this research, the selection of Arena as the simulation tool, for which VBA is the macro-language, fulfilled these requirements and brought considerable development benefits. As a general comment on data-driven simulation, the authors consider that despite the significant effort required to develop such an approach in a new domain, a persuasive case for a data-driven approach can be made. Data-driven simulation is not a replacement for general-purpose simulation tools, but an adjunct that is valuable in certain circumstances, where it is proposed to create many simulation models, allowing a variety of scenarios to be explored relatively quickly and without the level of simulation expertise that would normally be required. The model configuration and control mechanisms should also be reasonably stable and standard. It is only practicable if the structured data required to develop the model are readily available. The authors are currently conducting practical experimentation using the data-driven simulation approach. Simulations are being developed in collaboration with industrial partners, using fully
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detailed cases from the European aerospace industry. In due course, the authors intend to publish further papers describing the results of these activities, and assessing the capability and effectiveness of the approach. Acknowledgements The work presented is part of the EU Framework Six VIVACE project (contract number AIP3-CT2003-502917). The authors acknowledge the funding from the EU and collaboration from our industrial partners Volvo Aero Corporation, particularly Dr Torgny Almgren and Ms Helena Schultz. The first prototype supply-chain model builder was developed by Chang-Seop Kim, a former MSc student of the University of Nottingham.
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