DESIGN PROJECT FAILURE AVOIDANCE USING AUDITING APPROACH

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1 DESIGN PROJECT FAILURE AVOIDANCE USING AUDITING APPROACH Hsien-Jung Wu* & Hung-Wen Hsu *Dept. of Information Management, Mingdao University, Taiwan Dept. of Industrial Design, Tunghai University, Taiwan & Traditional product design management focuses on project management techniques, decision-making process and methodology of cost and schedule control. To manage multiple design projects (MDP) executed concurrently, these approaches may not pay enough attention to all situations occurred throughout projects. Unfortunately, increasing of information makes specific checkpoint review insufficient to control the project. The conflict of MDP causes more problems than those from traditional projects. Design project audit needs not only checkpoint (one-dimension) but also the overall process (multi-dimension) review. Negligence of any changes or abnormal project situation may cause project catastrophe. To ensure design project success and manage design project changes under control, an auditing system is developed to provide the function of avoiding project failure. This paper first uses Coloured Petri Nets to simulate the status of multiple design projects and analyze required auditing functions. Conflicts can be located and processed within the Coloured Petri Nets. A neural network is then created and used to recognize the error rate of route dispatching through training from previous design cases. Furthermore, Information flow of the Petri Nets is modified to avoid design project failure by adding alternatives. Keywords: design project, project failure, design project audit, Coloured Petri Nets (CPN), Backpropagation Neural Network (BPN), Design Project Audit System (DPAS) I. Introduction In 21th century, since the growth of using computers and the tool of Internet, rapid access and transfer of design project data can be accomplished regardless of time and location. The design organization of an enterprise is forced to adjust their development strategy through integrating business operation and management to enhance global competition. Advanced information and communication technology (ICT) changes the thought of managing design projects for the industry. Product design projects put into operation through international cooperation are gradually increased. Multiple and discrete design projects executed concurrently at different areas or countries become feasible and controllable. Design project audit is a series process of reviewing and solving problems occurring during project progress. It influences the whole project and includes the following issues: predict possible project risks, monitor ongoing changes, avoid future contradiction, evaluate information, empower the auditing. Project audit, which plays an important role in project success, is hardly discussed in practical design management. Negligence of any changes or abnormal situation may cause project catastrophe. To ensure design project success and manage design project changes under control, an auditing system is developed to provide the function of avoiding project failure. This paper first uses Coloured Petri Nets to simulate the status of multiple design projects and analyze required auditing functions. Conflicts can be located and processed within the Coloured Petri Nets. Design project processes, which contain information flow, can be modified to perform expected tasks through detecting problems and predicting alternatives in advance. The information flow is further recognized and controlled by the transfer of tokens described in the nets. Currently we use the Coloured Petri Nets to construct the design project audit system (DPAS) based on PC platform and Microsoft software to develop system and required tools. A Web-based design audit system is presented to audit multiple design projects in Internet environment. The next step in this paper is to create a neural network based on Backpropagation structure. Data collected form previous design projects provided by three design studios are fed into this neural network

2 for training and testing. Abnormal error rate at specific place or transition of the Petir Nets is then marked to modify the route dispatching. The result shows potential project failure caused by ambiguous design processes can be identified. The training and testing process performed during neural network construction reflects the elusive nature of designer thinking. The advantage of using the design audit system proposed in this paper is to express this realistic situation and add alternative of decision making into design process. Supplementary information generated throughout the design project can then be integrated with the process of design project audit. II. Related Works Bill Gates [1] mentioned the key for the enterprise is speed in the new century rather than the issue of quality and reengineering. It is essential for an enterprise to rapidly acquire right knowledge and reach its goal through the Internet environment. This trend also works in the process of managing the design project, particularly for those multiple projects executed concurrently. Type of projects influences the generation of information flow and process of management. In terms of project management, Evaristo and Fenema [2] defined project as the following types: 1) single project: traditional project and discrete project; 2) multiple project: co-located project, multiple co-located project, multiple traditional project and multiple discrete project. Multiple projects also can be divided into types of discrete location and shared location. They recognized possible evolution among these types of project and technology required managing these projects maybe generated and adapted. Voropajev [3] discussed several issues in project changes such as scope, quality, time, cost, risk, contract, human resource and information. The method of dealing with changes are monitoring, forecasting, control and verification. Johns [4] emphasized that there requires frequent communication in an organization to build project priority and review project process and human performance periodically. Heindel and Kasten [5][6][7] described the trend of developing the PC-based project management systems: refine current management examples, improve the usability of graphical user interface, and improve the accessibility of documentation. They developed a co-called P++ structure based on MS-Windows TM environment and used MS-ACCESS TM and MS-Project TM as the tool for development and test. This structure refers the concept of Central Relational Database Repository to connect project planner, R&D staff and project manager through the common graphical data interface. Herbruck and Umbach [8] pointed out the importance of collecting right information including technology and finance requirement to obtain the opportunity of product development. Graphical representation are usually used in project management such as Gantt Chart, Flow Chant, IDEF and PERT that provide the feature of understanding the relationship between project steps. Similar to these representation schemes, Petri Nets is a graphical and mathematical modeling tool for use to express parallel, dynamic and concurrent features of a system [9]. Kenichi [10] proposed a Stochastic Petri Nets (SPN) to develop the management information system which owns the advantage of smoothing work flow, finding out potential unbalance and control project operation. It provides more analysis and differs from flow chart. Knapp and Wang [11] presented that Petri Nets can offer conflict resolution and improve the flexibility of concurrency in a project. Basically, a design project management needs learning due to the complexity and uncertainty of design process. Artificial neural network has the characteristics of processing large amount of information in a parallel manner, fault tolerance, associative memory and self-learning, etc. Among those neural network algorithms, backpropagation provides quick recall to process pattern recognition problems [12]. In this paper, a graphical representation based on colored Petri Nets is used to construct the design audit system followed by a Backpropagation neural networks used to train design samples and modify audit processes. III. Design Project Audit Audit is a commercial term usually represented as any type of review or inspection. It is combined with other management issues of quality, technology and pressure. An audit process should analyze factors of economics, human concerns and other social topics in an organization. This task is more difficult than that of finance audit since the definition is ambiguous [13]. It can be divided into three parts: finance audit, operational audit, and compliance audit [14]. Design project audit discussed in this paper emphasizes the latter two topics. Therefore, design project audit is a series process of reviewing and solving problems occurring during project progress. It performs auditing tasks under a common base of audit structure rather than different audit structures run on each project. As a result, the same audit structure is applied to multiple design projects. It provides overall review instead of particular point check and includes the follows:

3 Project risk prediction Change monitoring Conflict avoidance and resolution Information evaluation Empowerment Design audits should bring up all processes required for executing review and control tasks since it is supposed to solve problems occurred in the design project. Figure 1 shows the base of design audit throughout the stages of a product design. After going through five audit tasks: forecasting, consider, evaluate, monitoring and control; each design stage defined in the Petri Nets is transferred to the next stage. These stages include design planning, industrial design, mechanical design, and inspection. Inside the base of design audit is the central design database, which provides the connection with outside environment to execute status checking, dispatching and editing modules. These three modules can be gradually refined through the Internet environment. Since abnormal situation can be recorded during design project implementation, knowledge and information are shared and modified based on current information technology [15]. IV. CPN Graph of Design Projects Audit Similar to other graphical tool, Petri Nets uses flow chart and node-and-arc network as representation for communication. The token run in the Petri Nets plays the role of indicating dynamic and concurrent features. It starts with the initial marking, and consists of several Places and Transitions. A Petri Net is defined as [11]:PN = (P, T, F, G), where P = {p 1, p 2,, p m} is the set of places and T = {t 1, t 2,, t n} is the set of transitions. F P T from P to T indicates the actions of input, and G T P form T to P is the action of output. Coloured Petri Net (CPN) enhances the Petri Net to: CPN = (P, T, K, F, G), where K ={k 1, k 2,,..,k p} represents the set of tokens. To simulate the design audit process, a place indicates all procedures executed in a design project such as feasibility analysis, design specification, idea sketch, working model, etc. Transition on the other hand represents the tasks of audit: forecasting, consider, evaluate, monitoring and control. A token is then used to simulate the progress of a design project where different colors of tokens mean concurrency of multiple projects. This paper uses this method to simplify and formalize the complex design processes. The transfer of tokens clearly shows the path and the result of design audit. Through this simulation, resource allocation and conflict resolution can be analyzed. Priority of different design process is also changeable. Figure 2 shows the modeling result of CPN graph to simulate design project audit. Four groups are defined: 1) p1 -> p3 as planning stage, 2) p4 -> p6 as industrial design stage, 3) p7 -> p9 as mechanical design stage and 4) p10 -> p13 as mold-making stage. V. BPN training and testing This paper uses software called PC Neuron as the tool based on Backpropagation model to examine the previous design samples provided by three design studios. Initial setting of this neural network are listed as follows: 50 samples, 22 units considered in the input layer, one hidden layer, and 1000 training Figure 1. The base of design audit

4 Figure 2. Model graph of design project audit error rate t1 t1 t2 t2 t3 t3 t4 t4 p5-t5 p6-t5 t6 t6 p8-t7 p9-t7 t8 t8 t9 t9 t10 t10 數列 design stage Figure3 Error rate of each stage cycles. 35 of 50 samples are used to train the network where other 15 are for the testing purpose. Totally 12 stages are analyzed through t1 to t10. The result shown in Figure 3 indicates that error rate are too high from t4 (idea sketch) to t7 (engineering drawing). Possible reasons for this training and testing result under the experimental setting are discussed as follows: Unlike general project management, project of product design has more uncertain factors generated through the communication between the project team and the customer. Therefore, tasks of design audit have more responsibility of improving project performance. In the case study examined in this paper, overall design audit is required particularly in the stage of industrial design and mechanical design. A. Too many uncertainties exist between two design stages that cause frequent design change. B. The number of original samples is insufficient to generate convincing data. C. The input factors are chosen by design studios and project manager s subjective experience, which ignores possible unexpected changes requested by customers. Based on the result obtained from the experiment of the BPN, we found several procedures simulated in the CPN graph should be modified to reduce the error rate. Figure 4 shows the modified version of the CPN graph of design audit. In this figure, it explains more transparently about the route dispatching which helps decision making. For example, nodes from p5 to p7 are redirected to add alternative and design audit procedure here should be rearranged. The base of design audit mentioned in previous section also share the modification to complete alternative editing.

5 Figure 4. Modified CPN graph of design project audit VI. Design Project Audit System To combine the result of two phases of simulation and learning, this paper aims to integrate the role of project audit with product design processes. Therefore, we develop a web-based design audit system to improve project progress from the viewpoint of project failure avoidance. The proposed system uses PC as the platform under Windows 2000 environment and combines Microsoft Project, Outlook and Access software. The portal of design project audit also can connect with project assignment module, information transferring module, and on-line communication module. After login in the portal, the system connects CAD software and shares the same working environment. The major portion of this system is the design projects audit where controls the flow of design projects. The simulated CPN graph indicates the project progress is shown in the Web page. The position of token represents the interconnection between different projects. It also links with the base of design audit to complete the required audit procedures. Based on the results from the experiment, the participant design studio reports that they can reduce the time of searching related information and progress review. More design samples are continuously collected and fed into the BPN for learning purpose. It is estimated that 20% project efficiency improvement can be expected if the audit procedures described in the CPN graph are coincided with their current setting. Since the system is still under development, this anticipation needs more investigation and data analysis. Especially, the operation of the manager should be put in consideration. The issue of error rate occurring while learning will be discussed in detail. VII. Conclusions This paper applies Coloured Petri Nets and artificial neural network technology to simulate the structure of multiple design projects audit. The CPN graph is modified based on the training result collected from the backpropagation model. As a result, a Web-based design project audit system is developed in this paper to speed the project progress under several audit procedures. The particular high error rate found in the BPN is recognized as potential project failure due to project uncertainty. To avoid project failure, the CPN graph of design project audit can be regenerated throughout the project progress. Ongoing research will focus on the issue of adjusting the base of design audit including priority setting and recovery procedure generation. References [1] Gates, Bill. Business@The Speed of Thought: Using a Digital Nervous System, Warner Books, Inc., [2] Evaristo, R. & Fenema, P.C. van, A typology of project management: emergence and evolution of new forms, International Journal of Project Management Vol.17, No5, pp , [3] Voropajev, Vladimir. Change management-a

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