Integrating Emergent Problem-Solving with ...

Integrating Emergent Problem-Solving with Construction Knowledge Management System —Lesson Learned from an A/E Consulting Firm Wen-der Yu1, Cheng-te Lin2, Cheng-tien Yu3, Shen-jung Liu4, Huai-ching Luo5, Pei-lun Chang6, Abstract Construction industry is faced with emergent problems and crises every day due to the contracting nature, fragmented organization, uncertain environment, and the changeable construction site. Speed and efficiency of problem-solving plays a key role not only for the survival but also for improving the service quality of the firm. This paper presents a case study on the integration of knowledge management system (KMS) with emergent problem-solving system (SOS), namely Knowledge Management integrated Problem-Solver (KMiPS), in a local A/E consulting firm, China Engineering Consultants, Inc. (CECI). It is found that such integration can result in major benefits that contribute to the improvement of the firm’s long-term competitiveness. A case study found that the average 50.88% time-saving, 63.53% man-hour decreasing, and 84.40% cost effectiveness were achieved. Moreover, the knowledge sharing on emergent problem-solving among the participating engineers/managers enriches a repository of intangible intellectual assets for the firm. Keywords: Knowledge management, problem solving, information technology, case study. 1. INTRODUCTION Construction management activities are fundamentally problem solving activities [1,2], where construction managers and engineers are faced with emergent problems and crises in their daily business operations, e.g., the bidding decision, design modifications, material selections, construction method 1

Professor, Institute of Construction Management, Chung Hua University, Hsinchu, Taiwan; Phone: +886-3-518-6748, E-mail: [email protected] 2. Master Student, Department of Civil Engineering, National Taiwan University, Taipei, Taiwan; Phone: +886-2-2736-3567#2915, E-mail: [email protected] 3. Ph.D. Student, Department of Civil Engineering, National Taiwan University, Taipei, Taiwan; Phone: +886-2-2736-3567#2901, E-mail: [email protected] 4. Manager, Department of Business and Research, China Engineering Consultants Inc., Taipei, Taiwan; Phone: +886-2-2736-3567#2850, E-mail: [email protected] 5. Section Chief, Department of Business and Research, China Engineering Consultants Inc., Taipei, Taiwan; Phone: +886-2-2736-3567#2868, E-mail: [email protected] 6. Engineer, Department of Business and Research, China Engineering Consultants Inc., Taipei, Taiwan; Phone: +886-2-2736-3567#2873, E-mail: [email protected]

determinations, site condition variations, change orders, dispute resolutions, etc. Some of the problems are due to the internal nature of the industry, such as the contracting system and fragmented organization; the others are caused by the external factors, such as the uncertainty in construction works or environment (e.g., site or weather conditions). No matter which reason causes the emergent problems, the requirements for problem-solving are always correctness, speed, and efficiency; that is, the problems should be solved correctly, in shortest time, and cost-effectively. Li and Love [2] found that construction problems pose several characteristics that should be tackled in order to solve them quickly, correctly, and cost-effectively; these characteristics include: (1) ill-structure nature—thus experimental knowledge plays important roles; (2) inadequate vocabulary—thus communications between researchers and practitioners is important; (3) little generalization and conceptualization—first solution is usually adopted, no guarantee on optimal solution; (4) temporary multi-organization (TMO)—relevant organizations have to work together in order to reach a consensual solution for all parties; (5) uniqueness of problems—it is hard to accumulate experiential knowledge from construction practices; and (6) hardness in reaching the optimal solution—adequate measures are required to evaluate the performance of problem solving, including quality of resultant solutions and their benefits. In Li and Love’s research, they found that the abovementioned characteristics are generally tackled in two areas of problem solving researches: the cognitive science and decision support system (DSS). No quantitative measures were provided on how well the two approaches have accomplished in real world implementations. In this paper, a case study is conducted on a leading A/E consulting firm (China Engineering Consultants Inc., CECI) in Taiwan focusing on its specialized knowledge management system (KMS) for solving emergent construction problems, which forms a Knowledge Management integrated Problem-Solver (KMiPS). The approach adopted by CECI is different from the two approaches described by Li and Love but rather based on the Theory of Organizational Knowledge Creation proposed by Nonaka [3]. Both quantitative and qualitative benefit evaluations are performed in the case study. It is found that not only the prescribed characteristics of construction problems are well tackled, the KMiPS approach achieves average 50.88% of time-saving, 63.53% of man-hour decreasing, and 84.40% of cost effectiveness; or equivalent to TWD 14,148,420 (or USD 431,354) of saving for 586 problem-solving cases. The authors believe that such experience can be very beneficial to other construction firms who are suffering in emergent problem-solving everyday.

The rest of this paper is presented in the following manner: perspectives of construction problem-solving and issues of knowledge management are reviewed in Section 2; in Section 3, the background of case study is described including the case A/E consulting firm, the KMS of the firm, and the emergent problem-solving system; the KMiPS is described in details in Section 4; both quantitative and qualitative benefits of the KMiPS in the case firm are evaluated in Section 5; findings and discussions are discussed in Section 6; finally, the conclusions and recommendations are presented in Section 7. 2. LITERATURE REVIEW 2.1 Review of Problem-Solving in Construction Three perspectives of construction problem-solving are reviewed in this section, including two perspectives mentioned by Li and Love [2] and the organizational knowledge creation perspective proposed by Nonaka [3]. 2.1.1 Cognitive processing perspective The cognitive perspective of problem-solving focuses on developing models to explain the cognitive phenomena occur in the problem-solver’s mind during problem-solving process. Simon proposed an “intelligence-design-choice (IDC)” model [4], where problem-solving is considered as the rational process involving recursive generation and interpretation of solutions. Newell and Simon modified the IDC model to the “generate-and-test (GAT)” model [5]. In GAT model, the potential solutions to the problem are generated and tested by the problem solver to evaluate their fitness to the solution goals. Other cognitive models can be found in Lang et al. [6] and Cowan [7]. The primary objective of cognitive science is trying to provide a generic model for problem solving, and so as to equip the problem-solver with explanation and planning capabilities for optimal solutions [8]. However, researchers of the other perspectives (e.g., information processing) may argue that the findings in cognitive modeling are not strong enough for practical implementation in construction problem solving [2]. 2.1.2 Information processing perspective Compared with the cognitive processing perspective, the information processing is relatively a well-researched area in construction problem-solving. In the past decades, researchers have engaged in developing problem-solving systems that can systematically solve well-structured problems step-by-step. Such systems are named Decision Support Systems (DSSs). Two basic approaches to develop a DSS is operation research (OR) and artificial intelligence (AI). The OR approach requires construction problems to be structured or formulated in

equations. The essential principle of OR problem-solving is optimization theories including Linear Programming (LP) [9], Integer Programming (IP) [10], Dynamic Programming (DP) [11], system simulation [12], etc. The AI approach requires a representation scheme to describe the construction problems for the domain experts. The foundation of AI problem-solving is the experience or knowledge acquired from the domain experts or human intelligence. Popular AI techniques adopted for developing a DSS include Expert Systems (ES) [13], Case-Based Reasoning (CBR) [14], Artificial Neural Network (ANN) [15], Fuzzy Logic (FL) [16], Genetic Algorithm (GA) [17], etc. In contrast to the cognitive processing, information processing perspective tackles the problem-solving task in three phases [2]: (1) the formulation of an explicit statement of goals for the problem to be solved; (2) identification of solution space; and (3) selection of the optimal alternative among all feasible solutions. However, either the problem formulation in OR approach or the problem representation in AI approach is difficult, if not impossible, for the problem solver due to the nature of construction problems. 2.1.3 Organizational knowledge creation perspective Nonaka [3] proposed a four-dimensional model for organizational knowledge creation (also known as “spiral of organizational knowledge creation”) that can be considered as another approach for problem solving. The concept of Nonaka’s spiral of organizational knowledge creation is depicted in Figure 1, where the vertical axis discriminate the knowledge type into “explicit” and “implicit”. The horizontal axis differentiates the ontology of knowledge creating entities, e.g., individual, group, organization and inter-organization. The problem is solved via the process of knowledge creation. That is, the problem is communicated between the questioner and the problem-solver through “Socialization”—transferring personal tacit knowledge to tacit knowledge of the other individual; then, the problem-solver documents the problem in words or drawings through “Externalization”—transferring individual’s tacit knowledge to explicit form so that the public can access and utilize; with some aids of external databases/knowledge bases, the problem-solver figures out the solution through “Combination”—transferring explicit knowledge to explicit knowledge by combining two or more sources of codified explicit knowledge to generate a new entity of explicit knowledge; finally, the experience of problem-solving is accumulated in the problem-solver’s mind for future use through “Internalization”—transferring explicit knowledge to tacit knowledge of individual. The four types of knowledge transformation—socialization, externalization, combination, and internalization—are called the four dimension

of knowledge creation. They can also be viewed as the four phases of the problem-solving process. Among those, the socialization is related to group process/ organization culture. Actions that building a better knowledge sharing culture or atmosphere can improve socialization; the externalization process is currently supported with no organizational theory; the combination is related to information processing, therefore software and hardware systems can stimulate combination activities; and internalization is related to organizational learning (OL), approaches of OL can be adopted to improve internalization process.

Explici knowledget

Tacit knowledget

E pi s t e m o l og y D i m en s i o n

Combination

Externalization

Socialization Individual

Group

Internalization

Organization

Interorganization

Ontology Dimension

Figure 1 Spiral of organizational knowledge creation [3]

2.2 Review of Knowledge Management in Construction 2.2.1 Knowledge management in construction Construction industry is highly knowledge and experience based. Knowledge and experienced accumulated from previous works is employed in solving problems confronted in future projects. As a result, more and more construction firms and organizations have adopted knowledge management (KM) in various forms and functionalities to facilitate the process of creating, acquiring, capturing, sharing, and utilizing knowledge so as to improve the competitiveness, productivity and efficiency of their organization [18]. Kim et al. [19] proposed a practical method for capturing and representing knowledge that is critical in knowledge management. The method employs a knowledge map as a tool to represent knowledge. The procedure consists of six steps: (1) defining organizational knowledge; (2) analyzing process map; (3) extracting knowledge; (4) profiling knowledge; (5) linking knowledge; and (6) validating map knowledge. Effective knowledge maps help identify intellectual capital, socialize new members, enhance organizational learning, and anticipate impending threats and/or opportunities [20]. The knowledge map has been

adopted in establishment of KMS in most of the prevailing commercial KMS software. Johansson [21] concluded from tremendous observations of great inventions of the world and proposed a new concept of “the Medici effect” for knowledge creation. According to F. Johansson, the innovators are changing the world by stepping into the Intersection: a place where ideas from different fields and cultures meet and collide, ultimately igniting an explosion of extraordinary new discoveries. Johansson calls this proliferation of new ideas “the Medici effect”, referring to the remarkable burst of creativity enabled by the Medici banking family in Renaissance Italy. Johansson explains that three driving forces (the movement of people, the convergence of scientific disciplines, and the leap in computational power) are increasing the number and types of intersections we can access. The Medici effect provides a theory for how the communities of practice (COPs) work in a KMS. 2.2.2 Performance measurement of the KMS System Swaak et al. [22] conducted as survey and concluded that there are two major measurement approaches related to knowledge management results: (1) questionnaire approach; (2) multiple indicators approach. Within the ‘questionnaire approach’, a questionnaire with closed and open questions, completed by participants of a KMS reveals the profile of an organization. Usually, the profile is used in subsequent interviews and workshops. Within this approach, major concepts are ‘extent of knowledge sharing’ and ‘learning potential’ of an organization. The ‘multiple indicator approach’ roughly makes a distinction between ‘customer capital’, ‘innovation capital’, ‘financial capital’, ‘internal business processes’, and ‘human capital’. For each category, a large number of indicators-- mostly objective and quantitative-- is collected. There are very few research reports found in literature on performance evaluation of a KMS. The most related work discovered in literature was a work done by del-Rey-Chamorro et al. in Cambridge University [23]. They developed an eight-step framework to create performance indicators for knowledge management solutions. The framework consists of three stages: (1) strategic level—comprising of measures that evaluate the organization’s goals; (2) intermediate level—comprising indicators that link the process performance indices at the operational level to the business performance indicators in the strategic level; and (3) operational level—comprising indicators that represent the measurable process performance of a KMS. del-Rey-Chamorro et al.’s work can be very useful for creating performance indicators of a KMS, however, their work was primarily developed based on the observations of KMS in manufacturing

industry. A recent work reported by Mezher et al. on a KMS in a mechanical and industrial engineering consulting firm [20] in middle-east is closely related to this paper. Their paper details the step-by-step implementation of KMS in the case company and lessons learned on the benefits of KMS implementation. Unfortunately, their work didn’t describe the evaluation of the performance of KMS. However, at the end of the paper, the authors addressed: “(Future researchers) should set up some quantitative measures to show the financial benefits of the KMS”. It pointed out the importance of quantitative performance evaluation for a KMS. Even though previous work on quantitative performance evaluation of KMS was rare, the similar study in performance measurement (PM) area is quite plenty even in construction industry. Bassion et al. addressed that in developing a conceptual framework for measuring business performance in construction should take into account the organization’s business objectives [24]. They also conducted empirical experiments on two case construction firms in UK. A systematic analysis model based on IDEF0 was also developed for the proposed framework. Bassion et al.’s work was theoretically based on some existing performance measurement systems such as Balanced Scorecard (BSC) [25], European Foundation for Quality Management (EFQM) excellence model [26], and Key Performance Indicators (KPI) [27]. The above systems provide useful indicators that can be adopted for performance evaluation in the present research. 3. BACKGROUND OF CASE STUDY 3.1 The Firm The case A/E firm is one of top three A/E firms in Taiwan. It was established in 1969 primarily for the purpose of promoting Taiwan's technology and assisting in the economic development of Taiwan and other developing countries. The number of full-time staffs of the firm is about 1,700. Among those around 800 are in-house staffs in headquarter located in Taipei, the other 900 are allocated in branches and site offices around the island. Headquarter, braches, and site offices are connected by Intranet. The structure of the case A/E firm consists of five business groups: (1) Civil Engineering Group; (2) Railway Engineering Group; (3) Electrical and Mechanical Engineering Group; (4) Construction Management Group; and Business and Administration Group. Each business group includes several functional departments. The annual revenue of case A/E firm is around 4 billion TWD (128 million

USD). According to the information disclosed by the firm, more than 1,700 A/E projects were finished in the past thirty years. Totally volume (construction budget) of the finished projects exceeds 300 billion USD. The case A/E firm is a multi-group international consulting firm, which is structured around a number of departments. These departments are either engineering or service departments. Service departments are those help engineering departments achieve their goals. The above departments complement each other and ultimately produce complete fully integrated design documents. These documents are preliminary technical studies, plans of design drawings, technical economic feasibility studies, specifications, methods of operation of projects, and Tender documents. Services provided by the case A/E consists of the following area: z Studies, investigations and surveying z Highways and freeways z Railways and high speed rail z Rapid transit systems z Airport works z Harbor works z Bridges and structures z Architecture z Urban planning /land development z Environmental engineering z Tunnels & geotechnical engineering z Electrical & mechanical engineering z Information network applications z Hydraulic/water resources engineering z Information technology and systems z Traffic control and management z BOT general consultant services z Construction supervision and management z Testing and monitoring 3.2 The Knowledge Management System The implementation of KMS in the case A/E firm started five years ago. Unlike most of other examples of KMS implementation, the case A/E firm chose to develop the KMS completely by their own staffs without help of external consultants. At the beginning, the KMS was proposed by the Department of Business and Research. Soon, it was realized that engineers of Department of IT

should be included in order to resolve the technique problems encountered in implementation of prototype system. Commercial software, Microsoft SharePoint® was adopted to develop the KMS. The system development took one year to complete the prototype. The prototype KMS began to operate after one year of the project commencement. It was found quickly that development of software KMS is not a tough job compared with the building of the culture and atmosphere for successful operation of the KMS. More that 40 communities of practice (COP) were established. The number of COP is varying based on an enter-and-exit regulation. That is, continuous evaluation of COP is performed to determine whether it should be maintained or closed down. The manager of COP is in charge of all activities for promotion of the knowledge creation in that COP. Incentives were provided by the company to stimulate the establishment of knowledge sharing atmosphere. To date, the KMS has been operating for three years. The KMS has been modified quite a bit from its prototype three years ago. One of the most significant modifications was the introduction of SOS system for emergent problem solving. 3.3 The Emergent Problem-Solving System (SOS) The SOS system is a special design of the KMS of the case A/E firm, which provides a tentative forum for emergent problem encountered by engineers/managers. Once the problem is posed as SOS-problem, it is posted in the SOS board on the first page of the KMS for emergent discussions. Such arrangement forces every participant of KMS to take a look at the posed problem. So that it generally receives attentions and usually has a better chance to be solved by responders. Problems posed on the SOS board receive no response within one working day will be automatically removed and transferred to relevant COP. After then, it becomes regular topic for discuss in COP. 3.4 Type of Emergent Problems Faced with the Case A/E Firm There are basically eleven categories of emergent problems facing the case A/E firm: (1) Requests of client—requests of the client can be very diversified but emergent, e.g., an assessment of the impact of a change, preparation of a RFP that is not included in the contract, evaluation of a set of different alternatives, etc.; (2) Reaction to accident—accidents are always omnipresent and emergent on construction site, problems in this category may include the mediation process or the remedy to an accident; (3) Dispute/Contract execution—problems in this category may be related to the interpretation of contract articles and should be determined within a time-bound; (4) Material and equipment—problems in this

category are mostly related to on site activities or a pre-construction planning; (5) Safety/Environment—problems in this category may relate to regulations of the government; (6) Request of engineering information—problems in this category are also diversified, which include the information of bid items or a design, construction method, etc.; (7) Completion and transfer—the problems that may happen when the project is completed and is transferring to the client; (8) SPEC and criterion—problems relate to technical specifications or design codes; (9) Problems with contractors/sub-contractors—problems in this category are those raised by the contractors or sub-contractors, such as schedule extension or claims of additional cost reimbursement; (10) Internal process of the firm—problems in this category are related to the business/administration process of CECI; (11) the others—all problems not belonging to the above categories. In this case study, the more than 800 problem-solving cases of CECI were collected from 2004/6 to 2006/9. After reviewing and screening, 586 emergent cases were selected for case study. The distribution of the 586 emergent problems in the eleven categories is shown in Figure 2.

Internal process of the firm 1% Problems with contractors/subcontractors 1%

Requests of client the others 6% 6%

SPEC and criterion Completion and 26% transfer 1%

Reaction to accident 1% Dispute/Contract execution 4% Material and equipment 14%

Request of engineering information 36%

Safety/ Environment 4%

Figure 2 Distribution of emergent problems in the case A/E firm

4. KNOWLEDGE MANAGEMENT INTEGRATED PROBLEM-SOLVER (KMiPS) The emergent problem-solving method adopted by the case A/E firm is an emergent problem-solver integrated with the knowledge management system (KMS) of the firm; tentatively named Knowledge Management integrated Problem-Solver (KMiPS). In this section, the KMiPS system will be described in details. 4.1 System Framework The system framework of KMiPS is depicted in Figure 3. The KMiPS is comprised of four major elements: (1) Problem diagnosis module—a pre-screening module that assesses the level of emergency of the posed problems; (2) SOS—a specialized community of practice (COP) with the top priority on the firm’s enterprise information portal (EIP), which provides a forum for all staffs and domain experts to participate in the problem-solving process; (3) Domain experts—a pool of firm’s internal and external specialists in all areas related to the services provided by CECI; (4) the firms KMS.

Problem posed

Problem solved Engineer/M anager

Search knowledge bases/ databases

Problem Solved?

KMS Problem Diagnosis

Knowledge Base

Data Base

Domain Experts (D Es)

DE 1

DE i

C ommunities of Practice (C OPs) COP 1

C OP 2

Figure 3 Framework of KMiPS

C OP j

4.2 System Operation Procedure The operation of KMiPS shown in Figure 3 follows the procedure: (1) as the problems posed by any engineer/manager, he/she should search the knowledge bases and databases of the firm first and enter the KMiPS if the problem is not solved; (2) the problem diagnosis module assess the emergency level to determine if the problem should be posted in the SOS system; (3) should the emergency level of the posed problem is low, it is posted in the related COPs of KMS; (4) should the problem is emergent, it is posted in SOS on the first page of EIP and the selected COPs of KMS simultaneously; (5) domain experts (DEs) form internal and external of the firm participate in discussions of the problem solution through a organizational knowledge creation process; (6) as the solution is obtained, lesson-learned from the problem-solving is documented into knowledge base of KMS for future problem solving. 5. BENEFIT ANALYSES The KMiPS described in previous section has been proven very successful not only in solving emergent problems encountered by the engineers/managers of the firm, but also in accumulating the lessons-learned from executed problem-solving cases. In this section, both quantitative and qualitative of the KMiPS system in problem solving are analyzed. 5.1 Data Collection and Questionnaire Survey The SOS system was implemented since June the 1st of 2004. The period of data collection is from 2004/6/1 to 2006/9/26. Totally, 654 SOS problem-solving cases were collected. A web-based system, namely Knowledge-management-activity Survey Module (KSM) was developed for questionnaire survey and data collection. For every SOS case, both the questioner and responders were surveyed with KSM. The questionnaire was surveyed with the “questioner” with the following information: (1) Whether the problem was solved or not? (2) Evaluation of the “level of contribution” (scale 1~5) of each solution from the responders; (3) Additional time spent to develop the final solution; (4) The numbers of meetings, phone calls, and interviews required to develop similar solution via traditional approach; (5) Average time required for meetings, phone calls, and interviews required respectively to develop similar solution via traditional approach. The questionnaire was surveyed with the “responder” with the following information: (1) The time required to develop the solution; (2) The time spent in-office and after-work, respectively, to develop the solution. Totally, 5,011 questionnaire surveys were conducted via KSM. Finally,

454 complete SOS cases and 3,250 valid responses were obtained. 5.2 Quantitative Benefit Analysis In order to measure the benefits resulted from the KMiPS system, a quantitative benefit model was proposed by Yu et al. [28] to quantify (1) time-benefit—saving of time required to solve a problem with KMiPS compared with the time required in the traditional process; (2) man-hour benefit—saving of man-hours required to solve a problem with KMiPS compared with the man-hours required in the traditional process; (3) cost benefit—saving of cost spent to solve a problem with KMiPS compared with the cost spent in the traditional process.. 5.2.1 Quantitative models The time benefit measures the saving of problem solving time with the aid of KMiPS compared with traditional problem solving approach. According Yu et al. [28], the time benefit is measured in the following with Equation (1): TB (%) =

NDT − NDS × 100% NDT

(1)

Where, NDT is the working days required for traditional problem solving approach, it is estimated by the problem raiser based on previous experience; NDS is working days required for problem solving with KMS, it is calculated with data recorded in KMS system and questionnaire survey with the problem raiser; TB is the ratio of time saving (time benefit) measured in percentage (%) Man-hour benefit measures the saving of efforts needed to solve a problem with KMiPS compared with the traditional approach. In order to calculate man-hour benefit, both man-hours spent in traditional approach and KMiPS approach need to be recovered. Traditionally, the problem is solved via meetings. Usually, only staffs of the relevant department are gathered in meetings. The problem solving process in KMiPS is quite different. In KMiPS, the problem is posted in a COP and all members of the COP participate in discussion of the problem solution. Such arrangement allows engineers with different contexts intersect one another in a COP so as to make Johansson’s “the Medici effect” happens [21]. According Yu et al. [28], the ratio of man-hour saving is calculated in the following equation: MHB % =

TTT − STT × 100% TTT

(2)

Where, TTT and STT were man-hours spend in traditional approach and in SOS respectiely; MHB is man-hour benefit in percentage (%)。

Cost benefit is always a central concern of top management of a firm as the cost relates directly to the financial performance. One of the primary objectives of KMIPS investment is cost saving. The cost benefit measures difference of costs between the traditional approach and KMIPS approach in solving the same problem. According Yu et al. [28], the ratio of cost-saving is calculated in the following with Equation (3): CB % =

TTC − STC × 100% TTT

(3)

Where, TTC and STC were total costs required in traditional and in SOS approaches, respectively; CB is cost benefit in percentage (%). 5.2.2 Quantitative benefits resulted The quantitative benefits resulted from KMiPS based on the models described previously are: (1) 50.88% of time benefit; (2) 63.53% man-hour benefit; and (3) 84.40% cost benefit. Other quantitative data describing the benefits of KMiPS are shown in Table 1. From Table 1, it shows that the KMiPS saves 8,279 man-hours in solving the 454 emergent problems compared with the traditional approach, and this means a cost saving of USD $431,354. Table 1 Data of benefits resulted from KMiPS Decreased No. of required meetings (times)

250

Decreased No. of participants in the required meetings (persons)

522

Decreased No. of required meeting hours (hours)

192

Decreased No. of required phone call hours (hours)

219

Decreased No. of required phone calls (times) Decreased No. of required interviews (times) Decreased No. of man-hours (hours) Total saving ($USD)

1,273 324 8,279 431,354

5.3 Qualitative Benefit Analysis In contrast to the quantitative benefits, the qualitative benefits resulted from KMiPS are not so obvious; however, they are no less significant. Identification of the qualitative benefits is difficult. In this research the qualitative benefits were identified through interviews with the engineers/managers of CECI who participated in KMiPS problem-solving. The identified quantitative benefits include (but not limited to): (1) increase of the firms intellectual property—during the process of organizational knowledge creation, all participants (including whom read but didn’t participants in discussions) accumulate their knowledge related to the posed problem, and the accumulated knowledge may be used the

other day; (2) solving of problems that cannot be solved before—in the traditional approach, the problem is discussed and solved by a taskforce with only limited members from different disciplines, which may exclude the real experts (who have solved the similar problem before) of the problem due to the unavailability of any reason; (3) increase of the Medici Effect—when integrated with KMS, the KMiPS can incorporate all staffs of the firm and thus maximize the Medici Effect and increase the possibility of problem solving; (4) improvement of client satisfaction—as the KMiPS shortens the required problem-solving time, client satisfaction is significantly; (5) improvement of the sense of belonging—the “sense of belonging” to an organization is a spiritual property that promote the competitiveness of the firm; with KMiPS engineers/managers and other staffs share the pressure of work and the pleasure of problem-solving, which improve the “sense of belonging” of all participants to the organization. 6. DISCUSSIONS From the case study of CECI and its specialized emergent problem-solving system, KMiPS, it is found that many issues in problem-solving have been improved or tackled properly. In this section, findings of the improvements of KMiPS in problem-solving are discussed. (1) ill-structure nature—thus experimental knowledge plays important roles; (2) inadequate vocabulary—thus communications between researchers and practitioners is important; (3) little generalization and conceptualization—first solution is usually adopted, no guarantee on optimal solution; (4) temporary multi-organization (TMO)—relevant organizations have to work together in order to reach a consensual solution for all parties; (5) uniqueness of problems—it is hard to accumulate experiential knowledge from construction practices; and (6) hardness in reaching the optimal solution—adequate measures are required to evaluate the performance of problem solving, including quality of resultant solutions and their benefits. 6.1 Integration of Cognitive and Information Processing Perspectives The KMiPS adopts both the cognitive and information processing problem-solving perspectives. It utilizes the KMS (an advanced information technology) for information processing (searching of knowledge base and database, and problem communication); however, the knowledge representation problem of traditional Information Processing approach is tackled with nature language in the COP. Moreover, problems with traditional cognitive science approach of problem-solving are improved with formulated lessons-learned of solved cases, so that the lessons-learned can be searched and reused by users.

Quantitative performance of KMiPS is also measured and monitored from time to time, in order to establish a systematic and reliable method for emergent problem-solving. 6.2 Improvement of Generalization and Conceptualization Traditional information processing approach suffers in the generalization and conceptualization of the proposed and implemented solutions to the posed problems. This is improved in the KMiPS by inducing the domain experts in tuning the solution for a specific problem. The problem of no optimizing or improvement mechanism in traditional cognitive approach is also improved in the KMiPS by a recursive process of solution discussion, where the questioner “socializes (the term employed by Nonaka)” with responders via a series of discussions in SOS. As a result, the responder can improve his/her solution based on the solution of previous responders; and the questioners can select the best solution before he/she develops the final solution. This is how the improvement or optimization mechanism is established in KMiPS. 6.3 Accumulating Lessons-Learned The problem of “temporary-multi-organization (TMO)” in construction industry is also better tackled by the KMiPS, since the previous problem-solving cases are accumulated with the KMS and modified by the questioners and responders participating in KMiPS problem solving. Such accumulation of lessons-learned does not only provides a source of organization’s intelligence, but also develops the learning capability of an organization to become a learning organization (LO). This is very different from traditional information process approach that relies on machine learning, and also expands the learning scope of traditional individual cognitive problem-solving approach. 7. CONCLUSIONS AND RECOMMENDATIONS 7.1 Conclusions In this paper, a case study is conducted on a leading A/E consulting firm in Taiwan. From the case study, a new approach for emergent problem-solving, tentatively named Kno0wledge Management integrated Problem Solver (KMiPS), which integrates the emergent problem system (namely, SOS system) with the knowledge management system (based on Microsoft SharePoint® platform). It is found that the KMiPS approach combines both cognitive and information processing perspectives of problem-solving. It tackles problems encountered in the traditional approaches and provides desirable functions for emergent problem

solving. Both quantitative and qualitative benefits are assessed with the KMiPS system in the case study. The quantitative benefit analysis concludes 50.88% of time benefit, 63.53% of man-hour benefi, and 84.40% of cost benefit for the KMiPS based on 454 historical emergent problem-solving cases. Other quantitative data shows saving of 8,279 man-hours or a cost saving of USD $431,354. It is thus concluded that the KMiPS approach is very beneficial for the case A/E firm. It can also benefit other organization in construction industry in solving of emergent problems. 7.2 Recommendations It is recommended to apply the KMiPS to other types of organizations in construction industry, such as contractors or EPC firms. Efficiency of problem-solving in different of construction problems needs further research, too. Advanced mode of problem-solving to improve the efficiency of problem-solving is worth of research, too. 8. ACKNOWLEDGEMENT The founding of this research project was partially supported by the National Science Council, Taiwan, under project No. NSC 95-2221-E-216-049. Sincere appreciations are given to the sponsor by the authors. The valuable case study information presented in this paper was provided by CECI, Taipei. The authors would like to express sincere appreciations to the China Engineering Consultants, Inc., Taipei, Taiwan. 9. REFERENCES 1. Runeson G. (1994) The future of building research, Australian Institute of Building Papers, 5, 3-8. 2. Li, H. and Love, P. E. D. (1998) Developing a theory of construction problem solving, Construction Management and Economics, 16, 721-727. 3. Nonaka, I., (1994) A dynamic theory of organizational knowledge creation, Organization Science, 5(1), 14-37. 4. Simon, H.A. (1965) The Shape of Automation, Harper & Row, New York. 5. Newell, A. and Simon, H.A. (1972) Human Problem Solving, Prentice-Hall, Englewood Cliffs, NJ. 6. Lang, J.R., Dittrich, J.E. and White, S.E. (1978) Managerial problem solving models: a review and a proposal, Academy of Management Review, 3(4), 854-66. 7. Cowan, D.A. (1986) Developing a process model of problem recognition,

Academy of Management Review, 11(4), 763-76. 8. Berger, D.E., Pezdek, K. and Banks, W.P. (1986) Applications of Cognitive Psychology. Lawrence Erlbaum, Hillsdale, NJ. 9. Srour, I. M., Haas, C. T., and Morton, D. P. (2006) Linear Programming Approach to Optimize Strategic Investment in the Construction Workforce, Journal of Construction Engineering and Management, ASCE, 132(11), 1158-1166. 10. Liu, L., Burns, S. A., and Chung-Wei Feng, C.W. (1995) Construction Time-Cost Trade-Off Analysis Using LP/IP Hybrid Method, Journal of Construction Engineering and Management, ASCE, 121(4), 446-454. 11. Moselhi, O. and Hassanein, A. (2003) Optimized Scheduling of Linear Projects, Journal of Construction Engineering and Management, ASCE, 129(6), 664-673. 12. Halpin, D. W. (1977) CYCLONE: method for modeling of job site processes, Journal of Construction Division, ASCE, 103(3), 489-499. 13. Bai, Y. and Amirkhanian, S. N. (1994) Knowledge-Based Expert System for Concrete Mix Design, Journal of Construction Engineering and Management, ASCE, 120(2), 357-373. 14. Yang, J. B. and Yau, N. J. (2000) Integrating case-based reasoning and expert system techniques for solving experience-oriented problems, Journal of the Chinese Institute of Engineers, 23(1), 83-95. 15. Chao, L. C., and Skibniewski, M. J. (1994) Estimating construction productivity: neural network-based approach, Journal of Computing in Civil Engineering, ASCE, 8(2), 234-251. 16. Chang, T. C., Ibbs, William, and Crandall, K. C. (1990) Network resource allocation with support of a fuzzy expert system, Journal of Construction Engineering and Management, ASCE, 116(2), 239-260. 17. Chan, W. T., Fwa, T. F., and Tan, C. Y. (1994) Road-maintenance planning using genetic algorithm I: formation, Journal of Transportation Engineering, ASCE, 120(5), 693-709. 18. Yu W. D. and Chang, P. L. (2005) Performance evaluation of the construction knowledge management system—a case study of an A/E consulting firm, Proceedings of ICCEM 2005, Session 4-D, Oct. 16~19, 2005, Seoul, Korea, 1058-1063. 19. Kim, S., Suh, E., and Hwang, H. (2003) Building the knowledge map: an industrial case study, Journal Knowledge Management, 7 (2), 34–45. 20. Mezher, M., Abdul-Malak, M. A. Ghosn, I., and Ajam, M. (2005) Knowledge Management in Mechanical and Industrial Engineering Consulting: A Case Study, Journal Management in Engineering, ASCE, 21(3), 138-147. 21. Johansson, F. (2002) The Medici Effect: Breakthrough Insights at the

Intersection of Ideas, Concepts, and Cultures, Harvard Business School Press, Cambridge, MA, USA. 22. Swaak, J., Lansink, A., Heeren, E., Hendriks, B., Kalff, P., Den Oudsten, J-W., Böhmer, R., Bakker, R., and Verwijs, C. (2000) Measuring knowledge management investments and results: two business cases, paper presented at the 59th AEPF-Tagung, Bremen, Oct. 3rd, 2000, Germany. 23. del-Rey-Chamorro, F. M., Roy, R., van Wegen, B., and Steele, A. (2003) A framework to create key performance indicators for knowledge management solutions, Journal of Knowledge Management, 7(2) 46–62, 2003. 24. Bassion, H. A., Price, A. D. F., and Hassan, T. M. (2005) Building a conceptual framework for measuring business performance in construction: an empirical evaluation, Construction Management and Economics, 23(5), 495-507. 25. Kaplan, R.S. and Norton, D.P. (1992) The balanced scorecard – measures that drive performance, Harvard Business Review, January–February, 71–79. 26. British Quality Foundation (2002) The Model in Practice–Using the EFQM Excellence Model to Deliver Continuous Improvement, The British Quality Foundation, London, UK, 2002. 27. Beatham, S., Anumba, C.J., Thorpe, T., and Hedges, I. (2004) KPIs – a critical appraisal of their use in construction, Benchmarking: An International Journal, 11(1), 93–117, 2004. 28. Yu, W. D., Chang, P.L. and Liu, S.J., “Quantifying Benefits of Knowledge Management System: A Case Study of an Engineering Consulting Firm,” Proceedings of International Symposium on Automation and Robotics in Construction 2006 (ISARC 2006), Session A4—Planning and Management (1), Oct. 3~5, 2006, Tokyo, Japan, 6 pp., 2006.