KNOVA: modelling the knowledge value-added factors that influence business process performance in organisations

Business process models are designed in isolation but must be applied in real organisational and business situations. A key management concern is how to identify those levers of control that injluence the factors that allow a business process to achieve maximum performance. In this paper, we demonstrate the application of the KNOVA (KNOwledge Value-Added) technique to the problem of making visible the hitherto intangible people, culture and knowledge factors that can easily influence the success or failure of a business process. We illustrate its application to the processes of a diagnostic team within a department of a large hospital. Two bene$ts ensue: a) the articulation of a common understanding of the factors that affect team performance and, b) the pin-pointing of resource investment to maintain performance and reduce risk or cost. 1: Introduction where business process engineering fails Business process modell ing (BPM) and re-engineering (BPR) are currently the subject of major commericial and academic attention. Many different methods are appearing, each one shedding a different light on the business problem. Some favor an ‘accounting’ emphasis on flowcharting and costs [1], [3], [7], [lo], [11], [19], [20], [23]. Others stem from an information technology systems pedigree [4], [15], [16], [17], [27] and focus on data and control whereas others look to apply a more organisational theory oriented approach [21], [22]. Each approach has its merits but all tend to have in common the fact that the resulting models typically are delivered as ‘stand-alone’ artifacts and are seen in isolation from the environment in which they are to be employed. One driver for the rise in BPR techniques has been the desire to cut out unecessary organisational beaurocracy and levels of management. In the classical management model, both these roles are concerned with manipulating information for the benefit of the directorial and operational business units. With the availability of information and communication technology, the temporal and spatial foundations of business are changing and these traditional roles are also changing, or disappearing altogether. For example, workers in a company can now be geographically far apart and send (electronic) documents in real-time; the associated removal of administrative layers means that core profit-making business processes and their effectiveness are being forced into a sharper focus. Given the pressure to reduce costs and increase performance, there is an interest in making new business processes maximally efficient. Although much effort is expended on developing elegant and ingenious BPM techniques, it is often overlooked that factors concerning the process environment (e.g. culture, shared knowledge) and the agents that support the processes (i.e. people and their level of skill and knowledge) can have a big influence on their success and acceptance. This paper is linked to work done by Glykas, Holden and Wilhelmij [8] using the ARMA (Agent Relationship Modell ing Analysis) BPM technique for Safety-Critical situations in the petrochemical industry. ARMA provides a structured way of modell ing the roles, responsibilities of people and other agents along with the information and knowledge resources that supported decision making. Further experience with ARMA confirmed its value as a way of formally validating business process (to find potentially unsafe situations), managing information flows and as a way of developing a commonly agreed understanding of collaboration amongst a team. However, it became clear that in such environments, two intangible and pervasive factors are found to have a major effect on risk and performance. These are knowledge and culture. For example, safety on oil rigs [12] is an area where people and environmental factors are acknowledged to influence safety in a profound way [ 141. Operational performance and risk managment are what team-leaders are frequently concerned with. ‘Risk’ may refer to the risk to financial success, human safety or simply the uncertainty of future outcomes. Certain types of business activities lend themselves well to orthodox 891 1060-3426/95$4.0001995 IEEE Proceedings of the 28th Hawaii International Conference on System Sciences (HICSS '95) 1060-3425/95 $10.00 © 1995 IEEE Proceedings of the 28th Annual Hawaii International Conference on System Sciences 1995 BPM. Manufacturing, administrative and accounting procedures are examples. These have clear steps and performance is easily measured or uncontentious (e.g. units of output / hour). In other areas, ‘soft’ human factors, culture or the ability to apply knowledge are of prime importance. The problem is that of articulating the link between the ‘hard’ factors of investment, performance and benefit that can be measured in tangible (e.g. financial) terms with the ‘soft’ and more pervasive, intangible factors relating to knowledge, people and culture. And, even more importantly, where are the fevers @control and where do we target our investment to effect a positive change in our team’s performance’? When making knowledge investment decisions, a clear understanding is required of the knowledge factors and related influences which affect performance and the bottom line cost benefit. The ability to track knowledge investment and to relate this to ongoing performance and benefits is also required. However, traditional management approaches provide limited support. In particular there is no suitable framework which integrates the different influencing factors, and presents them in a form suitable for use by decision makers. This paper describes the KNOVA (KNowledge ValueAdded) technique and how it was applied to elucidate a model of a complex medical diagnostic / patient care process existing in the department of a large hospital. Firstly, the underpinning philosophy and objectives of KNOVA are outlined. Then the hospital situation that is the subject of the case-study is presented along with an overview of the contemporary change-related and other factors that are concerning managers. Next, the process by which KNOVA is applied is shown in the context of the case-study. This include’sadescription of how a computerbased modell ing aid used to shed light on the dynamic aspects of the organisational and team situation. Finally, we conclude by discussing some of the implications of our approach. KNOVA has been developed from an in-depth assessment of practical knowledge requirements facing management in the oil and gas exploration industry by British Petroleum (BP), a major international oil company. KNOVA was used by BP to understand how knowledge affects business performance, and how to improve best practice regarding knowledge investment and management. Applications of KNOVA have included a post-project appraisal of the drill ing-team performance improvement campaign [25], and a comparison of the culture of drilling teams in Alaska, Gulf of Mexico and the North Sea [24]. These applications and other industrial work have served to ensure that KNOVA addresses practical, rather than theoretical, management needs. 2: What is KNOVA? KNOVA is a tool which allows managers to improve their understanding of the factors influencing the performance and direction of a working group or team. This improved understanding increases confidence in decision making and overall group operations. Specifically, KNOVA is particularly useful in identifying performance measures and areas for investment (such as training, technology or resources) to improve weak points or change the style of work. Critical managers might say “but I already understand my team’s situation”. KNOVA does not negate existing skills but allows them to be applied more quickly and with less mental effort. This is useful when operating pressures mean there is not time to give as full consideration to all factors as would be preferred. The foundation of KNOVA is expressed by the condensed influence diagram shown in Figure 1. This shows the relationship between the various main factors affecting team performance. The arrows show ilnfluencing relationships between factors. By progressively expanding this model using the techniques to be described, a detailed representation of the activity under examination is produced.