Decision-making in Asset Life Cycle Management (ALCM)

In the course of my master’s thesis I conducted research in the field of Asset Life Cycle Management (ALCM). Over a period of six months I designed and tested an asset decision-making tool at a case company. The applied methodology for the design of the tool is the design science methodology (Hevner, March, Park, & Ram, 2004).

A few of the most relevant challenges to overcome at the company were the following:

• A strong silo mentality in the organization
• No alignment between stakeholders in the decision-making process
• No reliable data across multiple disciplines

The designed solution builds on the lifetime impact identification analysis (LIIA) described by Ruitenburg, Braaksma, & van Dongen (2014), which objective is to identify “trends or events that might have a positive or negative effect on the remaining lifetime of the asset.” As part of the analysis a brainstorming session to identify impacts from all five TECCO perspectives (technical, economical, commercial, compliance and organizational) was organized. This approach is especially useful in a multidisciplinary environment where data across disciplines is hardly standardized for comparison. Moreover, the interaction between multidisciplinary experts  during the session facilitates the use of tacit knowledge. Important tacit knowledge in this case is experience and intuition. This can influence a decision in a particular way. After the identification of lifetime impacts their importance was determined using prioritization. Multi criteria decision analysis (MCDA) is a widely accepted technique that evaluates multiple conflicting criteria in decision-making in order to prioritize (Figueira, Greco, Ehrogott, & Ehrgott, 2005). Especially in a multidisciplinary context this is important. Criteria for the tool were established that aligned to the organization and interests of stakeholders, borrowed from the lifetime impact centered asset management prioritization part (R. Ruitenburg, 2017) and the balanced scorecard (BSC) perspectives (Kalender & Vayvay, 2016). In my understanding, the balanced scorecard perspectives in particular show how certain strategies affect the core area of the business, that is the financial performance, the customer satisfaction, the internal (production) processes, the organizational capabilities and compliance. In a second workshop the experts performed Analytical Hierarchy Process (AHP) pairwise comparison on the BSC perspectives to establish weighing factors and scored the identified lifetime impacts on all criteria. AHP is a powerful prioritization tool that can deal with subjectivity of expert knowledge (Saaty, 2003). Due to the discussion between experts in the session, consensus of scores was reached to a satisfactory level. Resulting from this a ranking of lifetime impacts and a visualization of the scores of lifetime impacts was shared with the experts. Feedback was asked in order to verify the results with the experts. After the identification and prioritization of lifetime impacts timely measures can be initiated.

Even though the tool was originally designed for application in ALCM to prepare assets for future challenges and opportunities, a similar framework seems to be applicable for system integration. First of all, the characteristics for asset life cycle management and system integration are similar. The interdisciplinary of system integration and the organization of technical and non-technical aspects in system integration strategies (INCOSE, 2015) shows that information from multiple disciplines (technical and managerial) will be exploited for the decision-making of system questions. Indicating that also in system integration information from multidisciplinary perspectives have to be combined for decision-making. In more detail, system integration is the process  that “synthesize[s] a set of system elements into a realized system that satisfies system requirements, architecture, and design” (ISO 15288 Committee, 2015, p.68). It can be understood in the way that silos of system elements are integrated and aligned as part of system integration. If stakeholders consensus on integration strategy and constraints is reached early on in the integration process and integration concerns are shared between multidisciplinary experts on a regular basis, it is more likely that integration challenges are identified and accounted for in time.

Summarizing, stakeholders from multiple disciplines are needed for decision-making in system integration. They are used to operate in silos rather than in a multidisciplinary approach. Moreover, the stakeholders focusing on different system elements have conflicting priorities on which decisions are based, depending on their needs, expectations and constraints. Especially stakeholder alignment is essential to the success of the system-of-interest integration. A possibility is to perform expert sessions to manage multidisciplinary information, so that tacit knowledge can be used and stakeholder consensus can be reached. Besides, prioritization of decision alternatives by means of ranking based on system requirements could be one way of aligning multiple stakeholders in system integration for decision-making.

References

Figueira, J., Greco, S., Ehrogott, M., & Ehrgott, M. (2005). Multiple Criteria Decision Analysis: State of the Art Surveys (Vol. 78). https://doi.org/10.1007/b100605

Hevner, March, Park, & Ram. (2004). Design Science in Information Systems Research. MIS Quarterly, 28(1), 75. https://doi.org/10.2307/25148625

INCOSE. (2015). System Engineering Handbook – A guide for system life cycle processes and activities. (D. D. Walden, G. J. Roedler, K. J. Forsberg, R. D. Hamelin, & T. M. Shortell, Eds.) (4th ed.). San Diego: Wiley.

ISO 15288 Committee. (2015). NEN-ISO/IEC/IEEE 15288.

Kalender, Z. T., & Vayvay, Ö. (2016). The Fifth Pillar of the Balanced Scorecard: Sustainability. Procedia – Social and Behavioral Sciences, 235, 76–83. https://doi.org/10.1016/J.SBSPRO.2016.11.027

Ruitenburg, R. (2017). Manoeuvring physical assets into the future.

Ruitenburg, R. J. (Richard), Braaksma, A. J. J., & van Dongen, L. A. M. (2014). A Multidisciplinary, Expert-based Approach for the Identification of Lifetime Impacts in Asset Life Cycle Management. Procedia CIRP, 22, 204–212. https://doi.org/10.1016/J.PROCIR.2014.07.007

Saaty, T. L. (2003). Decision-making with the AHP: Why is the principal eigenvector necessary. European Journal of Operational Research, 145(1), 85–91. https://doi.org/10.1016/S0377-2217(02)00227-8