Managing BPM life cycle transition risks in a small educational company to support change management

Abstract

PurposeThis article aims to present a methodology applied to the transition between the “as-is” and “to-be” stages of the Business Process Management (BPM) life cycle, supporting its implementation and maintenance for the organizational stability, using techniques from Operations Research and Information and Decision Theories, applied by a gamified system.Design/methodology/approachThe study used Design Science Research, considering the following methodological elements: (1) artifact model, after initial analysis of the organization; (2) problem relevance, incorporating components to the Markov transition matrix and the integer programming model for resource optimization; (3) model evaluation, establishing mechanisms to validate the methodology created; (4) research contributions, showing benefits found; (5) systematic approach, detailing methods used; (6) model's research process, revealing the means for execution; and (7) final presentation of results.FindingsAfter planning three scenarios for the company, containing zero, one or two implemented processes, the matrix of states in the Markov chain effectively identified the states of greater and lesser transition uncertainty. At the same time, the optimization model guided the organization toward a stable change in its operational and financial areas.Practical implicationsThe company's planning capacity has increased, as its managers now have a methodology to promote rational decisions about the development of plans. Before, managers believed that the methodology used was only for large companies. However, this view changed with the results, showing a structured view of the ability to absorb new customers, relocate established ones, increase the comfort level for employees and increase profitability for the company's business.Originality/valueThe study showed that the combination of techniques opens a new perspective to the incorporation of BPM in organizations, allows a smooth change between the current and future state, making it possible to predict the evolution of transition scenarios.