Mathematical programming and optimization theory have a lot of great nuggets for implementing better business practices. In fact, much of optimization theory is about optimizing decision making, hence the name. The minimax problem is a specialty within optimization theory – minimize the maximum value for an outcome. It’s a risk management measure.
During any transformational program, there will be incidents that have to be managed, and the traditional change management process won’t quite work. The most notable of these are high-risk areas that need deep technical knowledge. When invoking the Engagement vs Empowerment heuristic, minimax problems are identified as highly technical problems that are also high-risk.
When defined in the terms of the heuristic, Minimax Problems score above 10.5 points from questions 1-4, but are also deemed high-risk, thus having an overall score of 0. These challenges demand an agile, informal structure, where senior change leaders and local experts join forces to swiftly craft high-risk solutions on a tight timeline. In this article, we explore the unique nature of these minimax change problems and the strategies for addressing them effectively.
Minimax change problems are characterized by four key attributes:
High Risk: These challenges carry a substantial risk of failure, often due to their technical complexity or the urgency of the timeline.
Technical Depth: They typically require a high level of technical expertise to address effectively, making them unsuitable for standard, off-the-shelf solutions.
Tactical Nature: Minimax problems are tactical in nature, impacting specific teams or processes rather than the organization as a whole.
Short Timeline: The urgency of minimax problems necessitates swift action, leaving little time for protracted decision-making or extensive planning.
Addressing minimax change challenges requires a unique approach that blends the expertise of senior change leaders and local-level experts. Here's how to tackle them:
Senior Leadership Oversight: Begin by assembling a cross-functional team of senior change leaders who can provide strategic oversight. These leaders should be well-versed in the organization's overall objectives and capable of making swift decisions.
Local-Level Experts: Identify local-level experts who possess the technical knowledge required to navigate the problem effectively. These experts may be subject matter specialists or seasoned practitioners familiar with the specific context.
Agile Collaboration: Foster an environment of agile collaboration. Encourage open communication and information sharing between senior leaders and local experts. This ensures that insights from both ends are considered in crafting solutions.
Rapid Problem-Solving: Minimax problems leave no room for lengthy deliberation. The team must prioritize rapid problem-solving, focusing on high-impact actions that can be implemented quickly.
Risk Acknowledgment: Acknowledge the inherent risk involved. Embrace the possibility of failure while striving for success. In some cases, the best course of action may involve experimentation and adaptation.
Imagine a scenario in the realm of car insurance where a traditional risk adjudication model has been the bedrock of underwriting decisions for years. This model relies on pulling policy holder data and claims from several different systems. However, the company is now in the process of implementing a state-of-the-art tech stack that promises to revolutionize the company by automating processes, improving accuracy, and enhancing customer service. This transition, while promising, brings about significant challenges, as the existing risk adjudication model is not compatible with the new tech stack. To further complicate matters, the incompatible nature of the model wasn’t learned about until late in the process. This is a process that needs to be handled by empowerment, but the high degree of risk means that the SLT needs to have direct oversight of the effort. A minimax change solution is needed.
Senior Leadership Oversight: A segment of the senior leadership team, including the Chief Underwriting Officer, Chief Data Officer, and Chief Technology Officer, serves as the decision-making team. They provide strategic oversight and define the limits of acceptable deviation from the existing risk adjudication model, guiding the adaptation process.
Local-Level Experts as Risk Assessors: Local-level experts, such as experienced underwriters and data analysts, act as core team members. They contribute specialized knowledge to identify potential pitfalls and incompatibilities between the traditional model and the new tech stack.
Agile Collaboration as Adaptation Process: Agile collaboration becomes the adaptation process. Daily meetings and iterative assessments help refine strategies to modify the risk adjudication model while maintaining consistency and fairness in underwriting decisions.
Rapid Adaptation as the Iteration: Swift adaptations to the traditional risk adjudication model represent iterative refinements, moving toward alignment with the capabilities of the new tech stack.
Risk Acknowledgment as the Constraints: The organization's risk tolerance and commitment to maintaining fairness in underwriting decisions become the constraints, ensuring that the transition does not compromise the integrity of the policy adjudication process.
By applying a risk adjudication model to this scenario, the insurance company can effectively navigate the challenges of adopting a new tech stack while preserving the fundamental principles of risk assessment and fairness in underwriting decisions.
Minimax change problems represent a unique and high-stakes challenge in the realm of change management. To tackle these issues effectively, a dynamic approach is essential. Senior change leaders and local experts must collaborate in an informal, agile structure, embracing the risk and uncertainty that accompanies high-risk, high-reward solutions. In doing so, organizations can navigate minimax change challenges successfully, emerging stronger and more resilient in the face of adversity.
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