From Local Insights to Global Impact: Unifying Causal Reasoning for Smarter Decision-Making
by IBM Research
MediClean
As the operations manager of MedClean, you have been tasked with reducing the company’s operational costs by 10%. With multiple sites and subsidiaries across the country, you aim to identify key causes of operational inefficiencies as a foundation for implementing significant cost-cutting measures.
Recent advancements in techniques for identifying causal dependencies among variables now enable organizations to leverage recorded data to assess the potential consequences of various interventions under limited conditions—without requiring extensive field testing. However, these techniques cannot be applied when making decisions at a global organizational level or within a region consisting of several locations. In such cases, it is crucial to adopt a flexible approach that allows shifting between an "always causes" perspective and a "sometimes causes" perspective.
For example, consider the sterilization process for medical equipment in a hospital. These processes must be synchronized with operating theatres to prevent bottlenecks that could disrupt scheduled surgeries. While precise timing is essential, ensuring that sterilization equipment operates efficiently—optimizing the use of available resources—is equally critical.
Furthermore, sterilization processes may be federated across multiple hospitals at the national level, with certain steps being centralized for efficiency. This necessitates decision-making at a global organizational level, particularly at the executive tier, where choices regarding machinery maintenance and resource allocation must be made. Such decisions rely on continuous monitoring of all sites running the process. However, maintaining a global perspective requires understanding the intricate causal chains that unfold across different locations, and their blending into a holistic perspective that helps making decision at the organizational level. For instance, in one hospital, an aging machine nearing the end of its lifecycle may cause subsequent machines to produce imperfect results. In another hospital, however, an identical machine may still function optimally without generating such issues. Can a global policy be implemented for determining the appropriate incoming rate of materials or maintenance frequency without distinguishing between these differing causal conditions?
To address such complexities, IBM Research in the scope of the AUTO-TWIN project has recently enhanced its previously developed causal model to support the concept of “causal unification.” This approach enables organizations to generate causal maps—visual representations of the various causal relationships between organizational operations. These maps provide operators and decision-makers with a comprehensive and accurate view of causal conditions across different sites, facilitating more informed decision-making at the global level. Additionally, causal unification allows operators to differentiate between event occurrences that are consistently linked causally and those where dependencies may vary over time or between different locations.
By leveraging these advancements, organizations like MedClean can enhance operational efficiency, oversee their resource allocation, and implement global data-driven policies that adapt to the unique conditions of each site—ultimately achieving the targeted cost reduction without compromising performance.
If you wish to learn more about the work on causal business processes, click here
