Leveraging BCM automation to enhance organizational resilience
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Resilience capabilities remain as a claim till proven in real time, but there are methods to qualify the claim by demonstrating strong immunity against resilience risks, similar to how the anti-bodies in humans work!
Business continuity process automation focusses a lot on the collecting, processing and analyzing the data of interest to the business continuity domain. The data gathered in business impact analysis, risk assessment and other modules are processed further to understand and manage recovery objectives, BC strategies, continuity plans and incidents. However, a lot of the data that are gathered and processes during BCM automation are quite relevant to organizational resilience. In this article, we are delighted to illustrate how a few design concepts as well as application of artificial intelligence and machine learning can be used in the resilience space.
Most of the commonly available resilience framework deals with identifying and managing resilience across components like process (financial, human resources, ICT, supply chain), operational risk (continuity, cyber, crisis, incidents) in addition to compliance and environmental factors. Fundamental about resilience is to know about what is at risk, and then design and implement a response plan that can with stand threats and respond swiftly. In that sense, a resilient system should defend against potential threat by identifying and responding to systemic vulnerabilities. The good news is that BCM automation captures a lot of process and risk information of importance to resilience. In the rest of the article, we are delighted to share few ideas to enhance resilience by leveraging the BCM automation data. This is not the entire list, but the idea is to instill the necessary design thinking so that the professionals responsible for BCM automation can build on these in their own context.
BIA lateral view: One of the resilience expectations around HR practice is to identify and create shadow skills, succession planning for all critical processes. A well-designed BIA not only identifies the critical processes, but establishes interrelationship of people, location, infrastructure and vendor associated with the processes. Since the data is in a relational database, we can draw more inferences like Locations having more critical resources, the set of applications that are most used by resources in a given location (leads to training more resources on the application) and this list go on. So, a good BIA design should capture all the continuity resources as defined in GPG and ISO. In our experience, a number of BCM automation just focuses on limited data set, like IT sector focuses just on resources, supply chain focuses on warehouse infrastructure.
Simulating the weakest link: Just continuing from the previous illustration, BIA establishes resource – resource relation through the process association. If one were to tweak this relational data, it is not hard to establish a specific vulnerability, say, Process A in Location B is performed by set of people who are backed up, but the same process in another geography is vulnerable as shadow skills are not available. This is a possible gap if that geography. The above data is not just related to recovery, but about business as usual. Knowing the weakest link can provide significant insights to resilience focus.
Supply chain resilience: We used to advise a tier 1 automobile brand company and to our surprise, the company invested in ISO systems for its core suppliers and few of suppliers’ supplier. Taking cue, the nucleus firm in BCM automation should consider extending the BCM application to their core vendors. The how is a matter of detail. But extending the BCM automation to supply chain can help resonate together in a synchronized response to an incident.
Let us apply AI and ML concept: Let us say a leave application is received from an employee for a period of 2 weeks. Based on existing relational data, the AI engine should be able to skim the BIA to highlight what could be at risk from continuity perspective, and send notifications if the shadow risk is not yet mitigated. Essentially, AI can intelligently connect data from multiple systems that are humanly impractical, while ML can find patterns of such on number of combinations and aid a resilience decision.
Balancing the lag and lead indicators: Some of the BC performance indicators can provide significant resilience leads. For instance, test index at a business department looks good, but a specific location within the department has a significant lag. In addition, the combination of resources and their performance can help narrow down on a specific vulnerability and design a proactive initiative. Such data is readily available from BIA relation.
Giving back, responsibly: Resilience at an enterprise level covers the entire supply chain as well as enabling function. All along, BCM automation has been sourcing information from multiple systems through API, now, it is about time for us to give back a number of insights and information to other enterprise systems. This could help the respective business units to implement resilience initiatives.
The list could go on, we just wanted to share few illustrations to leverage BCM data to enhance organizational resilience. It is about invest today for a better tomorrow. The above design principles are best addressed in the early stages of BCM automation, else could lead to substantial effort in retrospective data collection.
About the author
Ilango Vasudevan, FBCI is a funder of Sara Analytics, a company that focuses on BCM automation and BCM simulation.
About the author
Founder & Director