The coronavirus (COVID-19) is not only impacting organizations around the world, but is deeply affecting many of us on a personal level--from our families to our peers to the employees whose welfare we have a responsibility for. Fortunately, protective measures are being implemented to safeguard our communities and our workers. Business owners and leaders have to begin thinking ahead as we craft strategic plans to guide our companies through this crisis. Many of us are dusting off playbooks and response plans from previous events. But we believe it is time to completely rethink the model, centered around the ability of supply chains to self-heal and be resilient.
Here is a different model that focuses on capabilities that build everyday agility that can handle crises systematically. It focuses on building systems that have a real-time understanding of the entire operation, that anticipate and advise proactively, and that augment human decision-making seamlessly across the supply chains.
As of March 2020, a look at current events already shows response patterns similar to previous supply chain events like the 2010 Eyjafjallajökull eruption, 2011 Tsunami, 2011 Thailand Floods, 2015 Tianjin Explosion, and numerous smaller events like the 2016 Hanjin Bankruptcy. All were considered extraordinary events managed by supply chains acting in crisis mode. The common thread in all these responses was a heavy reliance on emergency crisis management teams, large group conference calls, and endless spreadsheets and powerpoints to manage critical event decision making. A deeper look at these events yields a set of repeating patterns and exposes an inherent weaknesses in our supply chains:
Systems are almost entirely disassociated with the “questions” humans are trying to find answers for, and built to define pre-programmed answers. The underlying enterprise structure and slow batch-orientation of supply chain systems supporting this have largely remained unchanged since the 1990s despite major advances in cloud architectures and database scalability. This exposes the core major problem -- an orientation toward the wrong goal.
For at least the past 15 years, major IT and supply chain technology projects have been oriented around building for specific goals, KPIs and measurements. Such task-specific efforts are not unlike the mechanical calculators of the computer science eras, from 1800’s Babbage Engines to 1940’s Bombe Calculators. But progressively, we built modern computer systems with generalized processing that could respond and adapt to a wide range of tasks. Instead of building machines for specific tasks, we built machines with dynamic capabilities to which we could apply different questions.
Cognitive automation is the process of digitizing, augmenting and automating enterprise-wide decision-making processes. If we could redesign our supply chain with this model, we would build it as a series of skills and capabilities that could be re-used and constantly re-ordered to perform more general tasks. Very much like how our supply chain professionals learn fundamental scientific, operational, and management skills in the supply chain and reapply them everyday for ever changing problems and scenarios.
Let’s take a moment to make this more tangible using a real-world example: inventory stockout prediction. If you had a professional dedicated to this problem, you would essentially write the job description as:
In this role, we just ‘built’ a supply chain capability that solves for inventory shortages. But we also gained an asset that could do much, much, more if we just asked it different questions. It wasn’t an algorithm for calculating inventory for a specific SKU/Location based on a series of fixed formulas. It was a system designed to answer a question: “If you see an inventory shortage, what should we do about it?”
Here is how cognitive automation builds these capabilities in our example listed above, asking, “Do you foresee any inventory shortages, and, if so, how can we resolve it before it’s a crisis?”
We’ve answered our original question while also building a system that could answer a lot of other interesting questions:
Each one of the questions above could have been a project unto itself in the traditional, purpose-built model. But in a model designed around questions, the same set of skills is applied toward different queries. That is what cognitive automation can mean when we need to turn to our supply chains for help.
COVID-19’s effects are going to affect supply chains for a significant amount of 2020. But the ripple effects of single-sourced material, supplier force majeure and insolvency, and likely uneven demand resumption patterns are likely to last even longer. Let’s get ahead of this one. Let’s out-think this one. And let’s build a model where systems gain cognitive abilities and serve us solutions this time. Then we can not only survive this crisis but ensure we thrive when we emerge from the other side.
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