Cognitive automation reverses the equation of people doing data work with the help of machines to machines doing data work under the guidance of people.
Supply chain organizations have lived with disruptions for decades. Many are localized, one-off events — an unexpected supplier shutdown, transportation failures, a faulty component that derails a product, an earthquake or tsunami, even a cyberattack.
The Coronavirus disease (COVID-19) has proven to be the mother of all supply chain disruptions. The global scope and gravity of the pandemic have exposed weaknesses in legacy supply chain operations, and highlighted an acute need to strengthen resilience and agility.
In the wake of COVID-19, the imperative for resilience is recognized not just by supply chain professionals, but also by C-suite executives, including the CEO, CFO, CIO and chief supply chain officer (CSCO) at companies ranging from Fortune 50 multinationals to mid-sized businesses.
Study: COVID-19 impacted 80% of supply chains
Multiple studies have revealed a newfound focus on resilience to better withstand disruption as companies continue dealing with fallout from COVID-19. For example, a survey of supply chain leaders by Capgemini in late 2020 found that:
- 80% report their supply chains have been negatively impacted by COVID-19
- 62% cite greater supply chain resilience as a top pandemic priority
- 60% plan to increase investments in supply chain digitization
Yet resilience doesn’t happen by simply flipping a switch. Creating resilience requires a top-down rethinking and rearchitecting across the supply chain infrastructure. It also requires accelerating digital transformation initiatives to modernize legacy systems and processes that inhibit resilience and agility.
In working with top organizations around the world, we’ve observed that top strategies for resilience include:
1. Multi-source procurement
Companies are expanding sources of raw materials to minimize risk of being locked in to a primary vendor located in a single country that’s impacted by disruption. Moving from single-source procurement to two, three or four suppliers in multiple geographies helps ensure that production can continue on schedule.
COVID-19 magnified the need for multi-source procurement as many countries went into lockdown mode. Companies that relied on key suppliers located in China, Italy and other countries were upended when factory and transportation shutdowns took effect at certain phases during the pandemic.
2. Increasing safety stock margins
Maintaining an ample supply of critical products would have helped many consumer packaged goods (CPG) companies weather the pandemic, with disinfectant products and toilet paper among the examples. Lacking an adequate inventory buffer, companies weren’t able to replenish goods as market shelves remained bare amid a global buying frenzy of staple goods.
As a result, increasing “safety stock” of critical products without expiration-date issues has become a priority. Companies are now accounting for worst-case scenarios as they generate demand forecasts, schedule production and devise warehousing plans.
3. Near-shoring supply chain operations
Near-shoring operations to increase speed and agility — key characteristics of a resilient supply chain — is another top priority. Organizations are looking to mitigate risks of delay they’ve experienced throughout COVID-19 as a result of far-flung operations.
Near-shoring sourcing and production, and broadening a localized supplier base, reduces lead times and meets consumer demand for swift delivery. Pop-up warehouses are also a top goal to accelerate delivery and also provide space for expanded safety stock.
4. Newfound focus on regionalization
The pandemic has further fueled the shift from centralized global supply chain operations to more regionalized models. Varying degrees of COVID-19 impact in different countries across different time frames helped make clear the advantages of regionalization. So, did stark contrasts in markets and consumers between, say the United States and India.
Regionalized operations help ensure that sourcing, production and distribution can scale up or down based on demands of a particular market, without the time and cost overhead of centralization. Yet companies also need to balance regionalization with the ability to leverage capacity and inventory in one market to support another region in need.
5. Accelerate digital transformation
It’s not the CEO, CIO or CFO who’s been the key driver for digital transformation – it’s COVID-19. Yet the pandemic has also highlighted that supply chain resilience is difficult to achieve with legacy systems and slow, manual processes.
Digital transformation was under way before the pandemic struck, of course, but with mixed results. Traditional approaches such as enterprise resource planning (ERP) and specialized supply chain management systems, cloud-based data lakes and analytic tools can help improve supply chain efficiency. But, they still require a tremendous amount of time-consuming manual data management by supply chain personnel.
In post-pandemic digital transformation, companies are applying principles of production automation to data management. Much like robotic assembly lines have revolutionized manufacturing, the idea is to offload the difficult and imprecise human work of managing supply chain data to machines using cognitive automation technology.
Reversing the human-machine equation
Supply chain resilience is undermined because humans are poorly equipped to deal with the vast volumes of data generated across multiple internal and partner systems. It can take weeks to gather and analyze data from siloed applications to decide how to respond to disruption. And still, many decisions end up being educated guesses.
Cognitive automation takes a different approach with a cognitive data layer, consisting of information captured in thousands of daily Google-like crawls of enterprise applications. That near real-time data is indexed, cleansed, normalized and enriched and ready for making and executing data-driven decisions.
Instead of humans using Excel or rule-based business intelligence tools, analytics is done by powerful cognitive automation algorithms can identify weaknesses, anomalies and opportunities, and make data-driven recommendations on optimal actions that account for time, cost and various other scenarios.
Cognitive automation reverses the equation of people doing data work with the help of machines to machines doing data work under the guidance of people. That’s in effect the same model that revolutionized manufacturing, and it’s already in place at innovative organizations intent on building the resilience and agility needed to withstand disruptions large and small.