No amount of analytics, forecasting and planning could have prepared businesses for the onset of the global pandemic. Thousands of businesses are adjusting to a new normal and changing consumer behavior, striving to ensure continuity amid declining or surging transactions and increasing requests for customer service, while experiencing enormous disruptions across their value chain.
A Gartner survey conducted in March showed that only 12% of businesses believed they were highly prepared to handle the challenges that the pandemic presented. For the rest, maintaining continuity and managing through change have become an existential challenge.
One rapid route to increasing business resiliency now and into the future is the smart orchestration of intelligent process automation (IPA), which combines robotic process automation with machine learning and cognitive technologies to create intelligent operations. With IPA, companies can improve end-to-end process efficiency by addressing increased transaction volume and process chokepoints, and can streamline tasks and reduce or eliminate human error, and lower costs.
Orchestration of intelligent automation tools could be an effective answer
No automation technology can address all the business impacts of COVID-19. However, smart orchestration of intelligent automation tools can effectively address many operational challenges.
Not to be confused with a simple IT implementation or routine automation, smart orchestration consists of two elements: using process mining to identify bottlenecks or overflows in operational processes and having action engines implement the most effective combination of tools and technologies to solve them.
Existing ERP systems are valuable sources of information about daily processes and transactions across various touchpoints. These “digital footprints” can include transaction descriptions, time-stamps and transaction IDs. Intelligent process mining and process discovery tools use such information to visualize current and actual data flows in processes, such as areas of cashflow leakage or vendor inefficiencies that could delay incoming cash.
Using algorithmic process discovery, analysis and validation can improve processes by addressing bottlenecks and non-value-added steps. Robotic process automation; text, image and speech analytics; new workflows and smart data ingestion then improve process design and actions, while allowing organizations to implement solutions at scale. Managing disruptions — from predicting and identifying them to implementing process design changes to address them — can be intelligently orchestrated.
Predicting supply chain disruptions
We have seen this two-step process have measurable operational impact during the pandemic. One leading U.S.-based pharmaceuticals company we work with used process mining and machine learning to predict supply disruptions, automating alerts for inventory management and monitoring its supply chain to identify vendors with potentially restricted shipments. It identified countries with higher risk, then implemented automatic alerts for open purchase orders based on that risk, allowing the company’s purchasing function to continuously predict cycle times and flag delivery time risks, helping it with production planning.
Identifying at-risk vendors
More recently, a major pharmaceuticals company deployed process mining to identify vendors at risk as its supply chains in major economies were disrupted. The company was able to quickly measure potential impacts and address them by identifying alternative vendors and materials. This minimized operational risk, saved money, and helped ensure that the company could continue to supply vitally important products to governments and healthcare workers.
Implementing algorithms to find automation opportunities
Other examples of how intelligent automation became an important initiative during the crisis include companies with processes facing higher-than-normal transaction volumes. Travel restrictions in particular created a flood of requests from people to modify or cancel reservations, not only affecting airlines and other transport companies but also companies providing travel insurance. Companies we worked with were able to implement algorithms to identify process bottlenecks and failures in real time, enabling them to find automation opportunities.
Notably, these types of process automation do not require new technology. They rely on intelligent orchestration of well-established tools, such as robotic agents that work alongside and interact with employees. These types of robotic agents mimic human actions and can include chatbots relying on natural language processing (NLP) and artificial intelligence (AI) to interact with customers online, or action engines that perform tasks under specific business conditions based on background logic structures:
