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Analytics and the new normal

Using augmented intelligence for more informed business decisions

The COVID-19 pandemic is fundamentally changing the way we do business. Though analytics can help manage this period of uncertainty, its value extends far into the future. Analytics teams are at the center of strategic decision-making and play a critical role in helping organizations prepare for the new normal. As social distancing and digital-first business models become the norm, analytics can predict new business scenarios, risks, and customer behavior. Rapid experimentation, artificial intelligence (AI), machine learning (ML), and a business continuity framework can help enterprises effectively navigate disruption.

To prepare for the future, there are four ways to use analytics effectively:

  1. Build analytics-led rapid action and experimentation teams that can act with agility
  2. Develop a governance structure to monitor ongoing changes and support quick decision-making as new facts and trends emerge
  3. Plan for new ways of working and respond to changing business scenarios
  4. Prioritize strategic data investments to enable data-driven decisions

These outline augmented intelligence — in which a blend of machine intelligence and human judgment supports more informed decision-making at speed and scale.

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From crisis management to rapid action

For some businesses, analytics is already advanced. Many have established crisis management teams that are evolving into rapid action teams to revitalize cash flow, liquidity, supply chains, and customer communications. By focusing on rapid risk modeling, solution mobilization, and long-term transformation (figure 1), these rapid action teams are pushing for new and more responsive digital transformation services.

Figure 1. How to use analytics to prepare for a new normal


Rapid risk modeling
Supply chain is one area where COVID-19 creates huge disruption. Certain industries, such as pharmaceutical and retail, are seeing sharp increases in demand, and others are experiencing an unprecedented drop – all of which puts supply chains are under significant stress. Many supply chains will increasingly rely on rapid risk modeling to stay ahead of the competition.

Using risk modeling to build supply chain resilience

In supply chain, analytical risk measurement models can explore the likelihood of suppliers shutting down, spot inventory handling and order fulfillment risks, and track customer behavior patterns. The models also consider demographic profiles to predict risks associated with worker safety, order fulfillment, and customer satisfaction. This helps businesses make more strategic procurement, capacity planning, and worker safety decisions.

AI and ML algorithms for designing multi-tier supplier clusters also give a geo-spatially prioritized view of suppliers' on-time fulfillment capabilities. In this way, they can identify alternative suppliers in regions less impacted by COVID-19 if necessary.

Solution mobilization
While exploring new risk models, businesses also need to mobilize their digital and analytical teams to develop solutions to support other strategic priorities.

Developing solutions to meet demand

As governments begin to slowly lift quarantine measures across the globe, a medical equipment manufacturer developed forecasting solutions to predict volumes for non-COVID and elective treatments. This allows the business to strategically review production capacities and redesign its supply chain network to meet future demand. The manufacturer combined data on the COVID-19 infection rate with industry-specific knowledge to develop this solution.

Long-term transformation
To effectively prepare for the future, enterprises must also embark on longer-term transformation initiatives to make the best use of their data and analytics teams.

Defining long-term digital transformation

A leading retailer undertook a supply chain capacity planning simulation project, backed by a rapid action analytics pod (figure 2), to better manage demand and production capacity. Now, the retailer can optimize supply chain operations, reduce costs, and increase on-time deliveries to protect customer satisfaction.

In insurance, a leading provider used a rapid action analytics pod to develop a visual representation – or digital twin – of the account payables process. Backed by process mining analysis, the insurer uncovered on-time payment opportunities to improve cash flow.

Figure 2. The structure of a multi-skilled analytics pod


Governance and monitoring take center stage
Companies that have advanced analytics teams and solutions are finding ways to build increased risk-awareness into business operations. However, they must also understand what possible recovery curves might look like and plan accordingly.

The challenge is to continuously adapt with little clarity on what the new normal will be. Businesses will need to make real-time decisions to maintain a healthy working capital. They should start by exploring how COVID-19 impacts cash flow (figure 3), especially in manufacturing, consumer packaged goods, and retail.

Figure 3. How COVID-19 is impacting cash flow


Continuous monitoring for financial services

Real-time insights are helping a financial services firm that needs to provide Small Business Administration (SBA) loans as part of the Coronavirus Aid, Relief, and Economic Security (CARES) Act in the US. At one firm, the analytics team was able to monitor the evolving CARES Act while simultaneously verifying and identifying which customers were eligible for SBA loans using AI and ML. This real-time solution allowed the firm to proactively, effectively, and quickly reach out to customers. It also significantly improved working capital by reducing its days sales outstanding (DSO).

The business operations shift

To protect employee productivity while working remotely, many businesses are employing agile principles. Originally created for software development programs, agile principles play a key role in traditional business operations because of the speed and flexibility they bring to process improvement.

Agile principles work especially well in situations wherein businesses need to act and adapt quickly. Sprint cycles that were once managed over weeks now only require days – or even hours, in some cases. For example, while businesses are relying heavily on well-known digital platforms to enable a virtually connected workforce, they're also using more agile technologies like Jira, GitHub, Mural, and Celonis to minimize impact on customer deliveries. But none of this is possible without data, which must underpin every enterprise analytics strategy.

Connected planning to focus employees

An insurer developed a connected planning initiative to cope with a spike in contamination, arson, and theft claims, when their claims team was already spread thin. A claims processing solution, with minimal integration, has improved its planning strategy. Supported by agile principles, the insurer can fast-track straightforward cases to focus resources on more complex cases.

Creating and protecting exceptional customer experiences

A healthcare provider developed customer engagement management solutions to strengthen customer relationships despite reduced physical contact. Enhanced by advanced analytics capabilities to support a customer profiling and segmentation system, the healthcare provider was able to improve its overall customer experience across a variety of channels and respond to customers more quickly.

Now is the time for strategic data investments

Businesses that have made targeted and strategic investments in data are able to respond and adapt more quickly to the challenges presented by COVID-19. Predictive modeling frameworks that build resilience and investment in network security to manage cyberthreats will be essential. Perhaps one of the most noticeable trends is the way enterprise data networks have moved to the cloud to support remote operations. This is especially challenging for businesses that are still relying on siloed data systems.

Ultimately, investment in digital technology is more important than ever. Beginning with data infrastructure investments – to build a solid data foundation – is the key to success. Reducing the time it takes to turn data into insight and then take action will be critical as speed, agility, and data-driven decisions become increasingly important.

Turning data into insights with a rapid data engagement platform

A banking institution was facing data confidence challenges, hampering its business continuity decisions. It made targeted data investments by deploying a rapid data engagement platform team. The team helped the business identify high credit defaults and manage cash flow more quickly and efficiently using an early warning system.

Recommendations for analytics leaders

To prepare for the new normal, businesses need to understand how different scenarios could play out and impact their operations, products, services, suppliers, and buyers in the long term.

Here's where analytics leaders should focus their attention:

  • Prioritize analytics opportunities – aligned to business metrics such as working capital, inventory turns, and cross-sell rates – to develop a roadmap for the next 6-9 months
  • Blend agile principles into analytics strategy, backed by cross-functional teams
  • Create data liberation teams to free up data from transactional systems and accelerate value with AI and ML
  • Develop a library of reusable analytical components for data cleansing, data transformation, visualization templates and model quality checks
  • Create a roadmap for a targeted data foundation to inform decision-making and operations

COVID-19 has shown us that businesses that made the right analytics investments in the past were best placed to manage the current crisis. The time is now to act, build resilience, and strengthen operations as we prepare for the future.

Learn more about how Genpact can help with augmented intelligence.

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