Retail in the Age of Disruption (Part One)

Four ways that Big Data, Data Science, and Predictive Analytics must evolve and innovate

As the lockdowns intended to slow the Covid-19 pandemic start to ease, retailers are beginning to look ahead. What might our “after” world look like? How can stores be strategically positioned to compete in a transformed marketplace? Should they be done away with entirely?

There will be some short-term and some permanent changes. It’s hard to imagine returning to crowded aisles anytime soon, but it’s also hard to imagine paying rent on large buffer zones of empty space indefinitely. Both McKinsey and Brookings have already posted some on the future of the retail/distribution/delivery industry. It is clear that the drive to integrate in-store and online sales (“omnichannel”) will accelerate, especially as retailers must adjust to a world where stores may have to be shut for another long stretch or two.

This pandemic will pass, the economy will “reopen” (whatever shape that will take), and people will go back to work – but this is also just beginning of something much larger. We will live through an “Age of Disruption” where the modern systems we take for granted might simply be “switched off.” Retail, dining, and entertainment were “switched off” to enable social distancing, and it will ripple through the economy for some time to come.

In future disruptions, we might see things like electrical power, interstate highways, and clean water supplies be suddenly “switched off” due to cyber-attack, river flooding and sea level rise, or water-borne pathogens, just to name a few. These disruptions may only last a few days (or a few hours), while others may last for weeks and months (like our current situation), but their economic ripple effects will last for years. We will settle into a new “new normal” that usually lasts 8 to 10 years (on average), and then a new disruption will be just as rapid, unpredictable, and economically damaging as what we are going through now.

Recent viral outbreaks (SARS, H1N1, Covid-19) are believed to have come from habitat destruction, which pushes different species closer together, and from increasing human contact with these animals. This is part of a larger picture of what we have been doing with our planet. Climate change is likely to continue driving disruptions, as strong hurricanes, flooding, novel diseases, and fiercer competition over scarcer resources are likely to become increasingly frequent. We will need to rely on the supply chain to keep us fed and hydrated, clothed, and clean. But how do you get ready when you don’t even know what you’re getting ready for?

Here are four ways that Big Data, Data Science, and Predictive Analytics must evolve and innovate to keep up with their clients’ needs:

  1. From the “future state” network model to the “pendulum” network model
  2. From the “efficient” warehouse model to the “agile” warehouse model
  3. From the “portfolio rationalization” store model to the “omnichannel hybrid” store model
  4. From the “recalibration” periodic repeat business model to the “real-time analytics” ongoing subscription business model

From the “future state” network model to the “pendulum” network model

Disruptions will move us in cycles, with long periods of “new normal” punctuated by rapid-onset crises, from open to closed (like stores), from limited to plenty (like toilet paper), from free flowing to choked off (like gasoline). Instead of modeling and comparing potential real estate portfolios, geographic coverage, and transportation costs to find the optimal “one”, analysts will have to find the “average optimum” among several future states. Clients must be equipped with real estate strategies that are effective and efficient both during open and closed, limited and plenty.

From the “efficient” warehouse model to the “agile” warehouse model

Keeping only just enough space in the distribution network and having only just enough product in the supply chain leaves retailers stuck without much flexibility when circumstances suddenly change. Analysts must build multi-site contingency plans that provide clients with plans and procedures to rapidly change up the warehouse system to accommodate sudden demand for products like toilet paper or hand sanitizer.

From the “portfolio rationalization” store model to the “omnichannel hybrid” store model

Brick-and-mortar strategies were suddenly cut off when social distancing closures were ordered. E-commerce delivery strategies were strained by the sudden change to delivery-only. Firms that survive in the ongoing Age of Disruption will have to hybridize their networks, leveraging stores as storage and distribution nodes.

From the “recalibration” periodic repeat business model to the “real-time analytics” ongoing subscription business model

Analysts will need to help their clients stay on top of the daily data influx, constantly checking to make sure the data flow is working. They will need to build predictive algorithms that identify the early signs (such as a sudden run on disinfectant wipes) to activate their contingency plans. Firms will need to move from project-based model to subscription-based model.

Check back here for the next parts in this series, which will cover each of these four needs in more detail.

Continue on to part two here.

Or navigate all Age of Disruption series entries: (1) | (2) | (3) | (4) | (5)

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