Part 2: A Conversation with Biju Kewalram, Chief Digital Officer of Agility GIL
Biju Kewalram, Chief Digital Officer of Agility GIL
Q: What has the crisis taught us about the ability of companies to collect the right data and make smart decisions?
A: There has been little time to develop deep insights – remember that this is only a few months old – it’s more reinforcing things we already knew. The importance of data science — the value and the need to get better at it — have both been reinforced.
Q: What sort of shape would we be in if this had taken place five years ago, when the digital adoption level was not what it is today?
A: We would have probably seen a lot more chaos. In this crisis, we knew very quickly where the shortages of PPE and hygiene products were. You look at information governments have been able to manage and manufacturers have been able to manage. We knew where shortages were very quickly. The response to shortages hasn’t been ideal, but that’s a different issue. … Things we know now within hours would have taken days; and things we know within days would have taken weeks. N95 masks – the authorities knew where the need was within days. What I’m surprised at is that the governments and other organizations didn’t know where the inventory of PPE was, in some cases. In the United States, there was a public employees union that went and found a few million masks sitting in a warehouse after weeks. They did hack work to find it because they were looking for their nurses to have masks. How does that happen in 2020?
At a certain level, in the public sphere, our supply chain digitization and management is woeful. Not even in the same ballpark as the private sector.
Q: How can companies use their data to rebuild risk and financial models?
A: The algorithm is missing two critical inputs. One, how long the threat will be around because of the need to flatten the COVID-19 curve. If you knew how the curve would react, you’d have the input you need. The other missing element is how people are going to behave and react to science. That will alter the way the curve behaves. We don’t have patterns or use cases from the past to qualify and help us. We need more data. There is a temptation to over-rely on previous pandemics — I suspect that is human pattern-seeking behavior. It’s alarming that we see pictures from 1918 that resemble today — people in masks, social distancing. That’s all we’ve learned in a world where I can sign up for tele-medicine and have my phone gather my vital data every minute for transmission to a doctor? We can do much better.
Q: Has the crisis changed digital priorities? Will it change the tech portfolio and investment/spend calculation? What about some of the technologies you’ve championed: blockchain, IoT, RPA, data science?
A: Companies are still driven first by getting their P&L balance sheets quickly adjusted. As a society, we’re still in crisis mode, reacting to the human and cost elements of this. We’re not yet at a point where we’re saying what lessons have we learned for digital.
A lot of people are seeing digital right now as a way to reduce costs. We have not yet started to see digital as a value-add beyond what we saw coming into COVID. COVID hasn’t changed the value-add perception yet, it’s being used as a cost-management exercise and tool first.
When I think about companies, I see a need for them to balance the need to rationalize their costs in a crisis environment with the need to maintain digital readiness and a digital resilience. I think they need to remember that the key is to emphasize a focus on data, rather than on specific technologies.
Q: Will this accelerate supply chain automation? In what areas?
A: All of those areas were receiving adequate attention. Where I see it coming now is in warehouse and other logistics network components with Virtual Reality. … If I want to know what’s in the warehouse and where it’s located, but I’m trying to keep people out of the warehouse, the logical technology to use is a pair of goggles that lets me see how the warehouse is stacked. I’ve got freight, I want to put it into the warehouse, but I don’t physically want to go into the warehouse to find out where it should go.
Warehouse managers and crew could work from home if they had a drone going thru the warehouse and feeding them images on a Zoom call.
Another area for acceleration is RFID. We have struggled as an industry with RFID not being at the price point where it needs to be. But with these masks and other essential supplies now all being RFID-tagged — face shields, gloves, suits, ventilators, etc. – we’ve learned the lesson. You don’t know you’ve got 3 million masks sitting in a warehouse? That’s nonsense.
Q: What was missing, what would have helped? 5G? Better data science?
A: 5G is shaping up to be a game changer, particularly from an IoT perspective. Understand that billions of devices that need to provide ubiquitous visibility need bandwidth and spectrum to communicate. However, the key benefit to 5G is not just bandwidth and capacity (remember that bandwidth equals speed). The key issue is latency – the lag time we have today between a data request and a response. If I am using VR goggles to drive drones in warehouses, I need instant communication between all the devices – the latency has to be low, the response instantaneous. This is a key aspect of 5G that people sometimes miss.
Q: How do you explain prolonged shortages of PPE, toilet paper, hand sanitizer and recurring shortages of meat and protein as a result of cases in the meatpacking industry? How do you explain the disproportionate number of deaths in nursing homes and poor, densely populated areas? Shouldn’t we have had the data to avoid or manage our way through all of that sooner?
A: I don’t see that as a failure of digital supply chains. We couldn’t scale manufacturing fast enough. Manufacturers would have known within seconds we were running out of product. … This is more about manufacturing and scale. Transportation didn’t fail. The underlying supply chain systems didn’t fail. It was our inability to flex manufacturing that did this.
The fact that we couldn’t use data science to predict how quickly and where certain supplies would be needed? That’s a problem. We were asking questions like, where are these masks going? Are they being stolen? Why are we using so many? Those aren’t questions we should have been asking. We should have been able to see the consumption levels of masks in hospitals and elsewhere to tell us. It’s more of a political failure than a digital failure.
Technology doesn’t make itself useful. People make technology useful. Technology doesn’t help people. People help people (and technology).
Q: What else is on your mind?
A: We haven’t adequately addressed the issue of haves and have nots. … As we come back to normal, the companies with the right digital mindset and built-in capabilities like data science, centers of excellence, focused practice areas, will widen the gap with competitors. I’m really concerned about smaller enterprises. It’s no longer about the hardware and bandwidth and tech itself, which is getting cheaper. Instead it’s about mindset capability and adaptability and ability to learn new stuff and be a learning organization.