Companies like Blinkit might be following a tried-and-tested technique of slashing salaries to reduce costs, but their approach is hurting both the economy and workers. In fact, the best approach for all concerned—including the company itself—is a reorientation towards increasing revenue rather than decreasing costs. That is, instead of cutting salaries, get more out of the workers. Innovation will be needed to figure out how to do this without simply working employees harder.
Two weeks ago, this column argued that we need to include labour innovations in our conversations about innovation and R&D. This does not mean we shouldn’t research cutting-edge technology and software. As this week’s column will argue, if we are going to invest in such things, that investment needs to be in labour-oriented applications of technology and software.
Let’s start with a little bit of economics. There exists a metric called the capital-labour ratio. This is, in essence, the amount of capital used in an economy or company per unit of labour employed. In other words, it measures the capital intensity of that economy or company.
Broadly, the endeavour has been to increase capital intensity because it is considered more cost-effective to invest in more capital than in employing labour.
This can work in most countries. But India is not ‘most countries’. Our population is such that we cannot blindly emulate the developed economies and come up with solutions that reduce the amount of labour we employ. You cannot, on the one hand, criticise the government for high unemployment levels while also pushing for innovation that would further push up unemployment.
In India, we need something along the lines of a labour-capital ratio, which measures how much labour is being employed per unit of capital. The idea behind investments in R&D should be to increase employment while at the same time also increasing profits.
Now, in the last installment, this column mentioned Blinkit as an example of innovations in how labour is deployed. The timing of this proved unfortunate, given the troubles the company is having currently. But it is these troubles that actually show how important the targeting of innovation is.
Blinkit’s troubles right now arise from the fact that it has apparently slashed what it will pay delivery partners — from Rs 25 to Rs 15 per delivery. The delivery partners, rightfully so, have gone on strike. Blinkit shouldn’t be looking at only decreasing costs. Instead, a little bit of investment and innovative thinking can help it increase revenues at the same cost.
Also read: India innovates in labour. It doesn’t need the pressure to spend huge on R&D like US, Japan
Ways to deliver
Anecdotal evidence and observation shows that a large proportion of orders placed on these quick delivery apps take place in the morning and the evening. It is very easy for these companies to figure out which periods of the day are the busiest. They already implement surge pricing, but need to figure out how to increase revenues during non-surge periods.
What they have during non-peak hours is an under-utilised fleet of drivers and bikes. Figuring out how to use them productively is where the innovation comes in.
Another problem with this quick delivery model seems to be that orders aren’t clubbed. If somebody in one house orders one banana, and their neighbour separately orders one apple, two riders will be deployed to deliver these products even if the two orders were placed within 10 minutes of each other.
This seems incredibly inefficient, but is also an example of how mis-targetted R&D can lead to less-than-optimum outcomes. The easiest solution here would be to invest in a software that can efficiently track orders, locations, and drivers available in such a way that a single rider can deliver multiple orders. Amazon deliveries are already done this way.
But this wouldn’t be the most efficient as per the labour-capital system that India needs, since it would still reduce the number of delivery drivers needed. Instead, we need innovation in the way we think about the problem. A knee-jerk ‘reduce costs’ approach isn’t the way.
A major inefficiency in delivery models is that the rider must return from the delivery location empty-handed. That is, the return trips earn neither the driver nor the company any money. We need innovation to rectify this problem.
For example, what happens if delivery apps tie up with cab aggregators? On the way back, the delivery driver can drop a person who needs to get to the vicinity of the warehouse–for a fee, of course. There are issues with this idea, of course, such as the fact that many delivery bikes have big containers attached to their backs. But the point is not that this solution is the best. The point is to highlight the problem that we need solutions for.
Innovating in ways that can monetise a delivery driver’s return journey will be win-win for the company, the drivers, and whoever else stands to benefit from the return journey. It also helps the economy because that one driver’s output has been doubled, with the same amount of capital being employed.
Further, something like this could encourage delivery companies to refrain from clubbing orders and reducing the number of drivers on their payroll.
Also read: Indian policymaking is good when cautious, botched when rushed. Learn from US banking crisis
Use AI
This is at the company level. At a larger government policy level, there are an increasing number of ways that R&D in things like artificial intelligence can lead to a more efficient deployment of labour and increase employment overall.
A major problem in India is the huge mismatch between the demand and supply of skills. This seems like a problem almost designed for AI to fix. Concerted investments can be made to develop a dynamic software that can predict to a high degree of accuracy the skills that will be needed five years hence.
At the time when students graduate from school, they can be told what vocations are likely to be in high demand in five years, and so can then make informed choices about what they want to study in college. Along with this, AI can be used to design syllabi that are actually useful, including adding vocational training based on its analysis of what skills are needed.
Again, the exact contours of the solution aren’t as important as identifying the problem. We need to align our teaching to the country’s requirements in a near real-time manner because, at the moment, unemployable students are graduating every year. This is where innovation and investment in R&D is needed.
Rather than recoiling from the idea that sheer capital-focussed innovation isn’t the way forward, Indian policymakers and economic commentators would do well to push themselves beyond the conventional. Perhaps the first level of innovation needed is in their thinking.
The author tweets @SharadRaghavan. Views are personal.
(Edited by Prashant)