Problem Framing and choice of adjectives – story of ventilators and sanitizers

Problem Framing is considered a crucial step in the development of a new solution/product or in problem solving.

Yet, we miss spending enough quality time on this step, many a times.

Disclaimer: I will be over-simplifying a few aspects, to drive home the key point so please humor me.


Since early March, as the Corona virus spread to Europe and other countries in Asia/Americas, there was a lot of noise around two things.

Or rather lot of noise around the shortage of two things – hand sanitizers and ventilators.

In India, sanitizers were completely sold-out, thanks to panic buying and stocking up.

The demand for ventilators was projected basis the statistics in Italy and it was obvious that we did not have enough ventilators for the population. Even if one assumed that 1-2% of the infected population would need ventilators, there weren’t enough ventilators.

Not just India, it was the same case in UK, USA and most other countries.

If you read the news, or noticed your Linkedin or Twitter feed, it seemed the problem statement was:

We need more hand sanitizers and ventilators,  fast !

Within a few weeks, there were many solutions popping up.

Hand sanitizers made at home, being made by FMCG companies etc.

For ventilators, multiple paths were being proposed and explored:

Decathlon's 3D printed Scuba mask ventilator
Decathlon’s 3D printed Scuba mask ventilator

I was inspired by the determination by so many, to find a solution to the ventilator scarcity challenge. It was a phase, when I would spend almost 20-30 minutes looking at the new solutions and being amazed by the interesting paths taken by these innovators and tinkerers.

And then I read a Linkedin post that really shook me. (sorry – missed taking a screenshot )

It was from a doc, who asked a simple question to the ventilator innovators – he asked if the innovators would be willing to bet their own lives on an untested ventilator. He went on to say, the problem is not that we need a new design for ventilators, we need a higher supply of reliable/tested ventilators. Ventilators that medical teams are trained to use.

The problem framing for ventilators really was more like:

We need a higher supply of reliable ventilators. Fast.

Reading that post, it was clear that the challenge wasn’t so much about a new, easy-to-manufacture design but more about ensuring a faster supply of reliable ventilators. Reliability was non-negotiable. And most of us missed this in the early phase.

Many who co-created their solutions with medical practitioners, ensured that reliability wasn’t compromised.

This article, talks about it in the context of the UK:

Here, in the U.K., the government rather botched the whole ventilator thing quite royally. Instead of doing what industry leaders had suggested to the government, which was to repurpose factories to make ventilators using existing plans from ventilator manufacturers under license, the U.K. government took a different approach.

Boris Johnson hosted a call with 60 captains of industry from the U.K. — household names like JCB, Rolls-Royce, Airbus, Honda — and the focus was on making something from scratch from the bottom up. As has so often been the case with this virus, military metaphors were drawn. It would be a crowning moment in British manufacturing, akin to the Spitfire during the Second World War.

Medical advisors said, “Okay, but we don’t need those basic bag-and-a-bottle pumpjack machines. We need the product to be fit for purpose.” But it soon became apparent that what was being proposed was woefully under-equipped to meet what was needed. Instead of listening to experts, British politicians had felt that they understood the problem better and tasked people with a proven track record in an entirely unrelated field, the job of solving the problem.

The sanitizers on the other hand, didn’t have such bottlenecks. You could make your own at home too.

The key lesson I learnt observing this unfold, was to pay very close attention to adjectives in the problem framing stage.

Key adjectives for the desired solution were slightly different for ventilators vs sanitizers, yet making a world of difference:

  • ventilators : reliable (non-negotiable), low-cost, high-capacity
  • sanitizers : low-cost, high-capacity

So, how do we ensure we have an accurate framing of the problem? Some ideas include:

  • Get the key stakeholders (e.g. medical practitioners in the case of ventilators) involved in the early stages of problem-solving and solution-design. Co-create if possible. They will catch the missing adjectives
  • Define the conditions of satisfaction – again vetted by the user/stakeholder and not just the interpretation of the innovator
  • “Watch” the existing solutions in action. It is one thing to read about what a ventilator is and another to witness it in action in an ICU. One would know the stakes involved.
  • Slow down. As an engineer, I have witnessed the inherent nature of my mind to jump into the solution mode. This adrenaline surge that comes from building something new, or solving a problem, takes us away from spending enough time asking ” do we really know what the problem is

Varying rates of digital adoption across send and receive-sides impact payment-flows

Very simplistically put, payment is the movement of money from A to B.

And the world is becoming increasingly comfortable with money flow going digital. Whether its a consumer paying another consumer (P2P) or consumer paying a merchant (P2M) or business paying its vendors/suppliers (B2B) – the levels of digitization of these use-cases is very impressive.

But, what happens when the speed of digital adoption is very different at point A vs point B.

What does that mean for the digitization opportunities, challenges and product related nuances for the send and the receive side.

Let’s take domestic remittances as an example. In most of S Asia be it India, Bangladesh, Nepal – many blue collared workers send their monthly wages/salaries back home to their families/dependents.

These blue-collared workers are typically operating in urban or semi-urban areas and hence are exposed to an inherently more digitally savvy ecosystem. These workers have access to affordable smartphones, low cost reliable data-connectivity and also have multiple opportunities for assisted-on-boarding for any digital solution. Think of 4-5 workers staying in a house and one young digitally savvy showing others how to use the smartphone for voice or video chatting.

Their families (the receive side of this payment flow) on the other hand, may not have a similar eco-system. The data connectivity may be poor, opportunities for learning from others may not exist etc etc.

It might be safe to assume, that the digital adoption by the send side is expected to be much faster than the receive side.

And if that really happens, what does it mean?

Historically, domestic remittances was an agent assisted business. Remittance providers had extensive agent networks for cash-in and cash-out. Because the old flow has been :

  1. Worker gets the salary (most probably in cash)
  2. Goes to the remittance point (an agent outlet)
  3. Shares details of the recipient, validates himself and pays the amount (in cash)
  4. The recipient gets notified (usually on SMS) and
  5. Goes to a nearest cash-out-point for withdrawing cash. Or if it was a transfer to her bank account, would go to an ATM/branch for cash withdrawal.

Let’s look at what all is changing:

  • Many workers will start getting salaries into accounts/wallets/prepaid cards
  • Workers are digitally savvy now, comfortable doing transactions on mobile.
  • Some may start using their mobile wallets, cards for merchant payments – because in their locations (urban mostly) there are merchants accepting digital payments.
  • Many will not want to stand in line or physically visit an outlet to send money.

And this will mean that many mobile-originated un-assisted remittance origination services will flourish. All vying for this big base of consumers who are just becoming digitally savvy, just becoming comfortable enough to send their hard earned money digitally.

On the other hand, the receive side looks much the same as older times:

  • Even if the family in the village gets money digitally, they cannot spend it digitally.
  • ATMs may not be efficient for banks, so local shopkeepers are best way to withdraw cash.
  • And since this shopkeeper has been the usual cash-out point, one may see little value in changing how the remitted money comes in

Again, to make things really simple, lets assume that there are two distinct profiles on either side.

  • Send side – 1. Cash-first, feature phone user and 2. Digital first smart phone user
  • Receive side – A. Cash heavy spender and B. Digital spender.

And let’s assume that digital adoption is process of migration from the first profile to latter of a large enough pool of consumers. This would give us the usual 2X2 matrix. Here’s a quick visual model of what all this means –

Varying pace of digital adoption in the remittance use-case
Domestic Remittance : Varying pace of digital adoption

While this may be an oversimplified assessment, the bigger point I am making is the following:

  • For most transactions (not just in payments) , its a human at either ends. These individuals may have different environments, motivations, behavior and hence
  • The rate of digital adoption at both the ends may vary drastically
  • And this opens up a world of interesting opportunities as the use-case undergoes a fundamental change – bottlenecks will shift, old assumptions fail, new business models will need to emerge
  • And it in in these times that disruption works best. The incumbents may be too committed to the old model and the new players may have just timed it right.

Find what’s really broken before fixing it

I enjoy coding.

While I have a long way to go as a programmer, coding does put me in a zen kind of a state. The mix of learning and building something new, is almost magical.

It does fuel and inspire the problem solver in me. And it is a great teacher too.

I just learnt that ….

Finding out what is really broken, usually takes more time than fixing it !

Last week, I was trying to fix a bug on a piece of code that was first written few years back. The software itself has undergone multiple iterations and grown in its features and inherent complexity.

I spent almost the whole day digging deeper, going step by step to identify where the error was coming from. This meant tracing the code flow and checking the data integrity at each stage.

Once the exact source of the error was identified, the solution itself was just 2 minutes of code-writing. And then came the critical step of testing the updated code for all the test-cases, before moving it into production.

And as I ended my day and looked back on how my time was spent, it was an eye-opener to problem-solving.

Same is true for medicine also. The doc will suggest a few tests and look at the results to ascertain what really is wrong before suggesting any line of treatment.

SlashEMI’s debt reduction plan infact generates a detailed EMI fitness report, before starting any recommendations.

Note to self – Invest the time to be very sure about what needs fixing. (Problem framing is critical). Focus on fixing what is broken.

Sounds logical and intuitive right?

But do ask yourself how many times have we jumped into the solutioning mode without being sure about the real cause of the problem.

My guess is a little too many times.

Factory and Lab mindsets in product management teams

Factories and laboratories evoke very different images.

With a factory – I am usually thinking the industrial revolution in all its glory – machines, assembly lines, robots, workers, all working in a disciplined and predictable manner – churning out products that are all identical and meet the claimed specifications. Low room for error. Designed for scale.

On the other hand, when I think of a lab – I usually come up with a white coat wearing team of specialists pouring over data. Going deeper into a topic. Asking fundamental questions. Pushing boundaries. New ideas being discussed and prototyped.

While I know these are extremely simplified and probably exaggerated views, they are helpful in defining what I call as the Factory Mindset and the Lab Mindset. So bear with me.

The Factory and Lab Mindset

The Factory mindset or culture is where the blueprint has been validated and chosen for at scale business. Think of this as the mindset needed to to do more of the same thing at the right cost, quality – a highly process oriented approach.

And this is relevant in the service sector too. Think of a bank branch, or the claims underwriting team of an insurance co. The rule book, processes, roles and responsibilities are all clearly laid out. Thereby ensuring that customer after customer can be duly served.

The Lab mindset is what triggers change and innovation. It requires the ability to challenge the status quo. To ask fundamental questions, build hypotheses. Be bold enough to experiment with some of those hypotheses. And be ready to fail.

Need for both mindsets to co-exist

In today’s fast changing business environment, its imperative to nurture both these mindsets/cultures concurrently. More so within the product team(s) at consumer/enterprise technology companies.

And it’s tough !

In any multi-product organization, chances are that the products are at various stages of their lifecycle. Some might be in a MVP or Pre-commercialization stage, while (hopefully) most are on a scale-up path in the commercialization stage and few others may be close to the end of their life cycle.

Sarah is a product manager focused on an early stage product. She needs to be like a scientist – doing experiments, tracking whats working and what’s not and getting the product to evolve rapidly. More importantly her manager needs to guide, motivate and evaluate like a senior scientist would. The Lab mindset needs to prevail.

Contrast this with Peter, who’s product contributes to almost 15% of all revenues. A big part of his job is to keep the commercialization machinery humming. From updating pitch decks, to reviewing the pricing model, understanding the sales pipeline – its a very different role he plays. He also does some of what Sarah does full-time. Peter needs to have a close pulse of the market – understand the current pain-points, evolving consumer needs and build or modify features/functionalities to keep the product relevant.

This ability to toggle across the Factory and the Lab mindsets is critical for organizations and individuals to keep innovating, evolving and staying relevant while still growing.

Do you agree?

If you do, how do you think organizations should be designed to nurture this co-existence of seemingly different cultures and mindsets? Share how your organization does it well already. Because I feel this is more fundamental than just having different kinds of performance metrics depending on the stage of the product.

If you have found a way to do this well at your individual level please do share.

Going back to the earlier visualizations, isn’t it hard to imagine a lab-coat wearing nerdie walking around the factory floor? I remember from my engineering summer internship – the R&D deptt was in an air-conditioned separate section of the plant. The deptt infact had its own assembly line to make shock-absorbers!

Quest for Friction Less Experiences

Yesterday, I got to experience the WhatsApp payment flows. It surely felt like a neat friction-less experience both for adding/mapping bank accounts and for in-chat payments.

And in my excitement I forwarded it to a friend who didn’t have any UPI handle so far. And I was surprised by the reaction.

How does WhatsApp know my bank account ??!! 

Payment friction

And frankly I had looked at it the other way round – they are showing me the specific account that I want to associate here.

And this got me thinking about friction in digital consumer experiences.

I remembered my Amazon experience.

I have recently changed my laptop and phone and each time I logged into my Amazon account from a new device/browser I got a security challenge. I had to enter a security code that was sent on my email.

Friction during logging in

This is inspite of me authenticating myself using my Amazon credentials –  login id & password.

So why the additional step?  Why add to the friction of logging in?


  • Its a friction-less way of doing XYZ !
  • We have drastically reduced the friction in each transaction
  • Our platform provides the most friction less experience for ABC

Am sure like me, you keep hearing how every venture and corporate is focused on reducing friction and there by making it a significantly better experience for their consumers/stakeholders etc.

And I get it.

If I almost always use an offers platform to look for offers near me on a mobile app, it should not ask me to choose a city, then location etc – it should just pick my location and show me the offers. I get it.

Similarly, if my online or in-app payment process need an OTP and there is a way to automatically read the OTP rather than needing me to toggle from the merchant app to the messaging app and back. It is definitely so much cooler and easier.

BUT, ALL FRICTION IS NOT BAD

What I don’t get is how suddenly friction has become such a bad thing.

Way back in my school days, we were taught in Physics that while friction caused wear and tear, it also was the main reason wheels work – friction prevents slippage and aids rotation. Snow chains for tyres – aid driver confidence by increased traction (apart from helping break the top ice layer).

My current thinking on friction less experiences is as follows:

  • All consumers are not same. What is a great experience for some may be a concern for others (elevators vs escalators) . Hence it may be best to have varying levels of friction available for consumers.
  • Friction can help build consumer confidence – esp amongst users concerned about security
  • It may be useful in the on-boarding or early days of consumer-product relationship. As confidence builds, some more steps can be reduced. Like this recent experience where my Credit Card limit enhancement was pitched and delivered at the optimal moment.
  • Friction is also an industry level phenomenon. As an industry matures and consumer confidence builds, need for a faster, smoother way to do the same old task would become stronger.

What do you think?

Beyond Big Data – A Small and fast data example

Big Data is all the rage. Everywhere you go, any meeting or presentation one sits through, Big Data seems to be there.

But there are opportunities beyond big data. E.g. how we handle small data fast.

Here’s an example of small data that I experience almost everyday.

In many corporate buildings in India, you would notice that you need to punch in the desired floor into a panel, which prompts you which lift-car to hop on to.

Fast data ElevatorsSimple yet brilliant solution.

You club the waiting passengers into specific cars by their desired floors. The average wait time is lower, the average travel time to your floor is significantly lower.

And all the magic happens in a jiffy.

The data becomes irrelevant soon (apart from being used by the algorithm for learning and further optimization). And in a classic example of not-so-big-data. But the fact that this small set of inputs from users is taken, crunched and optimized for elevator allocation in almost real time makes it so amazing.

Small but fast Data.

In Data-led-solutions, the 4 V framework helps evaluate potential impact:

  • VOLUME – Available Data size. Is it big enough to build confidence in predictive models?
  • VERACITY – Data-accuracy: How accurate is the data that we feed into the system
  • VARIETY : Are we collecting data from multiple sources? What all do we know about the scenario
  • VELOCITY : What is the speed with which we process and move data? How fresh is the data as it moves across the value chain?

This small-data use-case is very high on the data-velocity parameter and I guess just solving for data-velocity has allowed for the solution to be adopted.

So in summary, there is life beyond Big Data too. 🙂

What do you think?

Good rules should be designed for higher adoption

How do we drive adoption for rules in a community, or even at a country level ?

Should a good rule be designed to make it easily enforceable too?

I think it should be.

If we want to build a society where most follow the rules, enforceability should be an important criteria.

To decide whether a new rule should be introduced or not. Whether an existing rule needs to be modified or scrapped.

Why?

It is my belief, that when we have rules that can be easily broken without any consequences, it sends a signal to the community. And this signal usually leads to a gradual loss of respect for the law of the land and for the fellow citizens. Read the explanation of the Broken Windows Theory

Effective rules implementation

Let me explain with an example of two rules, which most of us in India are familiar with

  • Front seat passengers should wear seat belts while traveling in a car
  • All vehicles should have a valid pollution-under-control (PUC) certificate

While both these were introduced in the last 20 years or so in NCR, the first one has seen significant levels of adoption whereas we all know that very few cars and bikes have a valid PUC certificate.

Why?

If you ask me, the reason is very simple.

For seat-belts, the fact that you are complying (or not) is visible each and every time you are driving. Any traffic-cop who sees you not wearing the seat belt can pull you over and issue a challan. So you run a very high risk of being punished if you are out on the road w/o wearing your seat belts.

Contrast this with the pollution certificate rule.

A traffic cop on the road has no clue if your vehicle currently has a valid PUC certificate or not. Hence the cop would rarely pull you aside asking for the certificate. It is usually asked for when you have already been stopped for some reason and they feel that they might put more pressure on you if you are w/o the PUC. Hence as car owners, we are usually not very afraid to drive w/o this certificate. The risk is just too low. And hence very few cars actually have a valid PUC certificate.

So while almost everyone knows that the laws need them to drive a non-polluting vehicle, very few actually end up doing so.

And I think its very simply just the issue of how easily the rule can be enforced.

In my opinion we should have few rules, but all should be enforced strictly.

What do you think?

Search Vs Social – the long tail of ad revenues

Google and Facebook together took away 64% of the total US online advertising spends. And Facebook had around 65% of the overall online display ad-spends. These are incredible levels of consolidation in the ad spends among the leaders.

search-vs-social

Enough has been said and discussed about

While one cannot argue with the numbers and the line of reasoning, I somehow felt that this discussion has ignored the long tail of ad-revenues or the lead generation aspect of these platforms. These reports are focused on big co’s with big media budgets who may typically have brand-building as the key target.

Let me explain this in some more detail.

There is no doubt that for Google or Facebook, the big marketing dollars would come in from big spenders like Ford, Coca-Cola & Pepsis, Samsung, Levis, Red Bull, Wells Fargo, Amex etc.

But if we were to evaluate these platforms from a start-up point of view (small budgets and maybe need to do lead generation instead of brand building), the story is very different.

1. Social targeting is profile based, too many bidders

On Facebook, the same user may be targeted by multiple brands, because there is hardly any other context. E.g. a 35 yr old male who lives in a metro and has liked multiple lifestyle brands would be a good target for many.

We do NOT have additional context for the specific session on FB when the ad is being displayed. One FB session is hardly different from another in terms of the intent or maybe when mood based marketing algorithms evolve things would change.

This means, each of the target users FB session will appeal to all the brands. Multiple brands would be vying for that same ad-impression, which in turn means higher bid rates and CPMs etc etc.

And this means that small budget advertisers would be elbowed out of the platform by big budget cos.

While this article on Forbes also has the same conclusion, the logic used is very different.

2. Search has deep context, removes non-relevant advertisers

Search on the other hand has hugely relevant context. E.g. a user looking for Mortgage loan options on Google will be targeted by Financial Services brands vs someone searching for Fine Dining Options in India.

And this means, that as an advertiser you are just competing with other competitors or maybe some adjacent industry players.

Bid rates would be lower and even with small budgets one can get the message out to a relevant audience.

3. Lead qualification is efficient on search

If one is looking at online advertising for lead generation, chances are search may be a better platform.

Before the Facebook fans pounce on me, let me qualify my statement.

Many of us run “boring” ventures – we pitch services that consumers may not want to share. And/or we do not have the creative bench strength to get a funny/interesting message out. Our content strategy may still be a WIP. Realities of life.

If the message/ad we create has low viral coefficient (i.e. we do not expect people to share it much), Facebook may not be the best platform. Coz then we are burning marketing dollars to talk to a prospect who may not be primed for our services and who is also not helping spread the word.

Google, on the other hand is a very different story. If a consumer is online actively writing into the search box key words that resonate with your offerings, you may have a very interested customer. Intent is high.

Also, my guess would be that the long-tail ad-spends are stickier.


But all this is just my 2 cents on how small ventures, start-ups and SMEs should look at spending their advertising money online – across the broad theme of Search Vs Social Marketing for the long tail in particular.

What do you think?

 

Unit Economics in the times of Auction Marketing Models

Unit Economics is all we hear these days in the consumer technology world. Unfortunately for many start-ups seeking venture funds, this is the biggest hurdle they need to cross to build a strong case for their business.

What is the concept of Unit Economics

I will not go into the definition and relevance of Unit Economics. That’s well documented here and here. Or just Google it.

Lets refer to Microeconomics 101 for our discussion – Marginal Costs(MC) and Marginal Revenue(MR). We all know that its a healthy sign if Marginal Revenues are higher than Marginal costs. And this delta (MR-MC) is what is unit economics.

On the other hand, if we are losing money on each transaction, either we see the losses reducing or we stop growing transaction volumes.

At least rational individuals would choose to do so. Or so goes the basic Economics assumption.

Unit Economics in web/mobile start-ups

  1. Marginal costs are volatile

A big chunk of a start-ups costs is the customer acquisition cost. ( I am excluding businesses with very high repeat volumes in early days where the operating costs contribute heavily to the overall transaction costs).

Most start-ups need to market their products and services. They are in a continuous state of transaction ramp-up along with concurrent improvements in experience or efficiency.

And in a world where most advertising/marketing channels are bid/auction model driven – this translates into the marginal costs being highly volatile. How volatile?

  • In the early days when you don’t have the luxury of brand-pull or of time, almost 60-70% of transactions may be coming from Google Adwords/Facebook/Ad-networks. Meaning 60% of your business is not insulated from pricing shocks.
  • Bid-rates may vary as much as 30-40% to maintain the same positioning. Maybe more, if there is a competitor who has just raised a round. Also, if you are competing in a category where big brands play, anything can happen. E.g. At Deal4Loans, we had seen bid-rates on our key-words jump significantly every time a competitor raised venture money or a bank launched a new digital campaign.
  • Add to this that the conversion-rates of your campaigns have not yet stabilized. Remember, these are early days, you are experimenting on your landing pages, and funnel optimization is still underway. So the final cost per account gets even more volatile.

2. Customer Pricing is relatively in-elastic

Theoretically, if you could pass on the burden of increased bid-rates and hence the ups/downs in marginal costs on to the consumer, your unit economics would be safe. The neighborhood vegetable vendor who has daily-prices does exactly this and is hence able to retain his margins.

Unit Economics

But this is rarely possible. Pricing is just not that elastic.  Most mobile/web start-ups can not /do not change prices so frequently.

3. Marginal Cost CURVE is UNPREDICTABLE AND NOT SMOOTH

We know that the bid-rates can inflict wild fluctuations(as seen in pt1) in the cost of acquisition, thereby making it unpredictable. But a bigger challenge is that the Marginal Cost curve is not smooth.

Realistic MC MR Curve

One rarely finds gradual changes in marginal costs with increasing through-put. It happens in unpredictable steps. Here’s why

  • Each fluctuation in effective bid-rate leads to drastic ups/downs
  • As a start-up you are experimenting with multiple channels. Success in any one will bring down the blended MC immediately.
  • Referral/Viral coefficient and % of in-bound of the campaigns can impact the costs significantly. e.g. One PR mention may bring in huge self-select traffic.
  • SEO traffic which is typically very predictable can also swing wildly with a new Google update as we saw with Penguin and Panda.

So what do we do?

  • Keep Experimenting. Do know that customer-acquisition at optimal price is a moving target. You are never really truly there. It can always be better.
  • Invest early in content. In-bound has significant ripple effects.
  • Raise money but don’t throw it all on branding. Consumer memory is short lived. Discover and test more channels, unlock access to more segments.

I would love to hear from bootstrapped ventures as to how they are/have handled the customer acqui costs. What worked, what didn’t?

Public policy, ripple effects and feedback loops

I have always been intrigued by product design and by extension policy design (& implementation). If the government were to look at itself as a start-up technology venture, the policies, schemes and guidelines issued by the government would possibly be the “products” of this venture.

And like any good product manager, one should study not just the immediate impact of change(s) in product design but also the delayed and maybe stickier changes in consumer behaviour.

And that is what I want to share with you today.

Shift in dietary habits due to Green Revolution

Sometime last month, I was visiting an uncle of mine – someone who is in his mid 70s, reasonably fit, exercises regularly and has borderline diabetes. While we sat at the lunch table, I noticed that he had multiple other grains in his roti as against mine which was from just wheat atta. It seems most physicians recommend adding ragi, chana etc in your atta mix as a healthier alternative.

And that’s how our conversation began.

Wheat Green Revolution

And what came out was quite surprising for me.

It seems in their childhood days in villages of western U.P., wheat was not the staple grain. Infact it was considered a delicacy and wheat-chapattis were made when they had guests over. And he comes from a well-to-do farmer family. This was not because of economic constraints, it was just how things were.

So as the elders started talking about this significant shift in probably the most important component in a typical North-Indian meal – roti – what emerged was that the shift was triggered by the Green Revolution in all probability.

This lunch group which included scientists and government employees, agreed to the following sequence of events:

  • Wheat was one of the chosen candidates for green revolution . Though am very curious to find out why?
  • Government stepped in on the supply side with higher yield varieties, irrigation support etc
  • It also created artificial demand by setting up floor prices thus encouraging farmers to grow wheat. Making wheat a critical component of Public Distribution System also ensured a big buyer for wheat at these prices. This in turn ensured that a higher percentage of land under cultivation now got sowed with wheat
  • This brought the otherwise-considered-premium grain into the middle-class households at a very affordable price. Imagine if suddenly, you find yourself able to afford an item which for years or maybe generations was considered premium, chances are you will buy more of it to feel good (my assumption)
  • And they all started eating wheat more, skewing our diet heavily towards this singular grain in North India.
  • And the subsequent generation(s) like ours has come to believe that our rotis have always been a wheat-only product. Coz wheat rotis is what we ever saw.

Am also very clear that India’s self-reliance on nutrition has been contributed heavily by progress on wheat and rice. So there’s no doubt that this has worked as planned.

The fact that wheat may not be the healthiest grain is probably something new. Gluten intolerance was probably unheard of during the Green Revolution.

But with the new facts before us, should the government re-evaluate its focus on just a handful of grains in its policies.

What if, the support prices on wheat are relaxed a bit? What if other “healthier” grains are encouraged similarly? Will the cost of managing supply chains and warehousing for multiple grains offset the advantages of a wider-spread in our diet?

Many questions and I don’t have any answers.

Low availability of fodder for cattle

Ask any elder who has seen standing wheat crop in the fields now-a-days vs in the old days. One thing they would tell you is that the wheat crop is now stunted. Its much much shorter.

This am told, was probably one of the biggest breakthrough in developing High-Yield-Varieties. The nutrients and water is no longer “wasted” in the growth of the non-grain-yielding parts of the crop.

But on the flip side – this has increased the cost of cattle-management for local farmers. Why?

There just isn’t enough fresh fodder for the cattle. The non-grain part of the wheat crop was used as fresh and dried fodder for the cattle that the farmer had at home. This is gone.

As my friend (who runs a dairy farm) tells me, procuring fodder is now a big challenge in most regions.


I am not an economist or an agriculture scientist and probably have understood just a very small part of the whole picture here.

But I learnt few important lessons from this lunch conversation :

  1. There are usually multiple ripple-effects of any new policy change ( or product change)
  2. While the product may deliver on the core metrics initially identified as measurements of success, we should zoom-out and ask ourselves, what else has changed
  3. I should start eating healthier. Right now  🙂