E-commerce: Ensuring fair practices in new marketplace concepts disrupting the linear supply chain

A brief history of supply chains in the 21st century

Traditionally, supply chains have been constructed as linear vectors with varying magnitudes but primarily unidirectional. This upstream and downstream paradigm is changing. The advent of internet, cross country supply chains, captive capability centres have ushered in a new era of commerce: E-commerce. The WTO defines e-commerce as the production, distribution, marketing, sale or delivery of goods and services by electronic means. Thus, e-commerce can potentially impact the entire value chain. Legacy monolithic ERP systems decoupled digital and physical flows but in today’s world of Artificial Intelligence, the two flows are converging with products increasingly being tracked. This owned data is restructuring business processes at a rapid pace. For example, a Fortune 500 retailer is able to better assess the inventory performance through stock digitisation; allowing it to restructure existing processes to liquidate slow-moving inventory. Similarly, heavy machinery factories are increasingly predicting wear and tear, enabling resources to repair such units while moving production to other assemblies. 

New Business Models

In effect, new business models are also emanating because of digitised supply chains. The ability to track a product’s usage post purchase lends itself to an entirely new spectrum of business models. It allows us to move from a product centric business model to a service centric platform based business model. With a constant stream of data, revenue models will increasingly be based on outcomes. For example, Rolls Royce now charges Airbus and Boeing on engine hours usage instead of a flat fee or the entire gamut of subscription businesses like Apple Music or Amazon Web Services which apply the pay per use model. Secondly, value added services are lent new transparency and can thus act as an additional stream of revenue. For example, logistical firms like Rivigo can now analyse shipments and charge an increased fee if a perishable requires superior packaging or temperature control. 

In conclusion, new forces in the market are shifting the traditional linear supply chain to a more networked, fluid ecosystem. Regulation thus has to keep pace with the market evolution. According to traditional theory, e-commerce through its network effects and lower search costs, allows more efficiency, lower prices, increased competition and economic surplus. As the market matures and e-commerce academic literature increases, a more holistic analysis is required. 

Regulation in India

Two key stakeholders in the Indian context are the Competition Commission of India (CCI) and the Department for Promotion of Industry and Internal Trade (erstwhile DIPP). The CCI is tasked with -

Promoting and sustaining an enabling competition culture through engagement and enforcement that would inspire businesses to be fair, competitive and innovative; enhance consumer welfare; and support economic growth.

The Competition Act 2002, which gave birth to the CCI states – No enterprise or association of enterprises or person or association of persons shall enter into any agreement in respect of production, supply, distribution, storage, acquisition or control of goods or provision of services, which causes or is likely to cause an appreciable adverse effect on competition within India.

The following agreements come into its purview - 

(a) tie-in arrangement;

(b) exclusive supply agreement;

(c) exclusive distribution agreement;

(d) refusal to deal;

(e) resale price maintenance.

This was drafted keeping the traditional supply chains in focus. But with the new age supply chain networks as elucidated in the previous sections, regulation needs to keep in my mind the wide range of exchanges and contracts mushrooming today. A particular distribution system – online marketplaces have scaled rapidly in India and can no longer be protected under the garb of nascent technology. Infused by VC capital and FDI (case in point - Walmart acquiring Flipkart for $16 billion), these feral platforms have stayed fairly unregulated. The spectral data being generated by such platforms is now delineated as digital capital by the DIPP in the National e-commerce Policy, conferring data the status of capital at par with financial capital of a corporation. One of the dangerous antitrust issues cropping due to digital capital is the proverbial first mover advantage in a large market driven and consumer oriented Indian economy. The more data a corporation generates, the more digital capital it possesses, which lends itself a competitive advantage to incumbents in the space. MSMEs and startups could be at an economic disadvantage due to the new kind of entry to barrier today – digital capital. Streamlining the access to data, while protecting privacy of users, in the current vibrant start-up culture would be a win- win situation for all stakeholders.

Case Examples in India

After going through multiple cases heard by the DG, CCI; two cases in this space of online marketplaces caught my attention – Case No. 61 of 2014 and Case No. 20 of 2018. 

Case No. 61 of 2014 was filed by Jasper Infotech Private Limited (Snapdeal) against KAFF Appliances India (Kaff). Snapdeal is an online marketplace and Kaff is engaged in the manufacture and sale of kitchen appliances which inter alia includes chimneys and hobs. This was in light of a caution notice which Kaff published on its website stating that Kaff’s products on Snapdeal are sold without its authorization and are counterfeit. Further, Kaff would not honour any warranties of such products and Snapdeal argued this as violating Section 3(4)(d) of the Competition Act (which is Refusal to Deal). Such vertical agreements/arrangements under Section 3(4) of the Act are considered anti-competitive only when an appreciable adverse effect on competition (AAEC) is established. Kaff’s chimney sales on Snapdeal actually quadrupled and hobs sales on Snapdeal doubled in the duration of the caution notice and thus an AAEC was ruled out by the DG, CCI. 

Case No. 20 of 2018 was filed by the All India Vendors Association (AIVA) against two parties – Flipkart India Private Limited (Flipkart India) and Flipkart Internet Private Limited (Flipkart Internet) again alleging inter alia contravention of the provisions of Section 4 of the Act. AIVA is a company registered under the provisions of the Companies Act, 2013, is a group of more than 2000 sellers selling on e- commerce marketplaces such as Flipkart, Amazon, Snapdeal etc. and Flipkart India (B2B) is engaged in wholesale trading/ distribution of books, mobiles, computers and related accessories whereas Flipkart Internet is engaged in e-commerce marketplace business. Flipkart Internet (B2C) connects buyers and sellers on its electronic marketplace platform and collects a platform fee from the sellers. The main contention of AIVA was that small vendors have become suppliers to bigger vendors like Cloudtail and WS Retail (owned by Flipkart founders until 2012) on platforms like Amazon and Flipkart respectively instead of directly selling to consumers on the platforms. Online platforms like Flipkart India procured the goods at a discounted price and used to sell it to companies like WS Retail, allegedly delivering preferential treatment to select sellers. (Interesting to note is 100% FDI under automatic route is permitted in marketplace model of e-commerce and not an online retail store). The fact that WS Retail Services Private. Ltd. is no longer a seller on the Flipkart Marketplace post 11 April 2017 and AIVA provided insufficient data inter alia with respect to Flipkart’s market share, led the DG, CCI to close the case. 

Conclusion and Next steps

As digital capital and omni channel become more paramount in the Indian context, safeguards needs to be in place to ensure fair competition. Predatory pricing, deep discounts and loss funding are not completely unfounded claims proposed by the Confederation of All India Traders and this led the Indian government to ban e-commerce companies from selling products from companies they have invested in with effect from 1st February, 2019. Additionally, companies will be prevented from entering into exclusive agreements with e-commerce sites which implies that OnePlus will not be able to sell its OnePlus phones exclusively on Amazon and Xiaomi will also not be able to sell its products exclusively on Flipkart.

The following I believe are the need of the hour in light of increasingly complex supply chains and new business models: 

1)   Education and awareness of the Competition Act, 2002 is urgently required for both consumers and suppliers. 

2)   The ambit of Competition Act, 2002 needs to be revisited and increasing its purview in light of e-commerce needs to be discussed. 

Measuring the Digital Economy

Gross Domestic Product (GDP), the de facto metric of economic health, is in dire need of an overhaul. GDP was created to measure monetary value of all final goods and services produced within a country. This cardinal number is now widely used as a proxy for economic well-being. However, Simon Kuznets, who led the team which formalised GDP, warned that

“the welfare of a nation can scarcely be inferred from a measure of national income.” 

Some known areas where GDP fails to account for economic well-being are:

1. Negative externalities created due to economic growth; like pollution.

2. Non-market activities like household work.

The 21st century challenge:

Increasingly, as zero or low priced digital services have become ubiquitous (think search, email, social media, with almost 6 hours and 42 minutes spent online per day per capita basis), there is a need to come up with a new way of measuring economic well-being. As per traditional macroeconomic theory, GDP is typically calculated basis what consumers spend for goods/services and since prices of most widely consumed digital services are near zero, they go unaccounted for in GDP calculations. Even for consumer subscription services like Netflix, Hulu, the subscription fees has been found to be a gross misrepresentation of the actual consumer value. A data point which drives this point home is that the share of IT in the US GDP has remained between 4-5% for the last 40 years. 

The impact:

Most policy makers rely on GDP data to make decisions across a breadth of dimensions - for fiscal and monetary decisions, to design policy that affect firms offering both digital and non digital goods and services. Since, the digital services are highly undervalued, the resulting decisions and policies are made with skewed assumptions. As economist Robert Solow famously commented in 1987 -

"the computer age was everywhere except for the productivity statistics"

(also called the Solow Paradox), alluding to US's anaemic productivity growth then, which was regained soon in the 1990s productivity boom (with semiconductor and manufacturing innovations). But as a lot of policy makers call for regulation of big tech and design incentives for digital service based businesses, the first step is to understand the nature of and the value being generated from digital offerings.

The solution:

Thus, there is a need to compute economic well-being via value generated in the economy and not pure play consumer spend. Free market economics proposes prices equal marginal cost, but marginal cost for digital services tend to be zero or near zero. Popularised by the economist Alfred Marshall, economics already does provide a base measure to capture economic value: consumer surplus. It is defined as the difference between the maximum a consumer would be willing to pay for the good or service and its price. MIT economist Erik Brynjolfsson and his team has developed “GDP-B” as a concept to capture the economic value of both zero and positive priced digital goods. Their model involves estimating consumer willingness to pay via massive online choice experiments (through both best-worst scaling and single binary discrete choices techniques to obtain ordinal ranking of digital goods, associated monetary values and derive demand curves). For example: asking the survey participants to forgo access to Facebook for a month for $500. This monetary value has been observed to be affected by substitutes (for ex: lesser value attached to losing Facebook access given that there exists Instagram, Snapchat etc), network effects (people with more friends tend to attach a higher value to Facebook) and demographics (for ex: older people tend to attach a higher value to Facebook since perhaps there are higher switching costs for this segment to move to alternative platforms). The benefits of using such survey methods are that:

1. Scalable to zero or positive priced goods.

2. Run near real time to measure well-being performance. 

And the challenges of using such survey methods are that:

1. Not as comprehensive and precise as GDP.

2. Does not capture negative externalities involved with digital consumption like addiction, lost privacy etc. 

The "GDP-B" model tends to lie in the middle of the spectrum between precise traditional measures like GDP to more subjective measures like the Happiness Index. With the question of technology regulation heating up globally, including the value generated by the digital economy can help policy makers design rules and regulations to incentivise and subsidise the new innovation streams as well. In the Indian context; players like Gaana (music player integrated with MX player now), Hotstar (video streaming), TikTok (video sharing), Google Pay (UPI based payments app), Newshunt (local news) and others across the digital stack are acquiring users at a blitzkrieg pace and it is imperative for the Indian government to include value generated from digital services in framing digital and content regulations.

Human Behaviour in Competitions and Rationality

Many a times while pursuing my undergrad, I used to partake in part time musings in classes which I did not like but was forced to take. This thought was the result of one such time and I am sharing this here after almost 8 years. Would love to hear more from people reading this.

Experiment:

Say, a random town is selected and all its inhabitants are asked to select a number from 0–100(inclusive), and the winning number will be the number closest to the half of the average of all numbers selected, then which number would have been selected?

Assumptions: The individuals selected conform to the present definitions of ‘rationality’.

Reasons for:

1) 0  -  As half of 100 is 50, none will guess more than 50, since the maximum possible number is 100 and half of 100 is 50. Thus, the sample of numbers to be selected reduces to 0–50. Again, thinking about the other individuals, and half of 50 being 25, the rationalists will reject all numbers greater than 25 and so on. This iteration should continue until the winning number becomes 0 and this shows that every person thus assumes perfect ‘rationality’.

2) Between 0 and 50  - The number thus selected shows that the iteration is stopped after a point of time, i.e. , the assumption that everyone is rational and that everyone believes the other is rational stops after a stage(if true, to be calculated after a sizeable stock of data is available). Since, to obtain 0, it requires that everyone assumes that everyone assumes that everyone assumes … that everyone assumes that everyone is rational (an infinite nesting of those).

3) More than 50  -  I haven’t thought of a reason yet and this occurrence can be attributed to data errors, or some other attribute of human rationality unknown to me.

From the data I collected, the average of the numbers selected was 14.833.

Conclusions:

1) This shows that the assumption of rationality does hold true but to an extent as the number does not fall all the way to 0. It actually shows that it stops near the 3rd iteration which is 12.5.

2) As opposed to the rationale for invisible hand of the markets given by the Adam Smith, where he said that the maximising of each individual’s own utility would necessarily lead to the best result for the society, and as an extension to the work done in Game theory where it has been shown that the ‘best’ result for an individual in a competing game might not result in the ‘best’ solution for the society as a whole, case in point  - cartels(though, I have analysed only the optimal solution in the game of Prisoner’s Dilemma), this study shows the depth of human rationality. The study shows how actually an optimal solution is reached as opposed to the just the final selection; we get to know in a competition where humans have to make the best choices by predicting others’ choices, to what extent and depth does human rationality extend to.

3) Still, it would be unwise to structure human rationality on the basis of this study, but it does throw light upon the capability of human decision making and human rationality.

Implications:

These conclusions have widespread implications in all forms of competitive games including stock markets, arms race by countries etc. Here I will analyse only one  -  Asset Bubbles.

The above study if applied in speculation in the asset market can help one in making a lot of money with a caveat  -  it can also bring you below the Indian poverty line, which is around $0.5/day .

Say you guess the value of an asset for pure speculation to be of G, which is between 0 and 100, and that is your reservation price (price at which you are indifferent between buying and not buying) and the actual market price is ½ of all guesses. So, if G=5/share and actual price turns out to be 12.5/share, then you are a profit as you can sell for a profit of 7.5(12.5–5)/share. Thus, at any price, G, less than 12.5/share, you profit. But as your reservation price is 5/share you would not buy at any price greater than 5/share, thus losing out on profits.

You can see there is an incentive to bid (guess G) higher than is suggested by appeal to common knowledge of rationality. You want to participate in this market (this game) to make money. You can’t make money by sitting on the sidelines. But that is exactly where you’ll be for sure if you guess G=0.

This is still the game as was studied. I have just interpreted the guesses and the winning value in a specific way. So we know the “right” price of the stock based on an argument of common knowledge of rationality is 0. But we also know that P (1/2 of the average of guesses) will not be 0. In fact it is likely to be close to 20. Everyone who is able to buy the stock at a price below P can make a profit and they’re rational to do so. It is not irrational to set G above zero. In fact it can be a very smart thing to do (in this game).

Therefore, a speculative bubble for a worthless stock can develop for which the price is far above the “right” price. Many market participants are behaving rationally. The bubble exists because an assumption of common knowledge of rationality does not hold. But an asset bubble is not a one-shot game. Players buy and sell multiple times. Eventually additional iterations of assumptions of rationality emerge. The price begins to fall. The bubble bursts, the price goes to zero, and everyone becomes a bear.

The version of the game presented here is different than the one presented previously. In particular, the payoffs are different. In the version presented before, there was a winner  -  the player(s) that guessed closest to 1/2 of the average of the guesses. In this, many players can profit (by differing amounts). Though, someone may make the most money (per share), I haven’t really defined a “winner.” Payoffs (incentives) can change strategy. It is therefore possible that 1/2 of the average will be different in the two versions of the game. Strictly speaking, it is not correct to assume that a number near 12.5 will be 1/2 of the average in this version even if it is for the previous version. Nevertheless, I’d bet a fortune that 1/2 of the average will not be zero in either game played by a population not familiar with it. After that population plays many times, the answer will likely tend toward zero. How many iterations will it take to converge? I do not know.

Community Building

A large part of my personality has been shaped by my innumerable travels, which began very early in my life. India is a very diverse in terms of language, religion, attire, cuisine and dress. Having lived and studied in many different towns and cities of India, I adapt well to new surroundings and people.
I am curious to learn about new places, associated culture and history and the evolution of their society. I have picked up different languages like Marathi, Punjabi, and Sanskrit and I am currently learning Spanish and Arabic.
As I grew up, my travel horizons also widened and led me across more than 25 countries, discovering newer perspectives and increasing my understanding of human behaviour. One thing I learnt is that people everywhere share the same aspirations - to be happy, safe and healthy, to preserve their self-esteem and freedom and to feel empowered.
I have tried to contribute to this in my own small way by volunteering with Umang, a school for underprivileged children and where I taught 8-12 year olds basic Mathematics and English besides writing the class 10th Board Examinations for poor blind students. A rural sensitization trip to a village in Uttarakhand helped me understand rural life and gave me a rare chance to live and work with a local family.
Currently, a Global Shaper at the World Economic Forum, I focus on eduction and digital up-skilling for the underprivileged. This allows me to collaborate and work with local civic authorities, governments, NGOs and corporates.

Auctioning Cab Rides: An Alternative to Surge Pricing?

Donned the hat of a consultant for my terminal project on Information Economics during my MBA, along with my study group members. We analysed Uber's current model and recommended a First Price English Open Auction model using inspiration from PageRank’s Markovian algorithm to solve multiple information asymmetry issues. Writing the crux of our proposal here.

Executive Summary:
Cabs are generally hired - be it the standard yellow taxi-cabs of old or the new-age cab aggregator apps like Uber that leverage mobile and technology to map cabs to the customer. However, these regular cab-hiring models come with their fair share of marketplace problems. In this project, we take Uber as an example of present day cab service and look at how the introduction of auctions into the process of cab hiring can solve the standard issues of transparency, matching market demand and ultimately debate if auctioning cab rides can be a viable alternative to the much disliked surge pricing. With just the right amount of gamification, one could end up bidding for cab rather than just hiring it! Uber is a ride hailing platform. Valued over $72 billion with an asset light model, it has heralded a new era of market economics.
The two biggest problems plaguing the Uber of 2019, pre-IPO are:
• Transparency (reducing information asymmetry)
With major PR problems on both rider and driver sides - pricing clarity and revenue clarity respectively, the core value proposition is itself in question. Is Uber a platform or a taxi service? This needs to be clearly elucidated since this has been the center of most lawsuits against Uber.
• Maximising economic (consumer & producer) surplus
Allocation of demand and supply through efficient matching of riders and drivers - maximises economic surplus. This benefits the riders, drivers and Uber (all three stakeholders) by enhancing the aggregate pie. With increasing competition across countries, Uber is facing a real threat as Lyft has already one-upped by announcing an IPO earlier in USA. With Didi acquiring Uber in China, lawsuit woes in the EU, among many more, Uber is really in a quagmire and needs to up its game. We propose auctions as a solution to tackle the above two problems. We analyse the First Price English Open Auction in this report specifically. This will entail a base fare (adjusted for demand) and an incremental bid determining the likelihood of a user matching with a driver. Present Scenario
Surge Pricing Model
Uber’s current model is Economics 101; shifting fares until demand and supply curves match for general equilibrium to be fair to partner drivers and users both. The pricing algorithm pings for demand data on the Uber app every five minutes and updates it to change the pricing surge accordingly. This data is visible to the rider and partner driver as well.
However, surge pricing can work in any/all of the below mentioned methods:
• Reducing demand for cabs (fewer people desire a cab for an increased fare)
• Generating new supply (providing an incentive for new drivers hit the roads)
• Moving supply/drivers to areas with higher demand
Limitations Thereof
• Surge price results in fares altering awfully frequently and is highly localised
• Instead of attracting more drivers on the road in the short-term, Uber’s surge pricing reduces driver supply in adjacent areas[1].
Thus, surge pricing appears to push drivers already on the job toward neighbourhoods with more demand and higher surge pricing, instead of bringing more drivers out on the roads.
Proposed Solution
Alternative Auction Model
This model will function as a First Price ascending bid auction. It will let the users bid in fixed increments on top of the base ride cost, increasing the likelihood of getting a confirmed ride when demand outstrips supply.
Features
In order to keep the user experience as quick, smooth and streamlined as it is now, there will exist certain parameters which a user will be able to define as his/her preferences for the auction model.
Mechanism
Inspired by the Google Ads Auction System, the customer will be matched to a driver, not only based on the relative standing of his bidding price but based on an aggregate rank calculated using the following four attributes
• Bid price: The price which the customer is willing to pay for a ride; basically, higher the bid, the better it is.
• Customer Ratings: A score on the scale of 5 calculated as an average of all the ratings the customer received on his /her previous Uber trips. Again, the higher the rating, the better it is.
• Number of Trips: The total number of trips taken by a customer with Uber worldwide. Again, higher the better.
• Minimum distance to the cabs: The customer’s minimum distance to the available cabs. This factor needs to be as low as possible to ensure proximity of cab. This is considered to give optimal weightage to the proximity of the user to the available cabs.
Service Improvement: How the proposed model makes cab-hiring better
The process of cab-hiring, rather cab bidding is enhanced with the implementation of the proposed first bid auction mechanism due to the following factors:
• Price Discovery: The auction model allocates rides to the users at the prices decided by the value customers associate them with rather than decided by the Uber’s internal surge pricing mechanism. This also empower the customers in assigning their own value to any ride. While in the present scenario, the user has an option only to reject or accept the offered ride at the shown price.
• Transparency: The auction model will make Uber’s ride pricing process much more transparent as customers will now be able to see the aggregate demand at their offered bid price and dynamic probability of securing a ride.
• Increased Market Thickness: As the customer feels more empowered & has an enhanced user experience, a greater number of customers will be attracted to Uber’s platform. As the customers increase, with positive cross-side network effects, number of drivers will also increase. This increase on both sides of platform will lead to increased market thickness with an enhanced experience, which marks impeccable market design.
• Increased Retention: As the customers’ aggregated rank considers the number of past transactions, this will act as incentive for the customer to stay loyal to Uber’s platform & not multi-home with other ride-sharing services.
• Alleviate Congestion: Customers, who are more price sensitive, will try to minimise their cost by participating in the bidding earlier than when they actually need the cab. This will lead to better congestion alleviation than as happens with the current surge pricing model.
Potential Downsides of Proposed Model
Though there are very few potential downsides of the suctioning model, it is worth discussing them in context of the application in Uber’s platform.
• Disutility from booking a cab: As the customer needs to participate in the auction every time he books the cab, there might be a disutility for the customer. To overcome this weakness, we propose adding a feature, wherein the customer can pre-empt and provide his default desired probability of getting a cab in the Profile Settings. Once updated, the system will automatically bid on customer’s behalf according to the preset probability, simplifying the process.
• Increased time for booking: The total booking time might increase due to the introduction of auctions. However, with the enhanced experience, transparency and ultimately satisfaction, it essentially boils down to a trade-off between increased time & more transparency/customer empowerment.
• Emergency Situations: If a customer needs a cab during an emergency, he might not want to wait & participate in the auction. This might lead to attrition. To circumvent this, the customer may be presented an option of choosing a price at which a cab is guaranteed.
Recommendation / Implementation
Uber’s goal is to be as close to a marketplace with perfect competition while maintaining service quality. The latter is a separate problem, targeted through proper on-boarding and star ratings. We have targeted the former problem through an auction model. This should be implemented through a native app-based interface which will receive live inputs from users, calibrate probabilities and match via a ranking algorithm. The ranker, taking inspiration from PageRank will involve four inputs as outlined above. The bidding-based system will be released in subsequent app versions in a phased manner (at select locations where demand greatly exceeds supply) for A/B testing to observe changes until a full-fledged release. There are certain risks as outlined above but the proposed solution is a more economic viable solution in the long term, increasing transparency and efficiencies - near perfect competition in an impeccable market design.

Reference: [1] How Uber surge pricing really works, The Washington Post, April 2015