Outsourced analytical solutions for manufacturers and retailing companies in the form of consulting support is a thing plainly used around the market. However those are only local solutions, and they are lacking access to the grander scale. While data storage grows bigger and analysis gets more complicated, it’s not a thing that a small team of people can handle anymore…or maybe they could? Powered by cutting-edge IT-applications and the sheer power of a blockchain approach, the OSA DC team, led by Alex Isaiev, makes things much more interesting with their complex approach to the everyday need of their clients. Today we have a wonderful opportunity to interview them for you, to enable you to learn about the background of the projects and their realization of blockchain features in a most practical way.

Privet, OSA team. Seems like this interview will be rather off-the-rails. You see, the full version of your white paper is so big and comprehensive that it already covers some of our routine questions, including team introductions and some relevant history behind the project. But we would like to ask it anyway, and something beyond the aforementioned sources would be much appreciated.

We’re sorry if the greeting was not appropriate, it’s just that we decided that the core of the team consists of specialists with Russian origin. It also seems that the roots of the project are growing from interactions with manufacturers and retailers on the local (Russian) market. Does it have any specifics that distinguish it from other local markets around the world? Is it facing the same problems as other local markets? Could this mean that you are planning to go worldwide eventually?

Key shareholders are indeed of the Ukrainian and Russian origin. As you may know OSA DC spins off an existing business, OSA Hybrid Platform, that was developed and successfully operates in the Russian Federation.

OSA Hybrid Platform indeed operates with Russian entities of the international manufacturers and the retail chains. Having said that, the issues that OSA Hybrid Platform solves for our clients in the Russian market are common world-wide. We do indeed plan to enhance and expand our platform globally.

We’re not experts when it comes to retail and FMCG manufacturers, but we’d imagine the competition among them should be pretty high. Why do you think they would want to use common platforms and share their personal data to the general pool instead of using their separate means of realizing the same solutions?

You are absolutely right, all retailers and major manufacturers seek to find the solutions that would optimize the supply chain. The core issue they face (and the key factor of success for the OSA Hybrid Platform solution) is that they work on this issue in isolation, at best involving 3rd party IT suppliers. Whereas the issue involves joint efforts of all members of the supply chain and, above all, exchange of clean big data that is possible to analyze. Many retail chains have found partial solutions to their issues, but much like the current OSA Hybrid Platform solution, they only tackle part of the issue. The issue of imbalanced stocks, for example, lies across the whole supply chain in its entirety and it is not possible to be solved just using the retail outlet/chain.

There are multiple instances in your whitepaper where you point out that your project aims at greater goals than other projects, and that you share interest with ‘competitors.’

“OSA is not limited to one vendor — retailer pair. “; “OSA does not stop with consumer behavior prediction algorithms within the store.”

How far do those goals stretch? It’s also mentioned that you think that providing individual solutions for business is not as productive as creating a platform for people to perform some of these actions by their own means. So could you give an example of how that interaction works with some abstract partner-company, and what functions do you take upon yourselves?

Correct. The key values of our solution are:

1) Availability of huge amounts of clean analyzable data and

2) LEGO type platforms capable of integrating 3rd party solutions.

To give you an example based on current OSA Hybrid Platform functionality: With the amount of big data that we collect from multiple sources it is possible (and not limited to) get the following solutions and insights for the participating businesses:

• Optimize retail shelves to maximize volume or profit

• Optimize shop floor workforce

• Optimize pricing and stock holding depending on the individual business objective

• Effectively plan and conduct promotions

• Monitor compliance of retailers to the pre-agreed KPIs with suppliers

• Offer businesses pricing and volume scenarios

• Stock planning depending on weather forecast and 30 other parameters

• Demand planning

Many of these tasks are subject to the high levels of expertise in every one of these areas – like – optimal price volume relationship which is currently provided by a number of research agencies, but not performed using real time data. Instead they rely on multiple artificially orchestrated consumer research endeavors.

OSA DC will have much more data. We plan to maximize the use of the available data and release the data to these 3rd party agencies, IT and data science developers, and hubs on our platform to perform these tasks without trying to do it all by ourselves.

It’s also mentioned in your white paper that OSA is not going to limit your activities with stock optimisation and would work for logistics as well. What difficulties might this present with the automation involved? What other services might you provide in the future? What sort of demand from retailers and manufacturers are you expecting in this regard?

Most importantly OSA DC will have 2 large business directions (within the same platform) – B-2-B and B-2-C. Objectives of the 1st direction – optimization of the business processes, maximization of volumes, profits and cash flows for the short term, and in the longer term having access to valuable data to assist in improving the quality of provided goods and services. The 2nd part – adding the value to the shoppers and the end consumers – the principally new thing that will define OSA DC platform.

Machine and Deep Learning have become buzz words for every marketing department nowadays. It’s true that you mentioned it a lot, but could you describe how general and “big” data could be helpful for specific retail units, like a single shop?

It’s also worth mentioning that data specifics (standards, factors, and specific models) should be considered. Every retailer and manufacturer has its own set of catalogs for the goods they provide, and it might become troublesome to understand from data what the product actually is from time to time. What types of models are you going to create?

The other important obstacle that we aim to overcome with OSA DC is that currently goods (as they move along the supply chain) are not continuously tracked – they are just passed from one operator to the next one. This prohibits tracking the right amounts of goods at every stage, conditions of storage and handling, and makes it impossible to optimize efficient flow. We plan to clean all that data with the help of a global master data catalogue and a blockchain enabled comprehensive continuous tracking system that, equipped with IoTs, will monitor the amount of goods at every step of the way, how they are stored and handled, and give real time prescriptions to the operators of the supply chain.

Big data can solve many of the above problems and many problems beyond those mentioned – AI processing to be far more effective in negotiations, forecasting, planning and tracking.

We’ve been hearing for decades already that globalisation is a big thing and institutional entities would go bigger and bigger as the time progressed, at least from the mainstream point of view. This means smaller retailers and manufacturers should eventually be driven out from the market. But in fact this reality forces them to work together to compete with bigger retailers.

So in this regard, how can much smaller entities benefit from your platform? There are other points of view stating that smaller entities could be more competitive than huge organisation for various reasons. Do you see some point of growth for smaller enterprises in the economy to expand in the near future?

We do not favor small operators over big ones in our project. We add value to our clients, no matter what size. At the same time it is expected that the smaller chains, having more challenges like those that you mentioned, and less red tape in the decision making process, will be faster in getting OSA services on board and benefiting from them. Having worked on the consumer goods manufacturing side I can confidently tell you that the manufacturers are interested in having healthy competition on the retail market, as retail consolidation slows retail development and leads to hand twisting practices.

We’ve also almost skipped the major party in the goods consumption chain here: the actual consumers. So plainly speaking, with your services applied they can get all the food they need and might even save a little money alongside companies having their costs reduced.

But this is not all, right?

There is the Smart Consumer Approach present as well. This provides a means to overcome a reality with too many intermediaries between manufacturer and consumer. Then, the question is how to get those consumers to change their old behaviors. How should current market agents react to these changes to stay competitive in the future? For instance, will retail slowly die out or it will just change its form?

OSA DC is not about changing the existing intermediaries or forcing a new way for consumers to shop. It is ultimately about giving shoppers comprehensive product knowledge and allowing them to make educated choices, avoid damaged, unhealthy, and allergic goods, while also enabling them to pursue their desired lifestyle. All of this happens simultaneously whilst saving time and money.