AIA Ecosystem

Big data and AI-based solutions for sales growth, offering the capabilities of advanced IT infrastructure:
- Monitoring and control of the key KPIs across all operations
- Ensuring product on-shelf availability in real time
- Digital transformation and data management in retail
- Precise demand forecast for promo products

Current clients:
Retail clients: Magnit (1500 stores, Russia), Dixy (2525 stores, Russia), Magnum (89 stores, Kazakhstan)
Manufacturing clients: Mars, Mondelez, PepsiCo, Borjomi, Iceberry, Renna Group, Splat, Progress, Nestle, Hygiene Kinetics, ASG, Sady Pridoniya, Efko, Unilever, Danone

Industry: CPG retail
Target client #1: Large retail chains (top 10 or top 50, depending on the market
Target client #2: Large CPG manufacturers

Total available market - $11.75 billion, including:
Russia - $1.2 billion
Ukraine - $0.2 billion
Kazakhstan - $0.2 billion
Turkey - $0.15 billion
United States - $3 billion
China - $5.6 billion
Japan - $0.9 billion
Korea - $0.5 billion

Problem #1
Product on-shelf availability is the #1 issue for grocery retail everywhere. When the product is not available, both retailer and manufacturer not only lose a sale, but also risk losing shopper's loyalty. High costs and product wastage are also part of the problem.
Problem #2
Lack of transparency and lack of understanding as to:
- what happens at store level and in the supply chain in real time
- how does it affect the sales
- how it may cause the loss of sales in the nearest future
- what can be done to save the sales and preserve profit.
Problem #3
The current IT infrastructure in retail does not allow to implement new processes and technologies fast enough, IT infrastructure in retail is highly fragmented and requires modernization, testing new technology solutions is time-consuming and requires a lot of resources.
Problem #4
Lack of forecast and planning accuracy leads to too much or not enough inventory at hand, resulting in lost sales due to understocking or higher than necessary inventory carrying costs due to overstocking.

Solution #1
OSA Hybrid Platform - an end-to-end AI-based solution ensuring on-shelf product availability and driving sales through process improvement across the entire supply chain.
Solution #2
Online Radar is an online tool for offline retail that allows to monitor KPIs and detect anomalies across the entire supply chain and all processes at the store level in real time.
Solution #3
Retail Cloud is an AI-based IT infrastructure for processing, structuring and enriching of raw data from various sources in order to make it ready for further use and integration with the third-party providers and services.
Solution #4
Promo Demand Forecast provides accurate forecast for promo sales, using machine learning algorithms. Promo Demand Forecast algorithms are fully integrated with OSA Hybrid Platform and Online Radar, taking advantage of the synergy and mutual enrichment of data between the key AIA services.

1. Competitors focusing on the same problem (product availability and out-of-shelf) and correcting the problem in real time:
Open (Mirum), Intelligence Retail, SoftServe, Visit Basis, RSi Retail, Simbe Robotics, Trax, ShelfMatch, Inspector Cloud, RI Team, Open (WoDo), Open (Smart shelves)

2. Competitors focusing on the same problem, but not correcting the problem or using delayed correction:
NextOrbit, Nielsen, BeMyEye, GoodsForecast, DataSeed, Bossanova, FieldAgent, IntraMarket, Lokad, CrowdSystem

3. Competitors with a wider focus, such as the store as a whole or the supply chain:
IBM, SAP, ABM Cloud, Oracle, 1C, Retail Services, Blue Yonder, TradeCheck, Repsly, CompliantIA

1. Low cost of implementation and scaling:
- Since OSA HP is an analytical (software solution based on data analysis) solution and doesn’t require installation of equipment or hardware, the cost of implementation and scaling is significantly lower than that of hardware-based solutions.
- OSA HP identifies the OOS issue with no personnel involvement, which makes it a cheaper solution than those where the issue is detected visually by store employees or merchandisers.

2. Real-time issue correction:
OSA HP solved product availability issues in real time (within an hour), which sets it apart from 55% of other analyzed solutions.

3. Issue correction and control of the correction within the solution contour:
OSA HP is unique in its issue correction approach: it doesn’t stop at alerting the store personnel of the potential availability problem, it also controls the fact and the quality of issue correction. No other solution offers this capability.

4. Support and correction of business process across the entire supply chain:
The majority of the solutions we analyzed stop at issuing OOS notifications/alerts to either retailer or manufacturer, but OSA HP goes much further, offering recommendations for issue prevention and optimization of business processes across the entire supply chain.

Revenue streams:
1. Revenue fr om providing basic and additional services to the CPG manufacturing clients.
2. Revenue fr om providing additional services to the retail partners.

Cost structure:
The pricing model is based on calculation, that takes into account the number of SKU (stock keeping units), check penetration, turnaround, as well as the size and the number of stores wh ere the client will receive the service. Based on this data, we determine the minimum subscription price for the manufacturer (step 1 in the chart above).
The final subscription price for CPG clients is determined through negotiations. Initially, the upper lim it, based on the expected ROI, is offered. For example, if the additional profit from using the service is expected to be 3 million rubles, the company offers a subscription price of 2.4 million rubles (expected ROI 25%).
For example, Danone’s 12-month contract for 2019 for services at 2,500 Dixy stores was 13.5 million rubles, Mars’ contract – 18.8 million rubles, Coca-Cola’s contract – 9.1 million rubles. As the number of stores increases, so can OSA’s revenue: the larger the number of stores, the larger the client’s additional profit.

Two revenue models are in use:
Subscription - for our manufacturing clients
Pay-for-performance - for our retail clients
Additionally, we use revenue share model for retailers who are willing to invest their effort and engage their suppliers into using our services.

Sales channels:
1. Support from ECR Russia in meeting potential clients.
2. The company’s own contacts.
3. Support from retail partners in selling the service to their business partners (suppliers and/or manufacturers).

1. Accelerating product and business development in Russia, Ukraine and Kazakhstan.
2. Expansion to the United States market.

Will be ready to share in the process of negotiations or during Q&A.

1. Long sales cycle.
2. Rapid changes in top management in retail.
3. With lack of buy-in from the individual store managers, task execution discipline can be low.
4. Growing share on e-commerce in grocery retail.
5. OSA may not be able to expand as aggressively as planned. Nevertheless the development in Russia is expected to stay on track.
6. The current stage of product development calls for verification of multiple hypotheses (pure venture). Active growth, the expansion of customer base and service functionality require significant investment, which puts break-even further away. With that said, the main product is fully functional, commercially successful, and has proven its effectiveness.
7. There is a potential risk that with the planned extent of the geographical expansion, the team is not yet distributed enough in the targeted geographies.

October-December 2020 - Global Pilots program (https://globalpilots.tech)

Protected trademarks:
1. OSA Hybrid Platform trademark
2. OSA Hybrid Platform software
3. OSA Hybrid Platform 2.0 software
4. OSA Hybrid Platform mobile application