Presentation of project at Skolkovo innovation centre.

1. Optimal Management, LLC
Resident at Skolkovo Innovation Center
Winner of Enterprise Applications & Big Data pitch
session at Startup Village 2013 in Skolkovo
Participant in Platform Development
Accelerator for SAP HANA
Participant in SAP Startup Focus Development Accelerator
Participant in IBM Global Entrepreneur
Participant in IBM PartnerWorld
We use mathematical models and optimization
methods for management of enterprises

2. 2
Problem
We solve a compelling challenge within Enterprise
Management:
• Optimization of internal supply chains for
multinational companies that yields the largest after
tax profit for the enterprise
To solve this problem we use heavy mathematical models,
new computational algorithms, Big Data tools and parallel
computing cluster power.
Our deep optimization can increase the profit of a
multinational enterprise to 5-10% and more.

3. 3
Problem
Optimization of Internal Supply Chains for
Multinational Companies
How to establish the most efficient Flow of Goods and Transfer Prices
if subsidiaries are in multiple countries?

4. 4
• Expansion of globalization leads to complex supply
chains
• Tax authorities increase tax requirements across the
world
• Current optimization techniques use methods of linear
programming and therefore are limited in scope
• Optimization calculations for a mid size company involve
millions of variables and several terabytes of data
Background of the
Problem

5. 5
• Currently, tasks of logistics optimization and tax
optimization are solved sequentially:
- At first Supply Chain planning systems calculate the best
Flow of Goods, providing minimization of costs;
- Later tax planning systems calculate the transfer prices,
providing the maximization of profit for global company;
- In both stages, tasks of linear programming are solved.
• The result of such sequential optimization does not
provide the most optimal solution. Only by optimizing
both - Flow of Goods and Transfer Prices simultaneously a
combination can be found that yields the most profit .
Current practice

6. 6
Our approach
Simultaneous solving of complex problem
Using Hadoop as Low Cost Super Computer
• Use math models of quadratic programing, new numerical
methods of optimization and optimal control theory
• Modeling and Optimization on SAP HANA and Hadoop
• Seamless integration with SAP Business Suite
SAP HANA
SAP ERP
SAP APO
Hadoop
Cluster

7. 7
Supply chain structure is constructed by taking into
account the multistage nature of manufacturing processes.
The Multinational Supply Chain includes:
• Countries (various tax jurisdictions)
• Internal and external suppliers
• Manufacturing plants
• Distribution centers
• Market zones
• Items of goods (including all raw materials, semi-
products, finished products)
• Available transportation routes
Supply chain
structure

8. 8
Adaptation of
mathematical model
The model describes multistage nature of manufacturing process.
The model can be static or dynamic (including time).

9. 9
• Simultaneous optimization of Good Flows and Transfer
Prices can increase the profit of multinational
enterprise up to 5% and more.
• The more advanced the supply chain (more goods
positions and more nodes in chain), the greater the
effect of simultaneous optimization of flow of goods
and transfer prices.
• Additional 2-4% of profit after deeper optimization of
internal supply chains by adapting mathematical
models that are capable of taking into account existing
price forecasts in various markets as well as differing
time-to-market characteristics between various chains.
Customer
Opportunity

10. 10
The following items are determined:
• Total maximum profit of Enterprise after taxation
as well as parameters that led to such result:
• Volume of each good that needs to be transported on
each route between subsidiaries participating in the
supply chain
• Transfer prices that need to be established between the
subsidiaries
• Allocation of transportation costs between seller and
buyer for each pair of participants in transportation
Optimization results

11. 11
• Maximal capacity of each node of supply chain
• Resource consumption of each node of supply chain while producing some product
• Procurement costs (excl. duties) of raw materials shipped from external suppliers
• Fixed and variable costs on each node of supply chain
• Amount of raw material needed on each plant to produce one unit of product
• Inventory cost of process loss and safety stock of each node of supply chain
• Transportation costs per unit of each product on each route
• Forecasted demand on each finished product in each market zone
• Market price of each finished product in each market zone
• Import and export duties
• Corporate tax rate of each country
• Lower and upper bounds of the transfer prices on each product between each pair
of countries
Static model
accounts for:

12. 12
Dynamic model is an extension of static model.
Dynamic model takes into account the following additional
aspects:
• Number of time intervals in common time period of modelling
• Delivery time on each transportation route
• Following parameters depending on time intervals:
– Forecasted prices and demand for each market zone
– Forecasted manufacturing costs
– Forecasted transportation costs
– Forecasted currency exchange rates
Additional results of optimization:
• Time of manufacturing start and shipping on each node of supply chain
• Forecasted sales figures for each market zone depending on time
Dynamic models

13. 13
Results
Type of model Model 1 Model 2
Number of manufacturing stages 1 1
Total number of suppliers 11 50
Number of internal suppliers 3 12
Number of manufacturing plants 3 8
Number of distribution centers 8 10
Number of market zones 20 80
Number of raw materials and components 10 35
Number of finished products 5 12
Effect of optimization 2,08% 4,90%
Results of optimization on test data (static model)

14. 14
Potential customers
Described problem is crucial for to the most industrial
companies that run subsidiary business units and competing
for the customers on global level. Most of the large business
companies as well as the higher level of middle business
companies fit these criteria.
Our potential clients can be from various industries:
• Oil and gas;
• Ferrous and non-ferrous metallurgy;
• Chemistry and petro chemistry;
• Production of building materials;
• Food industry;
• Consumer products industry;
• Pharmaceutics and bioengineering;
• …

15. 15
Team
• Experienced CEO with years of executive experience
• World renowned CSO
• 2 Dr.Sc and 2 Ph.D
• 200+ publications on Optimal Control and Optimization
• SAP guru consultants on EAM and SCM
• Experienced Project Managers and Architects
• International experience
• Combination of silver maturity and enthusiasm of youth
• Attracting most talented students

16. 16
Solution as a
service
While product is developing, we offer solution as a service.
The entire service process consists of following steps:
• Gathering of basic structure of customer’s supply chain
• Estimation of costs on calculation and full data gathering
• Approval for parameters taking into account
• Fitting of the math model to the client
• Gathering all necessary data for the developed model
• Transformation of gathered data into computational model
• Performing the calculation
• Applying of results
By analogy to the tasks of logistics optimization, static model
calculates for 18 months ahead every 6 months.
Dynamic model calculates weekly or monthly.

17. 17
Contacts
Contact persons:
• In USA & Great Britain – Vitaliy Baklikov
phone: +1 240 620 1229
e-mail: vitaliy.baklikov@optimalmngmnt.com
• In Russia & CIS – Andrey Sukhobokov
phone: +7 903 577 9667
e-mail: andrey.sukhobokov@optimalmngmnt.com