Optimal Management, LLC

Resident at Skolkovo Innovation Center

Participant in Platform Development Accelerator

for SAP HANA

Participant in IBM Global Entrepreneur

We use mathematical models and optimization

methods for management of enterprises

Optimization of internal supply chains for multinational companies that yields the largest after tax profit

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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.

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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?

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• 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

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• 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

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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

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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

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Adaptation of

mathematical model The model describes multistage nature of manufacturing process.

The model can be static or dynamic (including time).

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• 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

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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

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• 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:

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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

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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)

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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;

• …

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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

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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.

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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