MANAGEMENT STRATEGY ELABORATION JAVA TOOL

Edward Pogossian

epogossi@aua.am

Academy of Sciences of Armenia, IPIA

State Engineering University of Armenia

Optimal Management Strategy ProvisionProblem

  

A company is competing in oligopoly markets for some success criteria (max cumulative profit, max return on investment, etc.) and is going to make decisions in market situations that are consistent with the best strategy at least for k periods of the competition

The set of all plans

Plans allowable for the competitors

Strategy planning

Identification of the market situation

Identification of the competitors

1st stage: Test our plans without competitionsGiven plans search of the best strategy

The set of our perspective plans

2nd stage: Test our plans by the competitions

Test the plan by the competitions

Take a perspective plan

The Best Strategy Formation

Data input dialogs

New competition

Market description

Competitors description

Our company description

Dynamic changes dialogs

Market and competitors data changes

Competitors number changes

The game tree depth and assessment method changes

Carry out the selected strategic move

Select the best strategic move

The Best Strategy Formation

Show the competitors changes Show the market changes Show the carried out move

Start the competitionMain window

System overall structure

STATE OF THE ART

Strategy search in Artificial Intelligence:

- Botvinniks' method (Russia,1979)

Strategy search tools in business: - strategy planning tools (Rouse,USA, 1997)- strategy dynamic testing toolValue War (Chussil,Reibstein,USA, 1994)

- The method of local tournaments (Armenia, Pogossian,1979)

Strategy Provision Advisor for recommending decisions to a company in its oligopoly competitions

Internet Agents able to elaborate decisions for e-commerce, auctions, etc., to represent interests of owning them companies in

competitive environments

Standards for Management Skill Assessment –

a scale consistent with on-the-job performances of the managers and allowing to measure their skills by

standard means independent of human peculiarities.

Strategy Elaboration Skill Tutoring And Assessment Tool

for producing scalable strategies in oligopoly competition simulation games and making them regular

participants of the games for training of the users in development of valuable

strategies

URGENT PROBLEMS

Problem1: OPTIMAL MANAGEMENT STRATEGY PROVISION

A company is going to compete in oligopoly markets for some success criteria (e.g., max cumulative profit). Given market situation to find a decision that is consistent with the best strategy at least for k periods of the competition Application areas: 1. Optimal Management Strategy Provision In Oligopoly Competitions in Economics, Military actions, Games 2. Autonomous Agents Optimal Strategy Provision

In solving the MOSP two basic goals are targeted: - achieving an acceptable level of management decision making in business games, and

- constructing regular mechanisms for strategy improvement and learning.

 

The model must include, in particular, the following components:

- a proper market model,- a store for common and classified strategy planning knowledge – Strategy Planning Ontology (SPO), and syntax for their regular use,- a strategy search environment able to address to the SPO and, as a result, change its strategy search procedure,- an instrument for comparing the strategies and the selection of the best one as well as qualifying them on the management scales, - procedures causing guaranteed improvement of the strategies by records from the OSP. 

STATE OF THE ART

Strategy search in Artificial Intelligence:

- Botvinniks' method (Russia,1979)

Strategy search tools in business: - strategy planning tools (Rouse,USA, 1997)- strategy dynamic testing toolValue War (Chussil,Reibstein,USA, 1994)

- The method of local tournaments (Armenia, Pogossian,1979)

CORE DIFFICULTIES

- to use the Botvinnk's method:

it is necessary to represent the strategysearch space of the problem by a gametree where is possible to interpret thetrajectories of actions in the context oftheir positive or negative influence onthe intermediate goals of the solution

- to use the Value War:

- strategies are not reflectingadequately the variety developed inmanagement- no enough dynamic feedbackreflecting a change in the currentmarket situation- no means for the analysis of the matrixof grades

ALTERNATIVES ARE ALL STRATEGIES IN THE GAME TREE

STRATEGIES ARE CASE SOLUTION CHAINS COMPLETED BY GAME TREE SEARCH

COMMON PLANNING AND PLANS DYNAMIC TESTING STRATEGIES

STRATEGY EVOLUTION and LEARNING MODELS

In the Common Planning and plans Dynamic Testing (CPDT) model of the MOSP :

Common strategic planning knowledge is formed to narrow the search space followed by direct dynamic testing of the plans in the game tree.

It is supposed that knowledge in strategy planning is presented in corresponding ontology and the tree search is arranged by a procedure closed to the idea of Botvinnik’s method.

Java implementation of the model :

Oligopoly Planning And Competing Tool

The first version : OPACT1

It is worth to focus on the CPDT model of the MOSP because it

-provides an ability for regular improvement and learning of the strategies by injection of common knowledge and achievements from the management theory and methodology as well as individual experiences from the experts,

-is consistent with broadly recognized models of management, - is consistent with recommendations of an advanced strategy search Botvinnik’s method.

OUR APPROACHES AND PRINCIPALCONCEPTUAL FINDINGS

- To take the most advancedmanagement strategy formationmodels and try to simulate them

adequately

- To use appropriate constraints

Currently selected model:

Michael Porters' model (Harvard BusinessSchool) :

Essential questions the model asks : Where are we now? Where do we want to go?

How can we get there?

WHAT DID WE DO TILL NOW?

The Porter’s strategy search methodology recommendations are interpreted as the following:

1. Identification of the situation 2. Selection of the initial plan 3. Identification of the competitors 4. Specification of basic alternative strategy plans 5. Assessment and selection the best strategies

The first two are questions of strategy planning and the last question is related to strategy assessment.

The simulation: - a permanent structure of interconnections, data flow and interfaces - a system of knowledge bases

JAVA TOOL FOR OLIGOPOLY PLANNING AND

COMPETING

Edward Pogossian Academy of Sciences and State Engineering University of Armenia

epogossi@aua.am Robert Hovhannisyan

Maria Soukiassian State Engineering University of Armenia

Data input dialogs

New competition

Market description

Competitors description

Our company description

Dynamic changes dialogs

Market and competitors data changes

Competitors number changes

The game tree depth and assessment method changes

Carry out the selected strategic move

Select the best strategic move

The Best Strategy Formation

Show the competitors changes Show the market changes Show the carried out move

Start the competitionMain window

System overall structure

The set of all plans

Plans allowable for the competitors

Strategy planning

Identification of the market situation

Identification of the competitors

1st stage: Test our plans without competitionsGiven plans search of the best strategy

The set of our perspective plans

2nd stage: Test our plans by the competitions

Test the plan by the competitions

Take a perspective plan

The Best Strategy Formation

Fig. 1. All plans combinations in a competition

1.3

1.2

1.1

\

2.4

2.2

2.3

Market current situation

Example

Initially all combinations of possible strategic plans for our company and competitors are constructed (Fig. 1).

Then for each combination a game tree is generated, where all our strategic moves are assessed, based on the chosen strategic plan and all the combinations of competitors’ possible answer moves.Let’s see the combination i.j of strategic plans, where our plan is supposed to be Raise Price/ Raise Quality. For this case the tree shown on Fig. 3 will be generated.

A step of price and quality changing with “essential” responses of the competitors

Price +ΔP2, Quality +ΔQ2Price +ΔP1, Quality+ΔQ1

i.j

k.l1.31.2 2.41.1 2.1 2.2 2.3

Price +ΔP2, Quality+ΔQ2 Price +ΔP3, Quality+ΔQ3Price +ΔP1, Quality+ΔQ1

2.2m.n

1.21.1 3.23.1

Price +ΔP1, Quality+ΔQ1

1.21.1

Price +ΔP2, Quality+ΔQ2

2.22.1

Price +ΔP3, Quality+ΔQ3

3.23.1

A combination of our and competitors’ plans

The tree generated for the Raise Price and Quality plan

Testing plans without competitions

YesNo

NoYes

Get current node

Identify the state of our company

Generate nodes for each our move(competitors are not responding)

Go to the next node

Is it a depth of the search enough?

Go to the next level of the tree

The most promising plans selection by “independent” assessment

Get our possible moves

Identify the state of our company Identify the competitors

Get our possible moves Get all moves for the competitors

Get one of our not performed moves

Carry out our move along with all competitors’ move combinations

Yes

No

Get current node of the tree

Assess our moves and remove non perspective ones

Assess competitors’ moves and remove non perspective ones

Is a depth of the search enough?

Go to the next level

YesNo

Go to the next node

Yes

No

Assess the generated tree and select the best strategy

Testing our plans with competitions

Basic results

1. Strategic planning is carried out.

2. The best strategy is being selected as a result of a dynamic testing;

3. Dynamic testing of the strategies is carried out with the help of game tree;

4. Unlike other projects, such as ValueWar, MS2, here we automate the

decision-making process by the dynamic testing of strategies;

5. The program has flexible structure. At any point You can change:

Market and competitors description

The number of competitors

The game tree depth

The assessment method of the tree

6. The syntax of the Strategic Planning Block corresponds to the syntax of

ontologies. In future this will help to provide linkage with ontology-based

independent knowledge base

The utility of the OPACT is is evident, at least, in the following applications: 1.     - generating strategies for business simulation games with different and known strengths to make them regular participants in a teaching of marketing2.     - constructing an advisor that will recommend decisions for a company in its oligopoly competitions3.     - constructing a tool that allow to simulate different scenarios in oligopoly competitions to recommend the best one for a requesting company4.     - completing the1-3 tasks by a unit for strategies regular improvement and learning E - developing management skill measuring scale invariant to measuring human peculiarities

Technical parameters of the tool

1.The system has been written on JAVA – as an Applet;

2.Minimum System Requirements

CPU 350Mhz

RAM 64MB

3.System provides a friendly interface:

Menu

ToolBar

Dialogs

The following stages are planned:

1.Constructing OPACT1 able to form the best available strategies given market and strategy planning (SP) models, particularly:- acquiring oligopoly market model of an acceptable adequacy,- realizing basics of the Porter’s SP model, - given market and SP models developing methods for SPs dynamic testing and selecting the best decision, - experimenting with OPACT1 strategies in a marketing game to achieve an acceptable level of decision making, - modifying game tree search methods to achieve max available effectiveness given market and SP models,- determining OPACT1 strategies quality.

(continued)

2. Constructing OPACT 2 allowing to measure improvements of the strategies.

3. Constructing OPACT 3 consistent with the syntax of the strategy planning ontology (SPO) and involving SPO concepts in the strategy formation methods.

4. Experimenting with the OPACT3 to reveal means for strategies regular improvement / learning

Constructing OPACT 2

allowing to measure improvements of the strategies

The Quasi TransitivityConstraint:

It is possible to indicate a constant b(small enough compared to thenumber m of all strategies in theordering) such that for any i, 0 < i < m+1, all strategies located to the left ofthe segment [i+b, i-b] win the gamesagainst samples of competitors'strategies, which were won by thestrategy with location i ; thestrategies located to the right lose;the strategies within the segment canboth win and lose

Problem2: STANDARDS FOR MANAGEMENT SKILL ASSESSMENT Develop standard strategies for management skill assessment consistent with managers' ordering by their on the job performances Application areas: 1. Management Skill Assessment Centers 2.Personnel management 3. Learning Effectiveness Continuous Assessment:

Theorem:Given a class F of essentiallyimproved strategies andstrategies f and g from F, ifthere are b samples ofcompetitors' strategies suchthat f wins and g loses gamesagainst each of them, then f isstronger than g

CorollaryGiven a pair of strategies, f and g, itis possible to determine their inter-disposition in the global orderingusing only local, computationallyavailable resources