MANAGEMENT STRATEGY ELABORATION JAVA TOOL Edward Pogossian Academy of Sciences of Armenia, IPIA State Engineering University of Armenia

Optimal Management Strategy Provision Problem 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 1 st stage: Test our plans without competitions Given plans search of the best strategy The set of our perspective plans 2 nd 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 changesShow the carried out move Start the competition Main window System overall structure

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

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.

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.

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 changesShow the carried out move Start the competition Main 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 1 st stage: Test our plans without competitions Given plans search of the best strategy The set of our perspective plans 2 nd 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 \ 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 +ΔP 2, Quality +ΔQ 2 Price +ΔP 1, Quality+ΔQ 1 i.j k.l Price +ΔP 2, Quality+ΔQ 2 Price +ΔP 3, Quality+ΔQ 3 Price +ΔP 1, Quality+ΔQ 1 2.2m. n Price +ΔP 1, Quality+ΔQ Price +ΔP 2, Quality+ΔQ Price +ΔP 3, Quality+ΔQ A combination of our and competitors’ plans The tree generated for the Raise Price and Quality plan

Testing plans without competitions Yes No Yes 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 movesGet 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 Yes No Go to the next node Yes No Assess the generated tree and select the best strategy Testing our plans with competitions

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 marketing 2. - constructing an advisor that will recommend decisions for a company in its oligopoly competitions 3. - constructing a tool that allow to simulate different scenarios in oligopoly competitions to recommend the best one for a requesting company 4. - completing the1-3 tasks by a unit for strategies regular improvement and learning E - developing management skill measuring scale invariant to measuring human peculiarities

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