GENETIC ALGORITHM AND OPTIMIZATION OF THE SALES ASSORTMENT STRUCTURE

The genetic algorithm belongs to a group of evolutionary algorithms that find inspiration in Darwin\u27s theory of maintaining the best species. It is based on only three operations: selections, crossover and mutations. The application space is found in all areas that require optimization by finding the values of the variables that optimize the target function. Each genetic algorithm therefore uses a fitness function in order to choose the best crossover units from the population in the next generation. The optimal structure of sales assortment is a theoretical and pragmatic challenge to the sales function in every market-oriented organizational system. In this paper, the optimization of the sales assortment structure is viewed as the task of determining the share of individual products in a group of products sold on a particular market. There is a hypothesis that the structure of the sales assortment can be optimized using the genetic algorithm. In the paper is developed software solution in C# to verify the main hypothesis and the solution is open to new extensions and demonstrates a satisfying application power

Softverska rješenja u marketing istraživanju za otkrivanje znanja u bazama podataka pomoću fuzzy klasteriranja

Knowledge discovery in databases is the process of identifying nove, valid, useful and ultimately understandable patterns in data stored in databases. Data mining is only a step in this process in charge to find patterns or models in data. There are many data mining algorithms for clustering. Clustering is unsupervised classification, the process of grouping the data into classes so that the data objects (examples) are similar to one and other within the same cluster and dissimilar to the objects in other clusters. In the paper is developed a conceptual model and program solution for clustering data stored in subject oriented data warehouse. Data warehouse and mining algorithms are integrated and this integration has shown satisfactory implementation power.Otkrivanje znanja u bazama podataka je proces identificiranja novih, validnih, korisnih i razumljivih paterna i modela iz podataka pohranjenih u bazama podataka. Data mining je samo jedan korak u tom procesu, a on ima zadatak pronaći i otkriti paterne i modele. Postoji više data mining algoritama za klasteriranje. Klasteriranje pripada nenadziranom učenju (unsupervised learning), a ono je postupak grupiranja podataka u klase tako da je sličnost najveća između podataka u jednoj klasi a razlika što veća u odnosu na podatke u drugoj klasi. U radu je razvijen konceptualni model integracije i odgovarajuće programsko rješenje, klasteriranja podataka pohranjenih u skladištu podataka. Rješenje je u marketing funkcijskom području a integracija skladišta podataka i data mining algoritma pokazuje zadovoljavajuću implementacijsku snagu

UMJETNA INTELIGENCIJA U ODREĐIVANJU MARKETINŠKE STRATEGIJE KUPACA

Artificial intelligence is a computer-based analytical process that tends to create computational systems which we would incline to be called intelligent. Expert systems are the most important part of the artificial intelligence from economic perspective. Expert systems attempt to mimic the human thought process including reasoning and optimization. “Knowledge” is represented by a set of “if-then” rules in a form of knowledge base. The results of artificial intelligence system implementation in refining marketing customer strategy based on five customer behaviour factors: revenues, profit margin, market share, liquidity, long term value, and retention probability are presented in the paper. Customer marketing strategy depends on the combination of the value of these five attributes. Expert system helps a marketer to “drill down” into data and identify the most loyal customers, separates the customers into groups, and plans the adequate marketing strategy. Expert system for determining adequate marketing customer strategy is developed using Visual Prolog programming language. Visual Prolog has shown satisfactory application and developing power.Umjetna inteligencija je na računalu temeljen analitički proces koji nastoji kreirati računalne sustave koje obično nazivamo inteligentni. Ekspertni sustavi su najvažniji dio umjetne inteligencije s ekonomskog aspekta. Oni pokušavaju oponašati čovjekov proces mišljenauključujući rasuđivanje i optimozaciju. Znanje se prikazuje u obliku skupa „if-then“ pravila u obliku baze znanja. Rezultati primjene sustava umjetne inteligencijeje oblikovanje marketing strategije prema kupcima pomoću pet faktora koji opisuju njihova ponašanja: prihodi, postotak razliie u cijeni, tržišni udjel, likvidnost, vrijednost kupca u dugom roku i vjerojatnost zadržavanja kupca. Marketing strategija prema kupcu ovisio kombinacijam vrijednosti tih atributa njihova ponašanja. Ekspertni sustavi pomažu „uroniti“ u podatke i identificirati najlojalnije kupce, klasificirati kupce u grupe, i planirati odgovarajuću marketing strategiju. Ekspertni sustav za određivanje adekvatne marketing strategije je razvijen uporabom programskog jezika Visual prolog. Visual Prolog je pokazao zadovoljavajuću primjensku i razvojnu moć

SOCIAL NETWORKS AS CHALLENGE FOR MARKETING INTELLIGENCE

Social networks are changing the way of connection and communication between people by increasing the amount of publicly available information and knowledge. People of similar professional backgrounds and occupations link to online communities to share information. This has a direct impact on what is one of the most difficult aspects of marketing intelligence "efficient and rapid collection and sharing of data and information". The aim of marketing intelligence is not only access data but manage them, analyze them and based on the analysis to make the right decisions related to customers, products, price, promotion, sale. Therefore, a large number of companies today are looking for solutions by marketing intelligence that will enable access to text data, analyze them and improve the quality of marketing decisions. The paper raises the hypothesis that it is possible to build a system for marketing intelligence that collects and analyzes data from social networks and uses the analysis results (information) to make precise, concise and accurate marketing decisions. In the paper is used the R programming language for marketing intelligence system and the R language demonstrated satisfactory simplicity and application power

A NEW FIVE-PARAMETER MODEL FOR PV PANELS-EXPERIMENTAL VALIDATION ON A POLYCRYSTALLINE MODULE

A new five-parameters model to describe the relation between the electric current and the voltage for a photovoltaic module is experimentally validated on the field, with variable conditions of operative temperature and solar irradiance. The electrical parameters of the one diode equivalent circuit are found by solving an equations system based on the data commonly issued by manufacturers in standard test conditions. To verify the capability of the new model to fit PV panel characteristics, the model was tested on two different panels comparing the results both with the data issued by manufacturers and with the results obtained using the five- parameters model already proposed by other Authors. The comparison shows that the new model is able to reproduce with very good precision the I-V curve issued by manufactures. Furthermore, the reliability of the proposed model was assessed performing an experimental validation connecting a PV panel to several different electrical resistances. The simultaneous measurement of the silicon temperature, air temperature, wind speed and direction, solar irradiance and voltage drop across the load, has permitted to verify a very good correspondence between the measured and the calculated data