Abstract
Abstract: Nowadays, more and more enterprises are using Enterprise Resource Planning (EPR) systems that can also be used to plan and control the development of new products. In order to obtain a project schedule, certain parameters (e.g. duration) have to be specified in an ERP system. These parameters can be defined by the employees according to their knowledge, or can be estimated on the basis of data from previously completed projects. This paper investigates using an ERP database to identify those variables that have a significant influence on the duration of a project phase. In the paper, a model of knowledge discovery from an ERP database is proposed. The presented method contains four stages of the knowledge discovery process such as data selection, data transformation, data mining and interpretation of patterns in the context of new product development. Among data mining techniques, a fuzzy neural system is chosen to seek relationships on the basis of data from completed projects stored in an ERP system. Key words: knowledge management, new product development, knowledge discovery in databases, data mining, ERP system.
Abstract
In recent years, the advancement of information technology in business management processes has placed Enterprise Resource Planning (ERP) system as one of the most widely implemented business software in various enterprises. The use of an ERP system is especially significant in the production enterprises, in which a number of operational processes is enormous. ERP is a system for the seamless integration of all the information flowing through the company such as finances, accounting, human resources, supply chain and customer information [8] . The primary task of an integrated system is to maintain the data flow of an organisation and to reduce the redundancy [12, 16] .
Abstract
The present information and communication technologies have become one of the most important factors, conditions and chances of the company development. These technologies enable the collection, presentation, transfer, access and using of enormous amount of data. The data are a potential source of information that in connection with manager skills and experience may influence on the choice of the correct decision. ERP systems help to collect, operate and store data concerning daily activities of an enterprise (e.g. client orders), as well as the results of previous projects (development of products) [14, 24] .
Abstract
One of the functionalities of an ERP system concerns project management that a company can use to develop new products. Project management can be supported by knowledge management [9] , for instance in the context of information acquisition for project financing [13] .
Abstract
To obtain a project schedule, there is required data specification concerning resources and activities, including their sequence and duration. Project parameters can be specified by the experts or estimated with the use of an ERP database. First approach is suitable for the projects that have very unique form, e.g. for the construction projects. In turn, if an enterprise develops new products and a new version of product is connected with the superficial modification of a product specification, then it is possible to acquire the knowledge from the ERP database and to use it for the improvement of estimation quality of project parameters.
Abstract
The goal of this paper is to present the possibility of the use of the ERP database for seeking the relationships between the ERP attributes (e.g. delay of material delivery by suppliers, number of subcontractors, team members) and the project parameters (e.g. project duration).
Abstract
The sought relationships can support the user in the assessment of project parameters, and as a consequence to obtain the more relevant estimates. It is unrealistic to expect very accurate estimates of project effort because of the inherent uncertainty in development projects, and the complex and dynamic interaction of factors that influence on its development.
Introduction
In recent years, the advancement of information technology in business management processes has placed Enterprise Resource Planning (ERP) system as one of the most widely implemented business software in various enterprises. The use of an ERP system is especially significant in the production enterprises, in which a number of operational processes is enormous. ERP is a system for the seamless integration of all the information flowing through the company such as finances, accounting, human resources, supply chain and customer information [8] . The primary task of an integrated system is to maintain the data flow of an organisation and to reduce the redundancy [12, 16] .
Introduction
The present information and communication technologies have become one of the most important factors, conditions and chances of the company development. These technologies enable the collection, presentation, transfer, access and using of enormous amount of data. The data are a potential source of information that in connection with manager skills and experience may influence on the choice of the correct decision. ERP systems help to collect, operate and store data concerning daily activities of an enterprise (e.g. client orders), as well as the results of previous projects (development of products) [14, 24] .
Introduction
One of the functionalities of an ERP system concerns project management that a company can use to develop new products. Project management can be supported by knowledge management [9] , for instance in the context of information acquisition for project financing [13] .
Introduction
To obtain a project schedule, there is required data specification concerning resources and activities, including their sequence and duration. Project parameters can be specified by the experts or estimated with the use of an ERP database. First approach is suitable for the projects that have very unique form, e.g. for the construction projects. In turn, if an enterprise develops new products and a new version of product is connected with the superficial modification of a product specification, then it is possible to acquire the knowledge from the ERP database and to use it for the improvement of estimation quality of project parameters.
Introduction
The goal of this paper is to present the possibility of the use of the ERP database for seeking the relationships between the ERP attributes (e.g. delay of material delivery by suppliers, number of subcontractors, team members) and the project parameters (e.g. project duration).
Introduction
The sought relationships can support the user in the assessment of project parameters, and as a consequence to obtain the more relevant estimates. It is unrealistic to expect very accurate estimates of project effort because of the inherent uncertainty in development projects, and the complex and dynamic interaction of factors that influence on its development. However, even a small improvement in the estimation quality can influence positively on planning and monitoring the project, for instance, in project cost, resource allocation and schedule arrangement. This is the motivation to try to use the ERP database in order to discover the useful patterns.
2

Model of a knowledge discovery in databases
The knowledge discovery in databases (KDD) is concerned with the development of methods and techniques for making sense of data. KDD is the nontrivial process of identifying valid, novel, potentially useful and ultimately understandable patterns in data. The basic problem addressed by the KDD process is one of mapping low-level data (which are typically too voluminous to understand and digest easily) into other forms that might be more compact (e.g. a short report), more abstract (e.g. a descriptive approximation or model of the process that generated the data), or more useful (e.g. a predictive model for estimating the value of future cases). At the core of the process is the application of specific data-mining methods for pattern discovery and extraction [10] .
Model of a knowledge discovery in databases
One of the most used methodologies for developing data mining and knowledge discovery projects is CRoss-Industry Standard Process for Data Mining (CRISP-DM) and KDD process proposed by Fayyad et al. CRISP-DM states which tasks have to be carried out to complete a data mining project.
Model of a knowledge discovery in databases
This methodology consists of the following stages: business understanding, data understanding, data preparation, modelling, evaluation and deployment [15] . In turn, KDD process includes nine steps: developing and understanding of the application domain, creating a target data set, data cleaning and preprocessing, data reduction and projection, choosing the DM task, choosing the DM algorithm, DM, interpreting mined patterns and consolidating discovered knowledge [10] .
Model of a knowledge discovery in databases
The steps of the KDD process in the above-presented models can generally be grouped into four main tasks: data selection, data preprocessing, data mining, interpretation of patterns. Figure 1 presents the model of KDD process that is further considered in the context of knowledge acquisition from an ERP database.
Model of a knowledge discovery in databases
The data selection step is connected with an understanding of the application domain and the relevant prior knowledge and identification of the goal of the KDD process from the customer's viewpoint. This step also concerns creating a target data set: selecting a data set, or focusing on a subset of variables or data samples for the knowledge discovery. Second step -data preprocessing consists of data cleaning, preprocessing and reduction. Basic operations include removing noise if appropriate, collecting the necessary information to model or account for noise, deciding on strategies for handling missing data fields, accounting for time-sequence information and known changes, finding useful features to represent the data depending on the goal of the task.
Model of a knowledge discovery in databases
With dimensionality reduction or transformation methods, the effective number of variables under consideration can be reduced, or invariant representations for the data can be found [10] .
Model of a knowledge discovery in databases
Data mining step includes choosing the data mining algorithm(s), selecting method(s) to be used for searching for data patterns, and searching for patterns of interest in a particular representational form or a set of such representations, including classification rules or trees, regression and clustering. The user can significantly aid the data mining method by correctly performing the preceding steps.
Model of a knowledge discovery in databases
Fourth step consists of interpreting mined patterns and sometimes the visualisation of the extracted patterns and models or visualisation of the data given the extracted models. The discovered knowledge can be used directly, incorporating the knowledge into another system for further action, or simply documenting it and reporting it to interested parties. This step also includes checking for and resolving potential conflicts with previously believed (or extracted) knowledge. It is noteworthy that the KDD process can involve iterations and can contain loops between any two steps [10] .
Model of a knowledge discovery in databases
The knowledge acquisition from an ERP system requires using the KDD methods that are suitable for the characteristics of an ERP database. The proposed method for seeking relationships in the order of project parameter estimation is presented in the next section.
Method for project parameter estimation in the context of ERP systems
The presented method is dedicated for the production enterprises that use an ERP system also to develop the new products. The phases of product development depend on the characteristics of product and company, in which it is designed. However, some common phases can be distinguished, for example [1] :
Method for project parameter estimation in the context of ERP systems
plan and define programme, product design and development verification, process design and development verification, product and process validation, production.
Method for project parameter estimation in the context of ERP systems
These phases can also be considered in the context of concept initiation, programme approval, prototype, pilot and launch. Each phase requires the specification of duration and cost. In each of these phases, the critical factors (parameters of an ERP database) that significantly influence on new product development are sought. The estimation of these parameters is especially desired in the medium and large enterprises that develop a few new products simultaneously. In the case of significant variance of a project parameter, the use of the average or time series models can result in the inaccurate estimates. Thus, the search of conditional rules using an ERP database is proposed. The sought relationships can improve the quality of estimates that are input into an ERP system, in a project management module.
Method for project parameter estimation in the context of ERP systems
Among the KDD steps, two steps seem to be especially important in the context of knowledge acquisition from an ERP database, i.e. data selection and data mining. An ERP database contains dozens of attributes that can be irrelevant to the mining task or redundant. To reduce the data set size, attribute (feature) subset selection can be used that removes irrelevant or redundant attributes (or dimensions). The goal of attribute subset selection is to find a minimum set of attributes such that the resulting probability distribution of the data classes is as close as possible to the original distribution obtained using all attributes. Mining on a reduced set of attributes has an additional benefit. It reduces the number of attributes appearing in the discovered patterns, helping to make the patterns easier to understand [11] . One of the variable reduction methods is principal component analysis that reduces the dimension for linearly mapping high dimensional data onto a lower dimension with minimal loss of information.
Method for project parameter estimation in the context of ERP systems
Knowledge acquisition requires some data mining techniques that cope with the description of relationships among data and that solve the problems connected with e.g. classification, regression and clustering. These techniques include neural networks, fuzzy sets, rough sets, time series analysis, Bayesian networks, decision trees, evolutionary programming and genetic algorithms, Markov modelling, etc. Data mining should be connected with matching the goals of the KDD process from the user's viewpoint to a particular methods, for example, summarisation, classification, regression, clustering. Blind application of data mining methods can lead to the discovery of meaningless and invalid patterns [10] .
Method for project parameter estimation in the context of ERP systems
Database of an ERP system comprises an enormous number of parameters that can be considered as potential variables to identify the project parameters. One of the data mining techniques is fuzzy neural system that can take into account the imprecise character of data, cope with enormous amount of data, and identify the relationships among data.
Method for project parameter estimation in the context of ERP systems
Fuzzy logic and artificial neural networks are complementary technologies and powerful design techniques that can be used in the identification of patterns from among a large database and noisy data.
Method for project parameter estimation in the context of ERP systems
The fuzzy neural system has the advantages of both neural networks (e.g. learning abilities, optimisation abilities and connectionist structures) and fuzzy systems (simplicity of incorporating expert knowledge). As a result, it is possible to bring the low-level learning and computational power of neural networks into fuzzy systems and also high-level human like IF-THEN thinking and reasoning of fuzzy systems into neural networks.
Method for project parameter estimation in the context of ERP systems
The fuzzy neural method is rather a way to create a fuzzy model from data by some kind of learning method that is motivated by learning procedures used in neural networks. This substantially reduces development time and cost while improving the accuracy of the resulting fuzzy model. Being able to utilise a neural learning algorithm implies that a fuzzy system with linguistic information in its rule base can be updated or adapted using numerical information to gain an even greater advantage over a neural network that cannot make use of linguistic information and behaves as a black box [2] .
Method for project parameter estimation in the context of ERP systems
The behaviour of a fuzzy neural system can be represented by a set of humanly understandable rules or by a combination of localised basis functions associated with local models, making them an ideal framework to perform nonlinear predictive modelling. Nevertheless, one important consequence of this hybridisation between the representational aspect of fuzzy models and the learning mechanism of neural networks is the contrast between readability and performance of the resulting model [2] .
Method for project parameter estimation in the context of ERP systems
The combination of fuzzy systems and neural networks has recently become a popular approach in engineering fields for solving problems in control, identification, prediction, pattern recognition, etc. [6] [7] 26] . One well-known structure is the adaptive neuro-fuzzy inference system (ANFIS) that is a universal approximator and enables the non-linear modelling and forecasting [17] .
Illustrative example
The following example refers to four steps of knowledge discovery from an ERP database in the context of new product development. The output variable is the duration (in days) of the j-th phase in project i. In turn, the input variables of the j-th phase in project i are presented in Table 1 . These variables are derived from the ERP system modules that are connected with new product development (project management). The development of a product prototype requires the purchase of materials from the suppliers, storage of materials and usage of materials in production. The first step of the knowledge discovery process concerns data selection and it can be divided into two approaches: the expert chooses the data according to his/her experience and the use of one of the variable reduction methods. The input variables presented in Table 1 have been chosen according to the expert approach. All these variables have the numerical format, but different units, for example, purchasing is in monetary unit, delivery duration in days and productive capacity in percent.
Illustrative example
Therefore, the data requires transformation before one of the variable reduction methods is used. The principal component analysis has been chosen as the variable reduction method. This analysis transforms the input data so that the elements of the input vectors will be uncorrelated. In addition, the size of the input vectors may be reduced by retaining only those components that contribute more than a specified fraction of the total variation in the data set. After the use of principal component analysis, the data set has been reduced from 18 input variables to 5 (Delivery duration -DD, Number of work orders -NoWO, Number of standard tasks in the project phase -NoST, Number of unique tasks in the project phase -NoUT, Number of team members -NoTM). Moreover, the data set has been normalised so that it has a zero mean. Data preprocessing (transformation) is the second step of the knowledge discovery process and helps a fuzzy neural system obtain more accurate results.
Illustrative example
The third step of the knowledge discovery process regards to the use of data mining techniques/tools. In the order to seek relationships, the adaptive neurofuzzy inference system (ANFIS), which is the tool of Matlab ® software, has been applied. Figure 2 presents the structure of ANFIS for the duration of project prototype phase.
Illustrative example
In order to eliminate the overtraining of ANFIS (too strictly function adjustment to data) and to increase the estimation quality, the data set is divided into learning (P1-P12) and testing set (P13-P15). The learning phase requires the declaration of a membership function type of fuzzy sets (e.g. triangular, Gaussian function), defuzzification method, method of weights optimisation and stop criterion (e.g. error value or the number of iteration). ANFIS tool proposes two methods concerning the identification of the shape of membership functions: grid partition and subtractive clustering method. The shape of membership functions for input variables is presented in Fig. 3 . According to the shape of membership functions, the rules are built. The example of fuzzy rules for the duration is presented in Fig. 4 .
Illustrative example
After learning phase, the testing data are led to input of system to compare the error between different models. Root mean square errors (RMSE) for various models are presented in Table 2 . The least error in testing set for the duration of project prototype phase has been generated with the use of ANFIS with subtractive clustering method. The comparison of different models is especially recommended in the case of low level of variance for a dependant variable (in the considered case for the duration of a project phase). The membership functions and rules are a basis to evaluate the duration of an actual project. Let us assume that for the actual project are considered the following values: delivery duration -24 days, number of work orders -57, number of standard tasks in the project phase -11, number of unique tasks in the project phase -3, and number of team members -5. The presented analysis can be broadened into multidimensional analysis to support the decision-maker in determining optimal values of some parameters. For instance, the decision-maker would like to know the number of team members (from 5 to 6) and the number of work orders (from 55 to 65), for which the planned duration of project phase is the least. Table 3 presents the results for the above-mentioned constraints. The results indicate that the minimal duration is for 6 members of project team and 55 number of work orders. Moreover, the increase of project duration at 5 team members is proportionally larger than at 6 members For instance, the reduction of work orders from 60 to 55 for 5 team members decreases project duration by 9 days (131 days -122 days), whereas for 6 team members, the decline equals about 6 days (93.8 days -88 days). In the case of extensive search space, the time of obtained solution can be significantly reduced, e.g. using constraints programming languages [4-5, 18-21, 25] . The above-presented analysis is conducted for each phase of project and the obtained estimates can be used for further evaluation, e.g. in the planning of cash flow, working capital and financial reserves.
Closing remarks
The knowledge discovery process in the context of an ERP database can be considered as four steps: data selection, data transformation, data mining and pattern interpretation. An enormous number of data and attributes in an ERP database require paying attention to the proper choice of variable reduction and data mining methods. In the paper, the principal component analysis has been chosen as the variable reduction method and the fuzzy neural system as data mining technique.
Closing remarks
The fuzzy neural system has the advantages of both neural networks (e.g. learning abilities, optimisation abilities and connectionist structures) and fuzzy systems (simplicity of incorporating expert knowledge). As a result, it is possible to bring the learning and computational power of neural networks into fuzzy systems and also human like if-then thinking and reasoning of fuzzy systems into neural networks.
Closing remarks
The advantages of proposed approach include the search of conditional rules into an ERP database and using them for the project parameter estimation. This is especially important in the case of significant variance of a project parameter, for which the average and time series models result in the inaccurate estimates. More exact identification of project duration and cost enables more precision of project planning and control, as well as the improvement of cash flow planning. The proper choice of a set of new products can lead to increasing the market share, profitability and liquidity of an enterprise. The proposed approach is especially valuable for an enterprise that has a database of past projects, because there is the possibility to gather additional information in the form of conditional rules. The application of the proposed approach encounters some difficulties, among other things, by the collecting enough amounts of data of the past similar projects. Moreover, the lack of uniform rules that concern the development of fuzzy neural systems may cause an acceptance problem for the decision-makers. However, the presented approach seems to have the promising properties for acquiring information from an ERP system.
Closing remarks
Further research focuses on the development of the proposed method for project parameter estimation in the context of ERP systems, for instance, towards the choice of an optimal set of new products with the use of constraint programming techniques. The value of discovered patterns obviously depends on the quality of database of an ERP system.
Closing remarks
The data quality can be improved through using standards or methodology (according to the certification process) for managing of the project [22] , proper project communication [23] and the education approach in an organisation [3] .Hence, future research can be aimed at adjusting the proposed approach in the aspect of other project factors, including the improvement of training system in the company and communication in the project team.

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