Data mining in production management and manufacturing.

Matsi, Birthe ; Loun, Kaia ; Otto, Tauno 等

1. INTRODUCTION

Traditionally analysts have performed the task of extracting useful information from recorded data. But the increasing volume of data in modern business and science calls for computer-based approaches. As data sets have grown in size and complexity, there has been an unavoidable shift away from direct hands-on data analysis toward indirect, automatic data analysis using more complex and sophisticated tools. Nowadays the modern technology has made data collection as an almost effortless task. However, the captured data needs to be converted into information and knowledge to become useful. Data Mining (DM) is the entire process of applying computer-based methodology, including new techniques for knowledge discovery from data (Kantardzic, 2003).

DM is a general term, which encompasses a number of techniques to pick out useful information from large data files and enables to sort it. It is a new powerful technology for analyzing data from different perspectives and summarizing it into useful information. It has been also said that DM is the analyses of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner. Nowadays it is also very often known as Knowledge Discovery in Databases (KDD) or simply Knowledge Discovery (KD), which enables to identify trends within data that go beyond simple analysis and through the use of sophisticated algorithms; users have the ability to identify key attributes of business processes and target opportunities. DM is predicted to be "one of the most revolutionary developments of the next decade", according to the online technology magazine ZDNET News (February 8, 2001). In fact, the MIT Technology Review has chosen the DM as one of 10 emerging technologies that will change the world.

DM has been used widely by companies with a strong consumer focus like retail, financial, communication, and marketing organizations, where it enables to determine relationships among "internal" factors such as price, product positioning, and "external" factors such as economic indicators, competition, and customer demographics (Hand et al., 2001).

There are many different possibilities for using DM but the most frequent examples encompass mainly these applications:

* Rate customers by their propensity to respond to an offer.

* Identify cross-sell opportunities.

* Detect fraud and abuse in insurance and finance.

* Estimate probability of an illness re-occurrence or hospital re-admission.

* Isolate root causes of an outcome in clinical studies.

* Determine optimal sets of parameters for a production line operation.

* Predict peak load of a network.

[FIGURE 1 OMITTED]

The general Data Mining Process (DMP) includes six phases that address the main issues in DM (Lentzsch, 2007). All these phases fit together in cyclical process and cover the full DM process (see Fig 1).

In fact, the knowledge discovery activity could become the key factor to innovation and business success (Kusiak & Smith, 2007). Therefore DM could have a great potential also in manufacturing.

Data mining implementations in production management and manufacturing is the key question of this paper.

2. DM IMPLEMENTATION IN PRODUCTION MANAGEMENT AND MANUFACTURING

2.1 Data mining in manufacturing

Products and components generate a data trail across lifecycle phases such as market analysis, design engineering, manufacturing, and service. DM algorithms extract knowledge from this large volume of data leading to significant improvements in the next generation of products and services. Integrating a DM framework within the manufacturing information system enables to improve manufacturing decision making process and enhance the productivity. It enables to analyze enterprises opportunities and employee's skills and competences, find relations between enterprises, customers and subcontractors, and make consequences based on different data conjunctions.

In order to implement the DMP to the production management and manufacturing sector, it is important to understand the business problem--what we would like to do with all that data and also to understand what that data is all about. It is obvious that all data need to be in an easily accessible format and available from one central database. It is often the case that relevant data files are stored in several locations and in different formats and need to be pulled together before analyses. The extracted information and knowledge can assist the engineers as their reference and basis for advanced investigation of the root causes of the defects (Larose, 2005). In current case study is used data about the enterprises, their technological capabilities, and employees' competences from three different databases: Metnet, Innomet and Innoclus (see Fig 2). For data analyzing the data understanding and preparation is unavoidable phases in DMP. Also there is need to assess the data for the DM project. The several aspects which should be considered are the followings:

2.2 Relevant factors covered by data

For making a DM project worthwhile, it is important that the data contain all relevant factors/variables and is mutually joinable. Therefore the data was separated into different tables according to logical themes. For example, one table includes all information about the enterprises contacts, other enterprises technological capabilities, etc. It is smart to hold the data in different tables, because it simplify the data understanding and facilitate later the DMP. In order to joint the data between these thematically separated tables, the common identifications (ID-s, for example: enterprise_id, sector_id etc) were worked out. These ID-s are needed for connecting one enterprise data from different tables.

In the figure 2 is pointed out in which tables all the data were divided and from which primary source the data is available from.

2.3 Handling noisy data

The term "noisy" in DM refers usually to errors in data or also sometimes to missing data (Hastie et al., 2001). In this study that is the problem we have to handle. It results from the data collection. All data about the enterprises capabilities were gathered without multiple-choices. Initially the question options were not defined and therefore every enterprise answered to the questions differently. In order to understand the answers unambiguously, the data synchronization was unavoidable. This solution for employee's skills and competences and enterprise technological capabilities was simpler, as the multiple-choices were worked out and enterprises answered to the questions by doing the suitable selections.

2.4 Gathering enough data

It is obvious that the more complex patterns and relationships we would like to find with data mining, the more records required to find them. There is self-evident difference, when we are analyzing ten, hundred or all Estonian machinery enterprises. It is important to point out, that in our case study all machinery enterprises have been included and that information has been gathered and will be used in different analyses by DM.

3. DM ANALYSES

When the data will be gathered into one central database and also would be structured and therefore easily under-standable, we could use the DM in many different applications. We could build up the models, which would be able to predict different important indications for better and more effective production management. For example, it could be possible to create the predictive DM model for investigating the competences, which could be needed for most effective product development. In addition it could be possible to classify enterprises for different clusters based on different technological capabilities and etc. Therefore the DM implementation is also effective in manufacturing sector and certainly is necessary for improving enterprises productivity and innovation in product development and manufacturing.

[FIGURE 2 OMITTED]

4. CONCLUSIONS

Data mining is a powerful tool, needed when amounts of data increase rapidly. In addition, it could be used also for complex analysis at a country level in sector of machinery, metal and apparatus engineering. The implementation of DM could be useful for analyzing and updating existing databases in a process of development collaborative e-Manufacturing information system. In addition, its implementation could give the powerful effect for machinery enterprises productivity and innovation in product development and manufacturing. Therefore the future research is targeted to increase the proactivity of the system. If we add data feeds from embedded systems reporting technological capability, the DM is one of the most promising methods to handle the information thus increasing productivity of management and innovation in the collaboration network.

After the main database will be created (for example based on three existing databases: Metnet, Innomet and Innoclus) and "noisy" data will be eliminated. The aim is to use all these gathered data about the enterprises and those technological capabilities and employees' skills for making and experimenting some DM models in order to increase these enterprises productivity and innovation in product development. On the other hand DM could be also useful from product improvement and repair process improvement perspectives to be able to determine the most frequent repairs by product, the factors that contribute to a failure type, and the correlations between failures.

5. ACKNOWLEDGEMENTS

The work has been supported by Estonian Science Foundation grant G6795.

6. REFERENCES

Hand, D.; Mannila, H.; Smyth, P. (2001). Principles of Data Mining, MIT Press, Cambridge, MA.

Hastie, T.; Tibshirani, R. & Friedman, J. H. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.

Kantardzic, M. (2003). Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons.

Kusiak, A.; Smith, M. (2007). Data mining in design of products and production systems, Annual Reviews in Control, 31. 147-156 A.

Larose Daniel T. (2006). Data Mining Methods and Models. John Wiley & Sons Inc., United States of America.

Lentzsch, K. (2007). Introduction to Clementine and Data Mining. SPSS Inc.