Product Knowledge Management in Small Manufacturing Enterprises

Product Knowledge Management in Small Manufacturing Enterprises

Giulia Bruno (Politecnico di Torino, Italy)
DOI: 10.4018/978-1-5225-1642-2.ch008
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Abstract

An important issue present in the most of manufacturing systems, and become worse in SMEs, is the systematic management of the huge amount of unstructured information generated about products, from their design to their disposal. The aim of this chapter is to define a framework to manage such kind of data, overcoming the actual issues of the meaningless and the unstructured nature of generated information. To this aim, a knowledge management platform is proposed, both to store product information with semantic enrichment and to retrieve product information by means of a new similarity index. Such platform is based on the one hand on a non-relational data management system and on the other hand on a set of manufacturing ontologies. An example of the potentiality of the proposed framework is shown in the domain of telecommunication filter manufacturing.
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Introduction

Today, embedded technology, real time collaboration, intelligence and connectivity are enabling the evolution to a seamless and agile manufacturing ecosystem, giving origin to the fourth industrial revolution. In such globalized and interconnected system, information is considered as an important production factor as capital, human resources and material (Sandkuhl, 2009). Enterprises have acquired various tools that support them to imagine, design and manufacture their products (Laroche et al. 2012). However, with knowledge and know-how contained inside different systems, it is very difficult to find the right information. The percentage of knowledge available in a structured and reusable format is very low in companies (less than 5%) and the rest is either unstructured or resides in people minds (Rasmus, 2002). Furthermore, Lynn et al. (2000) found that a storage and retrieval systems for technological information is a key factor that impact a team’s ability to acquire and use knowledge to reduce cycle time and improve the probability of success. Thus, now the main challenge is no longer to guarantee the existence of the information, but rather to find and provide the right information on time for a given purpose (Nadoveza & Kiritsis, 2014).

Having at disposal a huge amount of data coming from heterogeneous systems, the task of finding the right information is very difficult: studies revealed that 39% of all business executives spend more than 2 hours per day in searching for the right information (Delphi Group, 2002). Furthermore, even if digital systems are used daily to design, develop, produce, deliver and support products, the wide range of systems used has created the landscape of “isolated islands of information” where information is locked in different repositories making it difficult to share (Madenas et al. 2014). Among these systems, Enterprise Resource Planning (ERP) is a general framework for information processing, due to its ability to process and organize transactions, and build decision support applications on them (Caplinskas et al. 2012; Lupeikiene et al. 2014). However, ERP systems often lack flexibility and are not used by small enterprises.

In recent years, various forms of virtual collaboration have grown, in which the organisations try to exploit the facilities of the network to achieve higher utilisation of their resources (Jardim-Goncalves et al. 2014). The big idea is that this scenario will allow a self-controlling production process, in which production reacts autonomously to changes or faults and takes appropriate measures. This will bring systems engineering, production IT, and business systems to a new level – leveraging business benefits from an increasingly integrated product lifecycle management (PLM).

The aim of this chapter is to propose a method to organize the data available in manufacturing enterprises thus transforming data in information, and then structuring it in order to allow its easier finding and reuse, overcoming the actual issues of the meaningless and the unstructured nature of generated information. To this aim, a knowledge management platform is proposed, which is based on the one hand on a non-relational data management system and on the other hand on a set of manufacturing ontologies. The modular structure of the proposed framework allow its easy extension depending on the specific industrial domain. The use of the ontologies also allow the exploitation of a new similarity index to compute the similarity among products. The potentiality of the approach is shown in a case study.

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