Data mart framework for production route selection.
Sahno, Jevgeni ; Savimaa, Raul ; Kangilaski, Taivo 等
Abstract. Having an efficient business model can be achieved by
improving existing business processes and excellent Knowledge
Management. In our research it is suggested to use Enterprise
Architecture management approach to improve the effectiveness of
developed solution. We combine that framework with Knowledge Management
approach based on data mart structure and introduce a novel solution for
production route estimation.
Keywords: knowledge management, ERP, PDM, IS, SOM
1. INTRODUCTION
The aftermath of the global financial crisis has caused a tough
situation in enterprises, especially in the machinery sector, which has
forced to reorganize their business processes to be more efficient
(Polyanchikov et al., 2010). In most cases process improvement has been
followed by changes in their Information Systems (IS). The current paper
suggests using an Enterprise Architecture (EA) concept, which enables
changes of business processes combine with analysis of changes on IT
systems, data management, and infrastructure levels.
The aim of the current research is to support production activities
by sustaining the technological knowledge of the company. A
technological knowledge management (KM) approach is proposed that
enables to store, classify and extract item/project production route
data from a Data Mart (DM).
Enterprises which are dealing with large number of manufacturing
tasks can benefit from the proposed framework. Every project has a
specialized production technology and every item has its specific
production route. Often the technical documentation of projects is
stored in separate files, which could lead to difficulties in finding a
required document. Also majority of technological knowledge is stored in
the brains of the engineers. When the engineer decides to leave the
company, this knowledge is often lost from the enterprise.
The framework presented in this research combines Computer Aided
Design (CAD), Product Data Management (PDM), Enterprise Resource
Planning (ERP) systems, and Operation Data Store (ODS) systems. It
facilitates storing, searching and assessing existing production routes
in the DM.
2. BACKGROUND
EA is widely used by organizations to respond quickly to changes.
It is a rigorous description of the enterprise structure including
enterprise goals, business processes, roles, organizational structures
and behaviors, business knowledge, software applications, and computer
systems. EA is employed as a reference that enables the organization to
assess the impact of the changes on each of EA components.
In this research EA based approach provides a strong framework
which will help the planning, developing as well as implementing the
required business changes.
KM systems facilitate organizational learning and knowledge
creation. They are designed to provide a rapid feedback to decision
makers, encouraging behavioral changes in employees and improving
business performance. As the organizational learning process continues
and its knowledge expands, the knowledge-creating company works to
integrate its knowledge into its business processes, products and
services. This helps the company become a more innovative and agile
provider of high-quality products and customer services, as well as
formidable competitor in marketplace (O'Brien & Marakas, 2008).
KM refers to a multi-disciplined approach to achieving
organizational objectives by making the best use of knowledge. There are
various components in a decision-making environment, including
collection of data, storage of data, data analysis and knowledge
discovery (Shevtshenko et al., 2009).
A PDM system is a component of the IS for managing product related
data generated by CAD systems. This system allows maintaining Bill of
Materials (BOM), visibility of relationships between parts and
assemblies and quick access to standard items, BOM structure, and files
for reuse and derivation. A PDM system reduces the risk of using
incorrect design versions (Saaksvuori & Immonen, 2008). Integration
with a CAD/CAM system permits describing the BOM and the parts of
manufacturing process (Portjanski, et al., 2010).
The ERP system is integrated cross-functional software that
supports the management of basic business processes, improving
efficiency, agility, and profitability through the reengineering of
processes (De Geus, 1988).
3. KNOWLEDGE MANAGEMENT
This research reveals the importance of collecting, storing, and
sharing technological knowledge inside company and with partners. The
proposed framework can be applied by practitioners of manufacturing
companies from different fields of industry that produce physical goods.
The purpose of the KM is to retrieve either explicit or tacit
knowledge that resides within people, artifacts, or organizational
entities. Also, the knowledge might reside outside the organizational
boundaries, including consultants, competitors, customers and employees
of the organization. The KM process benefits most directly from two
sub-processes: externalization and internalization that help capture the
tacit knowledge and explicit knowledge, respectively. During
externalization, explicit knowledge (e.g. printed information or
databases) is converted into tacit (i.e. knowledge in mind that is used
for production of goods in manufacturing). Similarly, during
internalization, tacit knowledge (as employees' experiences) is
converted into explicit by formalizing it in a data warehouse or manuals
(Becerra-Fernandez et al., 2004).
We suggest following the steps described by the EA concept in the
Figure 1. The EA starts with analysis of business process, which is
required to enable a storage and reuse of the knowledge related to
production routes.
The second step is analysis of IS requirements. IS should permit
users to store the production routing data and facilitate a reuse of
this data when creating a new production route.
Step 3 consists of analysis of reporting requirements. Reporting
simplifies the process of optimal production route creation based on
criteria like time, cost and failure data.
The final step is analysis of infrastructure, where the store for
DM should be added to existing infrastructure.
[FIGURE 1 OMITTED]
4. DATA MART STRUCTURE FOR STORING TECHNOLOGICAL KNOWLEDGE
The current research is focused on the design of framework for
technological knowledge management for manufacturing enterprises.
Elaborated framework can be used for improvement of profitability and
sustainability of manufacturing processes in industrial enterprises and
collaborative networks.
Based on analysis and modeling we suggest that routing data storage
and search framework should be consist of following levels: Input data
level, Data storage level, Data analysis level, and Output data level
represented in the Figure 2.
Input data level. The CAD data is converged to the PDM system where
the product BOM is prepared. The received data from the PDM and also
from a CRM system is moved to Operational data store (Zahharov et al.,
2009). The enterprise data is managed by an ERP system. The integration
is made through a database which shares all functions and data
processing applications in the company.
Data storage level. The data from the Input data level is
consolidated in an Operational data store. The data necessary for
production route selection is replicated to a DM. We propose searching
for production routes through the ERP using data from the DM. This
implies a classification of products by item name, dimensions, weight,
material standards and other properties.
Data analysis level. In order to choose the most efficient
production route, we propose to use Kohonen's Self Organizing Maps
(SOM). SOM is constructed on criteria such as production route time,
cost, and faults that eventually assist in decision making procedure to
select the most cost-effective route.
[FIGURE 2 OMITTED]
Output data level. The most suitable production route is selected
and sent back to the ERP system for modifications.
This introduced novel framework is a specialized modification to EA
concept in all four steps of EAM. Preliminary modelling showed that the
framework is also suitable for virtual enterprises where processes,
information systems and infrastructure of participating organizations
are only partially and temporarily integrated to achieve common goals or
to produce a specific product.
5. CONCLUSIONS
Modern knowledge management techniques help companies to capture,
store and re-use large amount of explicit and tacit information in order
to improve their management decisions as well as production activities.
As the result of the current research, the paper suggested an EA
management approach and presented a novel unified framework of
technological KM systems. The framework enables to elaborate a new order
route for production, based on enterprise goals and existing information
from CAD, PDM, CRM and ERP systems by applying a DM approach. The future
research will departure form presented results and will concentrate on
further detailed specification of suggested solution for virtual
enterprises.
6. ACKNOWLEDGEMENTS
The research was partially financed by Estonian Science Foundation
grant ETF7693. Hereby we would like also thank the Estonian Ministry of
Education and Research for targeted financing scheme SF0140113As08,
European Social Fund's Doctoral Studies and Internalization
Programme DoRa and financial funding for doctoral study from
MIT-Pt/EDAM-IASC/0033/2008 project.
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