Knowledge Management in the Automobile Industry

Knowledge Systems at Daimler AG

By Ashraf Abdo, Melanie Pittumbur and Niklas Kolbe

Université de Lorraine, 06/11/2014

PERCCOM

Contents 1 Introduction ........................................................................................................................ 2

2 Knowledge management in the automobile industry ........................................................... 2

2.1 Importance of knowledge management ....................................................................... 3

2.2 Knowledge management needs ................................................................................... 4

2.3 Main implementation strategies .................................................................................... 5

3 Knowledge management at Daimler AG ............................................................................. 5

3.1 Knowledge discovery systems ..................................................................................... 5

3.1.1 Knowledge Discovery in Databases ...................................................................... 6

3.1.2 CRISP-DM............................................................................................................. 6

3.1.3 KDD-applications developed by Daimler ................................................................ 6

3.2 Knowledge sharing systems......................................................................................... 6

3.2.1 Daimler AG Tech Clubs ......................................................................................... 6

3.2.2 Daimler EBOK ....................................................................................................... 7

3.2.3 Other initiatives of Daimler in Knowledge Sharing systems ................................... 7

3.3 Knowledge capture systems ........................................................................................ 8

3.3.1 Learning Bays Method in Daimler .......................................................................... 8

3.4 Knowledge application systems ................................................................................... 8

3.4.1 Cased-based reasoning system for KDD projects .................................................. 8

3.4.2 Other knowledge application systems ...................................................................10

4 Performance benefits of knowledge management .............................................................10

Bibliography .........................................................................................................................12

1 Introduction According to Britannica Encyclopedia, Automotive industry is: “all those companies and

activities involved in the manufacture of motor vehicles, including most components, such as

engines and bodies, but excluding tires, batteries, and fuel.”

The International Organization of Motor Vehicle Manufacturers or (OICA) stated that the

global vehicles production worldwide reached in 2013 87,299,993 vehicles (OICA O. , 2013

Production Statistics). This number means that the automotive market is very huge and

important market globally.

Except at the times of the economic crisis in 2008, the automotive market has always

witnessed a fast growth. For example, the following numbers show the growth in demand for

vehicles globally in the past four years:

2009 to 2010 +26%

2010 to 2011 +3.1%

2011 to 2012 +5.3%

2012 to 2013 +3.7% (OICA O. , Vehicles Production Statistics, 2014)

Automobile sector is one of the key segments of the modern economy having extensive

forward and backward linkages with other key segments of the economy. That forces the

reaction of automotive industry on market changes to be very flexible.

The automobile industry is a major innovator and the ongoing rapid technological changes

affect the automotive industry in many aspects. These changes varies from the

advancements of source of energy used to power vehicles, the increase of implementing ICT

tools and embedded systems in modern vehicles , the advancements in techniques used in

production lines and assembly, state of the art modeling and simulation tools and many

others factors.

For example, Auto market witnessed recently introducing different type of fully electric, hybrid

or even hydrogen powered car which meant that in order for companies to stay competitive,

they have to introduce and adopt technologies fast. Because consumers, as well as different

regions of the world, favour different technologies, automakers are developing a range of

automobiles.

The top five countries which lead the automotive industry in terms of production numbers are

China, the United States, Japan, Germany and South Korea (OICA O. , 2013 Production

Statistics). Whereas the top five production leader companies are Toyota, General Motors,

Volkswagen, Hyundai and Ford (OICA, 2013).

2 Knowledge management in the automobile industry Over the past decades, knowledge management has become one of the primary concerns in

managerial practice for establishing a sustainable competitive edge of a company. It has

even been argued that for modern businesses, the most important resource has become “the

collective knowledge residing inside the minds of an organisation’s employees, customers

and vendors” (Becerra-Fernandez & Sabherwal, 2010).

When considering all the industrial sectors, there is no doubt that the automotive industry is

the most striking example when referring to the complexity of management processes that

drive the need for a knowledge management system. It was always widely recognised that

the knowledge possessed by staff at every level of the production line was important for the

final productivity.

2.1 Importance of knowledge management

The rapid changes in the ICT sector has brought down the communication barrier in the

worldwide and has made instant communication and sharing of knowledge possible

independent of physical location. Globalisation, fuelled by this growth in the ICT sector, has

led to the creation of new markets in emerging countries and as a consequence to the deep

restructuration of supply chains further leading to the outsourcing of services and

businesses. Moreover the constant development and improvement in technologies has

changed the market trends and created the need for faster product development.

For the auto industry, all the major leading companies have most of their activities out-

sourced throughout the world. Also the demanding and competitive nature of the automotive

market has led to the merging of several companies. These new organisational structures

can only increase the complexity of accomplishing a design, manufacturing, assembling or

business process.

Moreover the process of new product development no longer requires only brainstorming

sessions by design engineers or product engineers but also requires the participation of all

staff at all levels of the organisation as well as other external partners such as the suppliers,

customers, equipment manufacturers and other automotive experts from other organisations.

For instance Toyota has come up with a new global business model called “learn local, act

global” (Kazuo, 2007) which takes advantages of the local tacit knowledge available in their

foreign subsidiaries so as to adapt better and faster to the local market needs.

Market volatility is a representation of the demand for automobile sales which has a direct

impact on the business, production and management processes of the companies. Thus it

has direct impacts on the cost of the purchasing, production and operating businesses of car

manufacturers as well as their product development processes. For instance due to the world

economic downturn in 2008, the demand for passenger cars equipped with advanced

electronic systems had suddenly dropped whereas the demand for economy, fuel-efficient

and environmental-friendly car designs had shot up.

Due to the highly competitive and volatile nature of the automobile industry, this sector has a

very high rate of employee turnover as companies are constantly downsizing or merging with

other companies. A great amount of knowledge is lost when a company loses its

experienced and technical employees. Besides the knowledge possessed by these

employees is actually useful for competitors and a lot of resources is later spent to retrain a

new employee. The loss of knowledge incurred by employee turnover inevitably has an effect

on the product development and innovation capability of the company. For instance in the

1980s, car American company Chrysler suffered from the loss of two important repositories

of knowledge: “Technical Reports” and “Chrysler Institute of Engineering” which led to many

problems such as inferior production quality, costly mistakes in new designs of cars, lack of

teamwork and loss in innovative ability. “It was as if Chrysler was forgetting its own solutions

and procedures on how to build cars” (Jacobson Al, 2007).

For all the reasons mentioned above Wikinvest defines the global automotive industry as

highly complex, competitive and volatile (Toyota Motor Corporation, 2007) where the need

for knowledge management is imperative to help in the production process, to keep the

innovative ability of the organisation, to be able to transfer knowledge across the spread

organisation and to preserve the knowledge asset despite high employee turnover.

2.2 Knowledge management needs

In order for knowledge management to bring solutions to the automotive industry to face the

problems it faces as explained in the previous sections, certain knowledge management

mechanisms have to be developed and deployed so as to enable the flow, discovery and

capture of knowledge within the organisation. These mechanisms directly involve the

organisation culture, employees’ behaviour and actions as well as the company’s actual

physical infrastructure.

It is important for the organisation to develop a culture that encourages, rewards and

recognises the practices of knowledge sharing, creation/discovery and capture. Also

exclusive company time should be given for weekly or monthly activities such as informal

meetings between factory engineers and technicians so as to discuss best practices on the

production floor.

The organisation structure that encompasses the hierarchical structure need to encourage

knowledge management mechanisms. For instance activities such as communities of

practice, formal or vocational training or on-the-job socialisation for knowledge sharing and

discovery purposes are important to pass on tacit knowledge from one employee to the

other. At DaimlerChrysler, later known as Daimler AG, the creation of an informal “Tech

Club” composed of engineers coming from the eight different engineering departments was

achieved. The aim of this community of practice was to exchange best practices and lessons

learnt. Also each department was given the responsibility of maintaining a part of an

“Engineering Book of Knowledge” that is shared with different teams.

The information technology structure existing in the organisation should provide the means

for data processing, storage and communication that are required for by the knowledge

management system. The three main components of the ICT structure should incorporate:

- Databases and database management systems to hold information on which data mining

processes can later be applied to.

-Communication and electronic messaging means to transfer or retrieve data instantly and

remotely.

- Internet or intranet network platforms to allow the remote and secure access to the

knowledge resources.

In order for knowledge management to be effective in the automotive industry, a common

language is required for financial and business staff, suppliers, manufacturers and engineers

from local or international subsidiaries to participate together in the process of knowledge

sharing, discovery and capture for the product development purpose.

For knowledge management to be pervasive within the organisation, it is sometimes a

definite advantage to bring about apparent changes to the physical structure of the

organisation so that knowledge management does not remain an abstract managerial in the

organisation. To encourage on-the-job socialisation and informal meetings, the availability of

meeting rooms and open spaces represents an incentive. Also a job position and office for

the Chief Knowledge Officer is a clear highlight of the importance of knowledge

management. Also it should be encouraged for leading car manufacturers to have a built-in

structure or workshop or even university for the formal and vocational training of future or

current employees.

2.3 Main implementation strategies

Some of the most useful and effective strategies to implement knowledge management for

an automotive industry include most of the following points discussed below:

1) The first step is to identify the sources of knowledge within the company and to convert

this knowledge into accessible form. This knowledge includes the technological-based

expertise and knowhow related to the automotive industry and customer and supplier

focused knowledge. All that is unique to a company and thus provide it with its

competitive edge over other companies.

Also an automotive organisation already possesses a vast amount of existing explicit

knowledge that may not yet be accessible across departments. This includes: customers

reports in customer service departments, CAD/CAM diagrams and software in design

department, suppliers specifications and information in accounting department and test

reports and troubleshooting guides in maintenance department.

2) Efficient and successful knowledge management requires an ICT infrastructure to

implement mechanisms and tools for sharing, capturing and retrieving knowledge make

them securely accessible and transferrable. For instance to support certain activities such

as communities of practice, building of book of knowledge or remote collaborative work

with customers and manufacturers in the global automotive industry, the ICT

infrastructure has to provide reliable tools to achieve these activities.

3) Automating some processes and controls of the automotive production line through the

implementation of mechanisms for knowledge applications such as knowledge rules and

procedures can provide more time for knowledge discovery and creation. For instance a

database of lessons learnt and best practices can save enormous resources when

solving problems or creating a new product.

4) Once the knowledge management systems has been implementing it is also important to

benchmark the performance of the mechanisms and tools put into place and relate them

to the resulting productivity, performance and competitiveness of the company overall or

of a specific process (Skyrme J. David, 1999).

3 Knowledge management at Daimler AG In this chapter the knowledge managements systems that were implemented by Daimler AG,

a German automotive corporation, in order to meet the needs described above will be

presented.

3.1 Knowledge discovery systems

Knowledge discovery systems are systems that help to create new tacit or explicit

knowledge. This can either be developed from data and information or from synthesis of

knowledge. In this section the knowledge discovery systems that are used by Daimler will be

described.

3.1.1 Knowledge Discovery in Databases

Knowledge Discovery in Databases (KDD) is intensively applied at Daimler AG. It describes

the development of methods to extract useful knowledge from data. It mainly relies on data

mining and includes technologies like statistics, databases, pattern recognition, machine

learning, data visualization, optimization and high-performance computing (Fayyad, 1997, p.

1).

3.1.2 CRISP-DM

The Cross Industry Standard Process for Data Mining (CRISP-DM) is a process model for

data mining and thus part of KDD. Data mining means discovering patterns in large data

sets. CRISP-DM was proposed by a consortium which included Daimler in 1996 (Kurgan &

Musilek, 2006, p. 5).

The defined process consists of six phases. The first phase is called business understanding

its goal is to develop the requirements from a business perspective. In the second phase,

data understanding, initial data will be collected. In the data preparation phase the final

dataset will be constructed. These have to be modeled, the fourth phase, and the model has

to be evaluated to ensure a high quality from the data analysis perspective. Finally the new

knowledge has to be organized and presented, which happens in the deployment phase

(Chapman, Clinton, & Kerber, 2000, pp. 10-11).

3.1.3 KDD-applications developed by Daimler

The Daimler AG has an Information Mining Department which is responsible for KDD tasks.

One KDD-application developed is the system called WAPS that is able to predict warranty

and goodwill costs. It is based on the QUIS database of Daimler where historical data of

warranty costs is stored. The system was developed in a joint activity from experts of the

Research & Technology and the Sales & Service department (Hotz, Grimmer, &

Nakhaeizadeh, 2001, pp. 1-2).

Another example for a KDD-application is REVI-MINDER. It detects and analyzes deviations

in warranty and goodwill cost statements (Hotz, Grimmer, & Nakhaeizadeh, 2001, p. 4).

3.2 Knowledge sharing systems

Knowledge sharing systems can be described as systems that enable members of

an organization to acquire tacit and explicit knowledge from each other (Irma Becerra-

Fernandez, 2010).

Over time, knowledge “islands” developed within Daimler, areas where Knowledge

Management practices had been applied, but in isolation from other parts of the company.

This made it a necessity to share the knowledge gained by those different “island”, several

initiatives have taken a place in Daimler AG to create Knowledge Sharing Systems that will

be explained in this section.

3.2.1 Daimler AG Tech Clubs

Communities of practice or as they’re called in Daimler “Tech Clubs” are informal cross-

platform clubs that facilitated interaction between engineers and designers working on similar

problems in advanced engineering, body, chassis, electrical, energy management, interior,

powertrain, program management, scientific labs, thermal, and vehicle development. The

clubs, which were sponsored by a corporate vice president, held informal meetings and

exchanged best practices (Michael G . Rukstad, 2001).

3.2.2 Daimler EBOK

EBOK or (Engineering Book Of Knowledge) is briefly, a system that provides best practice

information on almost every issue related to manufacture of cars. Or as Efraim Turban define

it in his book Information Technology for Management: The Engineering Book of Knowledge

(EBOK) is a knowledge database. It contains the best practices contributed by 5000 users.

The EBOK is divided into 3800 chapters. It contains both internal and competitors

information. It is a work in progress and will continue to accumulate new additions,

modifications, corrections and the results of ongoing research and development work

(Turban, 2006).

The EBOK was devised to capture and distribute the knowledge generated by the subject

matter experts in Tech clubs. Daimler decided to create the engineering book of knowledge

containing a bucket of knowledge such as test data, CAD/CAM drawings and data from

transaction of purchase, inspection and so on (Jawadekar, 2011).

EBOK of Daimler is a living real time online repository of knowledge for engineers to refer,

exploit for deployment, and share in collaborative spirit. Knowledge is explored and entered

into the EBOK through an iterative team approach: the Tech Clubs. Best practices are

identified, refined, confirmed, and finally entered into the EBOK in a secure interactive

electronic repository. When an author proposes a best practice, users in the Tech Club

responsible for that area of knowledge react by commenting on the knowledge through a

discussion list. One manager, the Book Owner, is ultimately responsible for approving new

entries and changes to the book. The Book Owner joins the conversation. The author can

respond to the comments by either building a better case or going along with the discussion.

Ultimately the Tech Club decides, and the Book Owner enters the new knowledge. The Book

Owner is the individual who is ultimately responsible for the accuracy of the book, and

therefore approves entries to, modifications to, and deletions from the book (Turban, 2006).

Tech Club members keep the EBOK up to date and highly productive. The EBOK comprises

several books devoted to different key areas of knowledge and problems and solutions. The

EBOK also provides views of experts and peers for using a particular knowledge entity

(Jawadekar, 2011).

3.2.3 Other initiatives of Daimler in Knowledge Sharing systems

Daimler AG developed a Worldwide Intranet system (Wel-Kom) to transfer knowledge from

one unit to another. The company also deployed a “Post Merger Integration Program” to

transfer knowledge and share information across the firm’s locations (e.g., US, and

Germany) (Cuffe).

Figure 1 - Daimler Tech-Clubs Structure

3.3 Knowledge capture systems

Knowledge capture systems support the process of eliciting either explicit or tacit knowledge

that may reside in people artifacts or organizational entities. (Irma Becerra-Fernandez,

2010).These systems rely on mechanisms and technologies that support externalization and

internalization.

Daimler AG has created a method of on-the-job training that has been later adopted by a lot

of German companies. This method is called Learning bays.

Daimler had a tradition knowledge sharing mechanism of vocational apprenticeship for new

employees. It thus had an in-built structure for exchange of tacit knowledge from one

generation to the other.

3.3.1 Learning Bays Method in Daimler

The Learning bays concept arose from a decentralized learning project in two Daimler plants

(Millward, 2005). The learning bay has a double infrastructure of a normal workplace

(Including all necessary resources) and a learning zone (comprising a range of learning

facilities).

In DaimlerChrysler, a group of four to six apprentices spend about six week in a one learning

bay during the last 18 months of a three and half year apprenticeship. Apprentices do the

same work as the skilled workers but in a controlled environment with a trainer. Each

apprentice job rotates within each learning bay so that they acquire competence on all key

tasks including team leadership.

The planning, doing and checking of assignment are performed by the group collectively on

both content and process (including personal attitudes) level. The trainer creates a semi-

autonomous learning environment to encourage self-managed problem-oriented learning.

The learning bay can also be a site for initiating innovation of new work processes and

arrangements. The theoretical basis of the concept of learning bay builds on the principles of

experiential learning. (Millward, 2005).

3.4 Knowledge application systems

Knowledge application systems utilize knowledge that was previously gained. With the help

of this kind of systems people don't have to actually acquire the knowledge in order to gain

the benefit of reutilizing it. In the following, knowledge application systems, their mechanisms

and their technologies that were developed and applied by the Daimler AG will be described.

3.4.1 Cased-based reasoning system for KDD projects

Daimler AG faced the problem of retrieving fast useful experiences from successfully

completed KDD-projects for new problems. This was the case because project teams in this

domain existed only temporary and time and resource issues during the projects didn't allow

a proper research and documentation of knowledge (Bartlmae & Riemenschneider, 2000, p.

1).

In order to compensate this problem, Daimler created a framework that is based on case-

based reasoning but also considers the organizational structure and responsibilities

regarding knowledge management tasks by adopting the experience factory approach and

knowledge building blocks.

Case-based reasoning

Case-based reasoning (CBR) is the process of solving a new problem by looking for a similar

problem that is already solved and reusing its solution by adjusting it to the new context.

Context-based reasoning consists of the steps retrieval, reuse, revise and retain of cases. A

case is a set of attributes that describe the problem and its solution. Cases are stored in a

case base. Additionally, the process relies on knowledge of how to select, interpret and

transform a case, also called CBR-system design (Bartlmae & Riemenschneider, 2000, pp.

4-5).

Experience factory organization

The experience factory as an organizational approach is based on the idea that collecting

experiences improves development processes. In this organization the collection of

experiences (project-teams) and the creation of experiences (experience factory) are

separated. The experience factory collects, structures, saves and retrieves experiences. This

is done by experience engineers, the experience factory manager and supporting agents as

they interact with the experience base where experiences are stored as experience

packages (Bartlmae & Riemenschneider, 2000, p. 2).

The following figure visualizes the different units of the experience factory organization

including the department management.

Figure 2 - Experience factory organization (Bartlmae & Riemenschneider, 2000, p. 2)

Responsibilities regarding knowledge management

Daimlers aim was to combine the above described frameworks to create a case-based

reasoning system and an organization that is suitable for knowledge management. Therefore

the tasks regarding knowledge management were separated according to the building blocks

framework. The knowledge blocks are identification, acquisition, development, distribution,

use, preservation, goals and valuation (Bartlmae & Riemenschneider, 2000, p. 3).

First, the CBR-steps were mapped to the knowledge building blocks. After that, the roles of

the experience factory organization were assigned with responsibilities regarding these

steps. The result can be seen in the figure below.

Figure 3 - Responsibilities of Experience Factory roles mapped to knowledge management tasks in the CBR steps (red = important, blue = less important role) (Bartlmae & Riemenschneider, 2000, p. 5)

Realization of a system for KDD-projects

Daimler applied this framework for their KDD projects in Customer Relationship

Management. They implemented the CBR based experience factory in their organization.

The experience base called Core-DM, Case Oriented Reuse of Experiences in Data Mining,

was the result of the development of the technical architecture. In this all the required

experience package types regarding KDD projects can be stored, e.g. documents about

CRISP-DM, data-transformation used in former KDD-projects, lessons learned, experts

involved in KDD projects and KDD methods (Bartlmae & Riemenschneider, 2000, p. 7).

3.4.2 Other knowledge application systems

The principle of case-base reasoning has also been used by Daimler to develop a method to

automatically set test-links between test cases and reused system requirements. This is

applied when a new vehicle series is introduced and previous artifacts, i.e. knowledge, are

reused for the engineering of the new series (Noack, 2013, p. 1).

4 Performance benefits of knowledge management The goal of knowledge management of companies in the automobile industry, as explained

in this report, is to improve their product and financial performance and thus to gain a

competitive advantage.

The case study on the Daimler AG provides examples on how knowledge management

systems can help to reduce costs, improve profitability and to improve products and services.

However though quantifying the direct performance benefits of implementing knowledge

management is difficult, it was easier for the company to cope with the problems they

previously faced. With the use of knowledge management mechanisms and tools, Daimler

AG was able to discover niche markets and decrease their product development cycle time

to meet the requirements of the abroad markets. This was a major factor in helping them

recover from significant financial losses.

Several studies which investigate the impact of knowledge management on the

organizational performance conclude that under a number of conditions knowledge

management improves the organizational performance. E.g. a study showed that knowledge

sharing through face-to-face communication is positively related to product and financial

performance. The condition of high technology dynamism, which is given in the automobile

industry, lead to the finding that technological knowledge sharing only has a positive impact

on the product performance (Lakshman & Parente, 2008, p. 1).

In conclusion, though there is a lack of information and tools to help correlate the direct

impact of knowledge management on the performance benefits of a company it is evident

that knowledge management system help organizations to cope with market challenges in

their domain and, in the end, improve their product and financial performance.

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