A Knowledge-Based Sales Support System
Increasing winrates with knowledge
Graduation Thesis Author: Mark de Groot
date: August, 15th 2004.
A KNOWLEDGE-BASED SALES SUPPORT SYSTEM
A Knowledge-Based Decision Support System approach for increasing availability and accessibility of knowledge
Increasing winrates with knowledge
Author: Mark de Groot
Student number: 1059858
University: Rijksuniversiteit Groningen Faculty: Management & Organization Major: Information Technology
1
stsupervisor: Dr. T.W. de Boer
2
ndsupervisor: R.S. Cijsouw
Location: Amsterdam
--- "The empires of the future are the empires of the mind." - Winston Churchill
Preface
When looking back at this six months during internship, I must admit this has been my most
challenging assignment ever since. My interest in the Information Technology industry has increased tremendously during this period. I also developed a great understanding on the knowledge
management subject. I learned how important the implications of knowledge are for companies and how to gain competitive advantage with it.
COMPANY X has provided me the opportunity to graduate on this very interesting subject with grounds in all other disciplines within the organization, for which I am very grateful. This was not possible without the help of all the people that spent significant time on interviews and other questions I continuously had in mind for them.
Special thanks go out to my COMPANY X supervisor Peter Dobber and University supervisor Dr.
Thomas de Boer, whose ideas and visions on the subject were often extremely useful to continue my path. I also would like to thank Simon de Koning and Rudolph Oudeboon for creating this opportunity for me at COMPANY X.
I hope you enjoy reading!
- Mark de Groot ’04.
NOTE: This is the public version of the thesis and therefore the original company name has been
replaced with COMPANY X.
Management Summary
This research is about the establishment of a knowledge framework that can be used to build a future knowledge-based decision support system which contributes to the improvement of availability and accessibility of knowledge to people involved in the sales process to increase the quality of proposals and thus increase the win-rate. The research is focused on the ITS Sales process and answers the question: What information and knowledge is necessary for designing a knowledge-based decision support system to increase efficiency in creating proposals and how is this to be structured?
In appendix C a complete overview is provided of the available sources that could be used in the support system.
By modeling the business process and providing a visual representation, a clear view is created how the processes are structured and which information and knowledge could be used. The framework distinguishes knowledge into four categories: Factual knowledge, Inferential knowledge, procedural knowledge and contextual knowledge, in order to fit these sources into a knowledge-based decision support system.
Increasing efficiency and quality with the support system is based on time reductions and increasing availability of knowledge. The system provides information and knowledge on People and Expert finding, Offering & Proposing, Sales & Customers and Procedures. I suggest reading paragraph 5.7 which graphically explains the sales process and information & knowledge used in the each step.
Implementing the system will decrease the user’s time spent on searching for people, information and
knowledge concerning the sales process. It also provides easy access to historic deals, solutions and
competitor information and knowledge, such as their solutions and proposing strategies. As already
said, with knowledge it is possibly to establish competitive advantage.
TABLE OF CONTENTS
Chapter 1 - Description of the Organization ... 9
Chapter 2 – Research Methodology ... 12
2.1 Situation ... 12
2.2 Research Issue ... 12
2.2.1 Research Objective... 12
2.2.2 Research Question... 13
2.2.3 Research Subquestions ... 13
2.2.4 Research Restrictions ... 13
2.2.5 Research Scope ... 14
2.3 Research Framework... 14
2.4 Key Concepts... 14
2.5 Data Collection ... 15
2.5.1 Documentation ... 15
2.5.2 Interviews ... 15
2.5.3 Observation... 15
2.6 Research Planning... 16
Chapter 3 – Theory ... 17
3.1 About Knowledge ... 17
3.1.1 Data, Information, Knowledge, Wisdom Hierarchy ... 17
3.1.2 Classification of knowledge... 20
3.1.3 Sharing Knowledge ... 21
3.2 Knowledge-Based Decision Support Systems ... 22
3.2.1 Introduction to a KBDSS... 22
3.2.2 The working of a Knowledge-Based Decision Support System... 23
3.2.3 Inferencing ... 24
3.2.4 Advantages... 25
3.2.5 Knowledge classification and KBDSS... 25
3.3 Competitive Environments... 26
3.4 Knowledge Management... 29
3.4.1 About Knowledge Management ... 29
3.4.2 Trends and future of KM ... 29
Chapter 4 – Analysis... 32
4.1 Business Modeling... 32
4.1.1 Sales Process Overview... 32
4.1.2 Signature Selling Method (SSM)... 34
4.1.3 Customer Relationship Management ... 37
4.1.4 Combining SSM and CRM... 38
4.2 Roles and Procedures ... 39
4.2.1 Opportunity Noticer (ON) ... 39
4.2.2 Opportunity Identifier (OI) ... 40
4.2.3 Opportunity Owner (OO) ... 43
4.2.4 Solution Design... 46
4.2.5 Offering Manager... 47
4.2.6 Opportunity Management Decision Team ... 47
4.2.7 Resource Deployment ... 48
4.2.8 Architect ... 48
4.2.9 Subject Matter Expert... 49
4.2.10 Technical Solution Manager ... 49
4.2.10 Solution Design Center... 49
4.2.11 Contracts & Negotiations ... 49
4.2.12 Pricing... 50
4.2.13 Procurement ... 50
4.2.14 Quality Assurance ... 51
4.2.14 Bidteam... 51
4.2.15 Bidmanager ... 51
4.2.16 NL Champion... 51
4.2.17 EMEA Champion... 51
4.2.18 ITS Services Management Team ... 52
4.3 Requirements from Sales Process... 52
4.3.1 Opportunity Identifiers ... 52
4.3.2 Opportunity Owners ... 53
4.3.3 Offering Managers ... 53
4.3.4 OMDT ... 53
4.3.5 Bidmanagers ... 53
4.3.6 Architects... 54
4.3.7 Subject Matter Experts ... 54
4.3.8 Technical Solution Manager ... 54
4.3.6 NL Champions ... 54
4.4 Proposals ... 55
4.4.1 Internal factors ... 55
4.4.2 External factors ... 57
4.5 Identification of information and knowledge resources... 59
4.5.1 Introduction... 59
4.5.2 Theoretical Approach ... 59
4.5.3 Identification of sources ... 60
4.5.2 Structure of sources ... 61
4.5.4 Classification of sources... 62
4.5.3 Problems and missing sources ... 63
4.6 User requirements ... 65
4.6.1 Needs and wants... 65
4.6.2 User Interface... 65
4.6.3 Considerations from the user requirement perspective ... 66
Chapter 5 - Design... 67
5.1 The Framework... 68
5.1.1 Mapping the sources... 68
5.1.2 Modules ... 69
5.2 The core system ... 70
5.2.1 Knowledge Base ... 70
5.2.2 Inference engine ... 70
5.2.3 Interfaces ... 71
5.2.4 Acquisition Agents... 71
5.2.5 User interface ... 71
5.3 Techniques... 71
5.3.1 Crawling the web ... 71
5.3.2 Crawling Notes Databases ... 73
5.3.3 Case Based Reasoning... 73
5.4 People & Expert Finder... 73
5.5 IT Market... 74
5.5.1 Market Intelligence ... 74
5.5.2 Competitive Intelligence... 75
5.5.2 News ... 76
5.5.3 Inferential knowledge... 76
5.6 Sales and Customers ... 77
5.6.1 Sales Information and Knowledge ... 77
5.6.2 Presentations ... 77
5.6.3 Customer References... 77
5.6.4 Customer Intelligence... 77
5.7 Offering & Proposing... 79
5.7.1 Offering information and knowledge ... 79
5.7.2 Intellectual Capital ... 79
5.7.3 Technical information and knowledge ... 79
5.7.4 Proposals... 79
5.8 Procedures ... 80
5.9 Overview & Recommendations... 84
Chapter 6 – Conclusion... 85
Abbreviations ... 87
Literature... 88
Chapter 1 - Description of the Organization
COMPANY X is the world’s largest informational technology company and was founded originally in 1911 under the name Computing-Tabulating-Recording Company (C-T-R).
In 1924 C-T-R changed its name into COMPANY X Corporation. Throughout the years COMPANY X went from punch-card tabulating machines to room-sized calculators to mainframe computing systems for large enterprises. COMPANY X even changed the nature of accounting, calculation and the basis back-office business processes.
The mission is stated as follows: “We strive to lead in the creation, development and manufacture of the industry’s most advanced information technologies”. These advanced technologies can be translated into value for customers through professional solutions, services and consulting businesses worldwide.
COMPANY X is divided into several business groups and units, the so called “brands”. This thesis is on a subject within Integrated Technology Services (ITS), which is a part of COMPANY X Global Services (IGS).
As shown in the picture below, COMPANY X has divided the market into 6 sectors for which it has two different approach strategies: direct and indirect, such as through business partners.
Figure 1 - COMPANY X Overview
COMPANY X Nederland NV
COMPANY X Nederland N.V. is the Dutch subsidiary of COMPANY X, embedded in the geographical region EMEA (Europe, Middle East and Africa). Legally, COMPANY X Nederland N.V.
is an independent daughter company of COMPANY X World Trade Corporation, which in turn is a subsidiary of the COMPANY X Corporation. This Dutch part of the organization has approximately 5500 employees. An organization chart has been included in the appendices.
Integrated Technology Services (ITS)
Integrated Technology Services is a subset of COMPANY X Global Services. ITS is the premier technology integrator, providing services to enable, integrate, optimize and manage on demand business infrastructures and increase the business value of IT for customers. ITS turns over 30% of IGS’ revenues. ITS covers the total chain from offering development, lead generation, through sales, and inclusive of delivery. The unit is segmented into four offering domains: Infrastructure Systems Management Services (ISMS), Networking & Connectivity Services (N&CS), Business Continuity &
Resilience Services (BCRS) and IT Educational Services (ITES).
ITS Solutions Areas
The building blocks or business units within COMPANY X are organized internally to provide value
in the most cost effective manner. Customers benefit directly from the economies of scale that comes
along. However, a key differentiator between COMPANY X and the competition is the ability to pull
together these various strands into solutions that meet the changing issues and consequent needs of
customers. That is why ITS has chosen to work with market solutions and to deliver the solutions by
offering category.
As shown in the picture above, the four Solution areas are: IT Optimization, e-Transformation, Keep IT running and Manage IT for me. A short description of these areas:
IT Optimization: The optimal balance between ICT costs and the value to the business
• Reduction of complexity
• Cost reduction
• Standardization
• Consolidation
• Increase productivity
e-ICT Transformation: Ensures your ICT infrastructure is ready for the future, ready for e- business on demand
• Integration of ICT-systems
• Integration new technologies (innovation)
• Design, build of e2e e-business environments
• Configure/build of ICT-environments
Keep IT running: Keeps your ICT running according to your business needs
• High availability
• Security
• Continuity / uninterrupted business execution
• Disaster recovery
• ICT support & maintenance
Manage IT for me: We manage your ICT environment for you against lower costs and fewer resources
• Selective out tasking of infrastructure
• Return to core activities
• Predictability of cost and service levels
• Access to specialists knowledge
• Piggyback with technological innovation
• Flexibility, functionality and capacity
Chapter 2 – Research Methodology
2.1 Situation
The ITS management team noticed that the win rate on the proposals, that were brought out, is insufficient. There has been done some research on the matter why deals were lost. The most important outcome was that the creation of proposals is too time-consuming and that specific knowledge resources (experts and electronic resources) were not available or easily accessible.
The management team indicated that they wanted a support system that combined all the useful information and knowledge available within COMPANY X to speed up the process of proposing.
Along aside arose the idea to combine this with market intelligence support so everyone within ITS would have easy access to information and knowledge about competition and what they are capable of.
The team asked me to do a research on how the availability and accessibility of existing knowledge could be increased and to try to identify missing knowledge. By increasing the accessibility and availability of knowledge, management believes the quality of the proposals will rise and consequently the win rate on these proposals will increase accordingly. Based on the knowledge framework designed in this research a knowledge-based decision support system will be constructed.
2.2 Research Issue
The problem definition consists of an objective, a research question with its underlying sub questions and research restrictions (De Leeuw, 1996). The objective is the practical relevance of the research (Baarda et al., 2001). Besides that, the objective tackles the why question.
The research question formulates the main question of the research that connects to the objective and is defined in accessible terms.
2.2.1 Research Objective
Based on the initial problem description by the management team I constructed the following research objective:
Establishment of a knowledge framework that can be used to build a future knowledge-based
decision support system which contributes to the improvement of availability and accessibility
of knowledge to people involved in the sales process to increase the quality of proposals and
thus increase the win-rate.
2.2.2 Research Question
The following research question belongs to the objective of this research:
What information and knowledge is necessary for designing a knowledge-based decision support system to increase efficiency in creating proposals and how is this to be structured?
To be able to provide an answer to this question, the research question will be divided into sub research questions in the following paragraph.
2.2.3 Research Subquestions
By dividing the research question into sub research questions the researcher is led step by step to the answer of the research question.
This leads to the following sub research questions:
1. What is information and knowledge and why is it so important?
2. What is a knowledge-based decision support system?
3. How can information and knowledge be used to gain competitive advantage?
4. How is the sales process organized and what requirements can be derived from this?
5. What factors influences the win rate on proposals?
6. What are the wants and needs from the user’s perspective?
7. What information and knowledge is available 8. What sources are missing?
9. How could the information and knowledge sources be structured?
2.2.4 Research Restrictions
Restrictions set the limitation of the research and research method. Restrictions are predetermined by the external environment and thus can not be influenced by the researcher.
The following restrictions apply to this research:
• The domain of this research is the Dutch IT Service Market.
• The timeframe of this research is six months.
• The research and research methods should be both theoretically responsible and practically useful.
• The research must be in line with the company’s strategy.
• Staff departments are outside the research’s boundaries.
2.2.5 Research Scope
The focus of this research is on the analysis of the required information and knowledge for building a framework to be used in a future design of a knowledge-based decision support system.
2.3 Research Framework
The research will cover the following issues:
Sales Process
Users
Support System
Information/
knowledge Sources Framework
options requirements
selection requirements
Theory
Figure 2 - Research Framework
• Sales Process - Business modeling of the activities around proposal
creation.
• Users - What are the needs and wants of the users?
• Support System - What are the possible options regarding to the
knowledge based decision support system?
• Information/knowledge sources - Identification of information/knowledge sources
• Framework - The outcome of this research
2.4 Key Concepts
The research will be approached from different perspectives, the so-called multiform-view. The
requirements ensue from the sales process and the users, combined with the options resulted from the
theoretical support system analysis and the available information and knowledge, will provide a good
and objective input for the design of the framework.
By modeling the sales process, areas to support are being identified. These are the requirements that result from this view.
User requirements are established by taking interviews. What do they really need and what are their additional wants?
By analyzing the available information and knowledge sources combined with user interviews to establish the most used and desired sources, the appropriate selection of sources can be determined.
The options of the Support System will be determined by analyzing literature in context of the research objective. All this together will provide the elements to be designed in the framework.
2.5 Data Collection
The research question as well as the sub questions will be answered by using multiple sources and by applying several methods to collect data. Collection methods that are used are gathering information from already existing documentation, such as the COMPANY X intranet and Lotus Notes databases, interviews with stakeholders of the sales process and observation of activities within the sales process.
2.5.1 Documentation
Existing documentation
• Media
• Brochures
• Internet
• Databases
• Internal reports
2.5.2 Interviews
Several stakeholders that are involved in the sales process have been interviewed. Interviews were held in a semi-structured way. In case of business modeling it has often been a iterative process. For this research I interviewed several people out the salesforce, a technical solution manager, several offering managers, the manager of the opportunity management decision team (OMDT), the manager ITS business operations, bidmanagers and consultants.
2.5.3 Observation
To obtain a good perception of the actual sales processes and procedures, it’s best to experience it
yourself, so I took place in two project teams. In those project teams new standard offerings were
developed in cooperation with potential customers. I have experienced all processes from first client
contact to contract signing. This provided me better understanding of the sales process.
2.6 Research Planning
What is information and knowledge?
What is a knowledge base support system and how is it applied?
Business Modeling - What are the processes from customer to proposal?
Identification of relevant knowledge sources
Procedural Factual Inferential Contextual
Users
Requirements
Framework What factors influences
the winrate of proposals?
AnalysisDesignTheory
Conclusions & Recommendations Research Problem
Chapter 3 – Theory
As formulated in the research restrictions, it must be possible to use the solution to this research problem in the future design of a knowledge-based support system. In this paragraph I will clarify the differences between data, information and knowledge, I will describe what a knowledge-based support system is and what the possibilities regarding to the research objective are. Also I will describe the competitive environment and its effects and how knowledge management is related to the problem.
3.1 About Knowledge
The key questions in this paragraph are What is knowledge? and Why is it important to companies? In order to comprehend knowledge it is important to understand the distinction between the terms data, information, knowledge and wisdom. I will discuss this in the next subparagraph.
3.1.1 Data, Information, Knowledge, Wisdom Hierarchy
It is important to make a distinction among data, information, knowledge and wisdom in the context of this research. In this case it is very relevant because competitive advantage is driven by continuous innovation, which in turn relies on knowledge creation (Nonaka & Takeuchi, 1995).
“Knowledge is a justified belief that increases an entity’s capacity for effective action.” (Nonaka 1994, Huber 1991). This definition is derived from Plato’s Theaetetus which thought to have taken place in the year 369 B.C. In this dialogue Socrates, Theodorus and Theaetetus try to define knowledge.
“Information is the flow of messages or meaning which may add to, restructure, or change knowledge.” (Machup 1983).
The concepts of data, information, knowledge and wisdom are closely related and are often confused.
In everyday discourse, the distinction between data and information, on one hand, and between information and knowledge, on the other, remains typically vague. Therefore I will discuss these terms here thoroughly.
Data are raw facts that are meaningless by themselves, such as names or numbers. Data are items about things, events, activities, and transactions are recorded, classified, and stored but are not organized to convey any specific meaning. They can be numeric, alphanumeric, figures, sounds, or images (Turban, 2001). Data can also be seen as the carrier of knowledge and information, a means through which knowledge and information can be stored and transferred. Both information and knowledge are communicated through data, and by means of data storage and transfer devices, and systems. In this sense, a piece of data only becomes information or knowledge when it is interpreted
Theory
About knowledge About KBDSS
Competitive Environments
Knowledge Management
by its receiver (Kock et.al., 1996). Likewise, information and knowledge from a person can only be communicated to another person after they are encoded as data.
Information is data that are organized in a meaningful way, so they have meaning for the recipient.
They confirm something the recipient knows, or may have “surprise” value by revealing something not known (Turban, 2001). Information is data that has been given meaning by way of relational connection. In computer vernacular, a relational database makes for information from data stored within it (Ackoff, 1989).
Knowledge is in common parlance the possession of information (Wikipedia). A broader definition is provided by Turban: knowledge is understanding, awareness or familiarity acquired through education or experience. Anything that has been learned, perceived, discovered, inferred, or understood. The ability to use information. In a knowledge management system, knowledge is information in action (Turban, 2001). Knowledge consists of data items and/or information organized and processed to convey understanding, experience, accumulated learning, and expertise as they apply to a current problem or activity. Knowledge can be the application of data and information in making a decision (Turban, 2001). Ackoff defines knowledge as the appropriate collection of information, such that its intent is to be useful. Knowledge is a deterministic process. When someone "memorizes" information, then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, an integration such as would infer further knowledge (Ackoff, 1989). For example, elementary school children memorize, or amass knowledge of, the "times table". They can tell you that "2 x 2 = 4" because they have amassed that knowledge (it being included in the times table). But when asked what is the outcome of "1267 x 300", they can not respond correctly because that entry is not in their times table. To correctly answer such a question requires a true cognitive and analytical ability that is only encompassed in the next level: understanding. In computer parlance, most of the applications we use (modeling, simulation, etc.) exercise some type of stored knowledge.
Understanding is an interpolative and probabilistic process. It is cognitive and analytical. It is the process by which I can take knowledge and synthesize new knowledge from the previously held knowledge. The difference between understanding and knowledge is the difference between "learning"
and "memorizing". People who have understanding can undertake useful actions because they can synthesize new knowledge, or in some cases, at least new information, from what is previously known (and understood). That is, understanding can build upon currently held information, knowledge and understanding itself. In computer parlance, AI systems possess understanding in the sense that they are able to synthesize new knowledge from previously stored information and knowledge (Ackoff, 1989).
Wisdom is an extrapolative and non-deterministic, non-probabilistic process. It calls upon all the
previous levels of consciousness, and specifically upon special types of human programming (moral,
ethical codes, etc.). It beckons to give us understanding about which there has previously been no
understanding, and in doing so, goes far beyond understanding itself. It is the essence of philosophical
probing. Unlike the previous four levels, it asks questions to which there is no (easily-achievable)
answer, and in some cases, to which there can be no humanly-known answer period. Wisdom is therefore, the process by which we also discern, or judge, between right and wrong, good and bad. I personally believe that computers do not have, and will never have the ability to possess wisdom (Ackoff, 1989).
Recapitulated the distinction made above is stated as follows:
Data: raw facts that are meaningless by themselves, such as names or numbers.
Information: data that are organized in a meaningful way
Knowledge: Application of data and information, information in action Understanding: appreciation of "why"
Wisdom: evaluated understanding
Bellinger, et. al., say that the sequence as described here above is a bit less involved. They therefore represented the transitions from data, to information, to knowledge and finally to wisdom in the following diagram.
data
information
knowledge
wisdom
Understanding relations
Understanding patterns
Understanding principles connectedness
understanding
Diagram - From data to wisdom (Bellinger, et. al.)
Bellinger, et. al., differ from the above on the understanding part. They state that understanding supports the transition from each stage to the next, in contrast to the fact the understanding is a separate level of its own. In his opinion data represent a fact or statement of event without relation to other things, for example: it is raining. Information embodies the understanding of a relationship of some sort, possibly cause and effect, for example: the sky became dark and then it started raining.
Knowledge represents a pattern that connects and generally providing a high level of predictability as
what is described or what will happen next, for example: when humidity is very high and the sky turns
grey, the atmospheres is often unlikely to be able to hold the moisture so it rains. Wisdom embodies
more of an understanding of fundamental principles embodied within the knowledge that are
essentially the basis for the knowledge being what it is. Wisdom is essentially systemic, for example:
It rains because it rains. And this encompasses an understanding of all the interactions that happen between raining, evaporation, air currents, temperature gradients, changes, raining.
In my opinion the definition of Bellinger et. al. is the best suitable definition for this research. So, the definition of knowledge in this thesis will be:
The collection of representations of patterns that connect and generally provide a high level of predictability.
These representations exist out of relevant and actionable data and information. Thus knowledge reduces uncertainty and narrows the band of unpredictable outcomes in decision making.
3.1.2 Classification of knowledge
As concluded in the previous paragraph, knowledge is the collection of representations of patterns that connect and generally provide a high level of predictability. Knowledge can be expressed in various forms, which I will discuss down below.
There are many different perceptions of knowledge and the different types that are distinguished.
Clarke (1998) classifies knowledge as follows:
• Advantaged Knowledge
• Base Knowledge
• Trivial Knowledge
Advantaged knowledge can best be described as knowledge that can provide competitive advantage.
Base knowledge is knowledge that is integral to an organization, providing it with short-term advantages (e.g. best practices) and trivial knowledge is knowledge that has no major impact on the organization. In terms of this research it can be quite useful to classify knowledge this way, because base- and advantaged knowledge are to be the master ingredients for gaining higher win rates on proposals.
According to Polanyi (1958) an organization has two types of knowledge: Tacit Knowledge and explicit knowledge. Tacit knowledge is usually in the domain of subjective, cognitive and experiential learning, whereas explicit knowledge deals more with objective, rational, and technical knowledge and is highly personal and hard to realize (Nonaka and Takeuchi, 1995).
Knowledge can also be classified into a priori knowledge and posteriori or empirical knowledge
(Wikipedia Encyclopedia). A priori knowledge can be freely obtained without needing to observe the
world, in contrary to posteriori knowledge, which is only obtained after observing the world or
interacting with it in some way. Inferential knowledge, which is based on reasoning from facts or from other inferential knowledge such as a theory, may or may not be verifiable by observation or testing (Wikipedia Encyclopedia). The distinction between factual knowledge and inferential knowledge has been explored by the discipline of general semantics. Procedural knowledge is simply said know-how.
This is de knowledge how to perform a task. All other knowledge that cannot be caught by procedural, factual and inferential knowledge, will be called Contextual Knowledge. Context is from a engineering point of view the collection of relevant conditions and surrounding influences that make a situation unique and comprehensible (Hasher and Zacks, 1984; Anderson, 1995). Brézillon and Pomerol (1999) define contextual knowledge as the context that is not explicitly used but influences the problem solving. It is perfectly possible and moreover very common to share the same contextual knowledge and to make different rational decisions. It is even not necessary to invoke a difference between the values of the subjects, it suffices that the context be interpreted differently (Pomerol and Brézillon, 2002).
The classification used for this research has to be in line with the possibility to integrate this into a knowledge-based support system. The classification of knowledge by procedural knowledge, factual knowledge, inferential knowledge complies with the requirements of a KBDSS. The procedural and factual knowledge can be stored into the knowledge base, the inferential knowledge is used to build the inference component of the KBDSS and contextual knowledge is all knowledge which cannot be classified in one of the other categories and requires special attention when incorporating this into the KBDSS. I will further discuss this in paragraph 4.2.
Procedural Factual Inferential Contextual
Knowledge
Figure 3 - Classification of Knowledge
3.1.3 Sharing Knowledge
The top five benefits a company can encounter using knowledge management systems according to a survey conducted by the Harris Research Center are:
• Better decision making
• Reduced costs
• Faster response time to key issues
• Improved productivity
• Shared best practices
The benefits are quite clear, but why does not everyone share knowledge within a company or organization? Some clear reasons according to Vaas (1999) are:
• Willing to share, but there is not enough time to do so
• No skill in knowledge management techniques
• Don’t understand knowledge management and benefits
• Lack of appropriate technology
• No commitment from senior managers
• No funding for knowledge management
• Culture does not encourage knowledge sharing
Only a few apply to COMPANY X: a lot of people are willing to share, but due to a lack of time, a lot of information and knowledge is not (properly) shared. Commitment from senior management is in attendance, but on a low priority basis. The main cause for the lack of commitment is that funding for improvement in knowledge management projects has been frozen because of other higher priorities.
3.2 Knowledge-Based Decision Support Systems
In this paragraph I will describe what a knowledge-based decision support system (KBDSS) is, how it works and how it can be aligned with the research question to increase efficiency in the sales process and increasing the win rate on proposals.
3.2.1 Introduction to a KBDSS
A Knowledge based decision support system (KBDSS) is a combination of a Knowledge-based
system and a Decision support system. Sometimes this is referred to as an intelligent expert system. A
Decision Support System (DSS) is a computer-based information system that combines models and
data in an attempt to solve non-structured problems with extensive user involvement through a
friendly user interface (Turban, 2001). A knowledge-based decision support system can enhance the
capabilities of decision support not only by supplying a tool that directly supports a decision maker but
also by enhancing various computerized decision support systems (Turban, 2001). According to
WordNet, decision making is the cognitive process of reaching a decision. In this research the persons
who are involved in creating proposals have to make choices about price, product combinations to be
offered and how the customer is to be approached.
3.2.2 The working of a Knowledge-Based Decision Support System
The collection of knowledge related to a problem (or opportunity) used in an Artificial Intelligence (AI) system is organized, and it is called a knowledge base. (Turban, 2001). From a systems’ point of view, knowledge is to be divided into facts and procedures, which are usually rules, regarding the problem domain.
A unique feature of a knowledge-based support system (KBDSS) in comparison to the native decision support systems, is that is has the ability to reason. Based on the expertise stored in the knowledge base and the ability to access databases, the system can make inferences. Inference is defined as the reasoning involved in drawing a conclusion or making a logical judgment on the basis of circumstantial evidence and prior conclusions rather than on the basis of direct observation (WordNet).
Inferencing in KBDSS is performed in a component called the inference engine which includes procedures regarding problem solving.
Knowledge Base Inferencing capability Inputs (questions,
problems, etc)
Outputs (Answers, alternative solutions, etc.) Computer
Figure 4 - Applying AI concepts with a computer (Turban, 2001)
Expertise captured in the knowledge base and inference engine can include the following types of knowledge (Turban, 2001):
• Theories about the problem area
• Rules and procedures regarding the general problem area
• Rules (heuristics) about what to do in a given problem situation
• Global strategies for solving these types of problems
• Metaknowledge (knowledge about knowledge)
• Facts about the problem area
The knowledge-base is usually filled through an acquisition module. This can be handled by a
knowledge engineer, but nowadays it is also combined with automated acquisition. Because corporate
data doubles every six to eight months (of which 85% is unstructured), it is useful to incorporate
autonomous acquiring engines in the design of a KBDSS. The IBM Research Center defines
unstructured data as any data residing unorganized outside of a database and can be text, audio, video
or graphics.
3.2.3 Inferencing
The inference engine, also known as the control structure or the rule interpreter, is the brain of the system (Turban, 2001). This component provides a methodology for reasoning about information and formulating conclusions. There are several techniques for reasoning, such as reasoning with logic, rule-based reasoning, model-based reasoning, case-based reasoning and meta-level reasoning.
Inference with logic as well as with rules can be very effective, but there are some limitations. One reason is the familiar axiom that there is an exception to every rule. Other limitations are (Nilsson, 1998; Russel and Norvig, 2002):
• Can be difficult to create (the “knowledge engineering” problem)
• Can be difficult to maintain, in large rule-bases, adding a rule can cause many unforeseen interactions and effects (difficult to debug)
• Many types of knowledge are not easily represented by rules o Uncertain knowledge: “if it is cold it will probably rain o Information which changes over time
o Procedural information (sequence of tests to diagnose a disease)
Model-based reasoning is based on knowledge of the structure and behavior of a device (Turban, 2001). A model-based system is designed to understand and simulate the working of a device and therefore not applicable in the design of a KBDSS in this research. Case-Based reasoning (CBR) is to adapt solutions that have been used to solve old problems for use in solving new problems (Turban, 2001). This process finds cases in memory that contain solved problems similar to the current problem.
It adapts the previous solution or solutions to fit the current problem, taking into account any differences between the current and previous situations. A case is the primary knowledge-base element in a CBR application. It defines a situation or problem in terms of natural language descriptions and answers to questions and associates with each situation a proper business action (Vrooman, 1991).
CBR has proven to be an extremely effective approach in complex cases (Kolonder, 1993). CBR Web identifies some application categories and examples on their website:
• CBR in electronic commerce – intelligent product catalog search, intelligent customer support and sales support
• WWW and information search – browsing advisor, retrieving tour packages from travel catalog, cased-based information retrieval in construction, and skill profiling in electronic recruitment.
• Reuse – reuse of structural design calculation documents, reuse of object-oriented software, and reuse assistant for engineering designs.
• Reasoning – heuristic retrieval of legal knowledge, reasoning in legal domains, and computer-
supported conflict resolution through negotiation or mediation.
Meta-knowledge is a system’s knowledge about how it reasons. This is knowledge about what is known, for example about the importance and relevance of certain facts and rules (Turban, 2001). It allows the system to examine the operation of the declarative and procedural knowledge in the knowledge base. This knowledge is used for generating explanations.
It is obvious that when searching for a solution for new problems within a large amount of data, information and knowledge that there will not be a one-good-solution. There is often a degree of uncertainty in determining a solution for the problem. The Bayesian formula can be used to handle this uncertainty. It is based on subjective probabilities which express the degree of belief or how strongly a value or a situation is believed to be true (Turban, 2001). Autonomous crawlers, such as Autonomy use Bayesian for probabilistic pattern-recognition out of unstructured data and information. It extracts the context out of information. The exact working of these kind techniques is very complex and will not be further discussed in this research.
3.2.4 Advantages
The advantages that could be achieved using knowledge-based support systems differ with the nature of the system. Based on the advantages defined by Turban, the following could apply to COMPANY X:
• Decreased decision-making time
• Increased process and product quality
• Capture of scarce expertise
• Flexibility
• Accessibility to knowledge
• Ability to work with incomplete or uncertain information
• Enhancement of problem solving and decision making
• Improved decision-making processes
• Improved decision quality
• Ability to solve complex problems
• Knowledge transfer to remote locations
• Enhancement of other information systems
3.2.5 Knowledge classification and KBDSS
As defined earlier, knowledge in this research is classified in factual knowledge, procedural
knowledge, inferential knowledge and contextual knowledge. This is completely supported by the
KBDSS. Factual knowledge can be stored as facts in the knowledge base, procedural knowledge
comprises rules that are also to be stored in the knowledge base. Inferential knowledge is stored in the inference engine.
3.3 Competitive Environments
One of the restrictions of the research is that it must be in line with the company’s strategy. In this paragraph I will show how a corporate mission is linked with strategy and tactics. Increasing the win rate, as mentioned in the research objective, is to the expense of the competition, therefore it is important to know your competitors very well. Eventually, the increase in win-rate of the proposals should result into better market share. To achieve this, a knowledge based support system is be introduced to aid people involved in the sales process.
As IT becomes more and more important and penetrates to the heart of a firm’s strategy, the complexity of IT management challenge increases considerably. Michael Porter’s industry and competitive analysis (ICA) framework has proven very effective in this respect (Applegate, 1999).
Bargaining power of suppliers
Potential new entrants
Bargaining power of buyers
Threat of substitute products
or services Industry competitiors
Porter’s Five Forces Model (Porter, 1980)
When we look at the traditional work of Porter (Porter, 1980) we can identify the potential uses of IT to combat forces. The threats of new entrants can imply new capacity, substantial resources and reduced prices or inflation of incumbents’ costs. With the use of IT there can be provided entry barriers, such as economies of scale and switching costs. Implications of the buyer’s bargaining power can be that prices are forced down, demand for higher quality and more services. IT can be used to make buyer selections, create switching costs, and develop entry barriers and differentiation. The bargaining power of suppliers can imply raises in prices and reduced quality and services (labor). IT can be used to make selection and reduce the threat of backward integration.
The force where this research will focus on is the so-called traditional intra-industry rivals. This force
is based on competition with price, product and, distribution and service. IT can be used to increase
the cost-effectiveness, gain faster market access and differentiation on product, services and firm.
Knowledge management is an important focus point to support these competitive elements and to create competitive advantage.
“In the emerging economy, a firm’s only advantage is its ability to leverage and utilize its knowledge.”
1. So, knowledge management should directly reflect the business strategy (Tiwana, 2000).
Therefore it is important to determine business objectives and strategy and how knowledge management can help to achieve those goals. Competitive advantage is the goal of any strategy (Porter, 1980). Knowledge is the basis of technology and its application. In the 21
st-century competitive landscape, knowledge is a critical organizational resource and is increasingly a valuable source of competitive advantage. (Hitt, et.al. 2001). The probability of achieving strategic competiveness in the 21
st-century competitive landscape is enhanced for the firm that realizes that its survival depends on the ability to capture intelligence, transform it into usable knowledge, and diffuse it rapidly throughout the company. (Hitt, et.al. 2001). So, knowledge offers potentially one of the most sustainable advantages. Knowledge - especially the tacit, context-specific knowledge embedded in complex business activities and processes and developed from experience - is unique and very difficult to imitate (Zack, 1999).
Strategy is a method or plan adopted by a firm to balance its external environment (opportunities and threats) and the internal capabilities (strengths and weaknesses)
2. The current ITS strategy is to focus on the growth areas, the offering portfolio and how ITS is positioned within the COMPANY X on demand strategy. The On Demand strategy is a world-wide strategy to transform businesses into a On Demand environment. An On Demand business is defined as: “An enterprise whose business processes –integrated end-to-end across the company and with key partners, suppliers and customers- can respond with speed to any customer demand, market opportunity or external threat.” (Sam Palmisano, CEO COMPANY X Corporation).
In order to accomplish this strategy it has to be translated into an operational plan, also called tactics.
Every competitive position implies some things a firm must be able to do to compete, which in turn implies some things it must know and know how to do. What a firm does know constrains what it can do. The gap between what it must do and can do represents a strategic positioning gap – the traditional SWOT analyses. The gap between what a firm must know to support a competitive position and what it does know represents an internal strategic knowledge gap. It is this gap that knowledge management must address to add significant and lasting value (Zack, 1999).
1
Larry Prusak, Executive Director – The Institute of Knowledge Management
2
R. Sures, Knowledge Mangement - A Strategic Perspective, www.kmadvantage.com
Figure 5 – Identify the Strategic knowledge gaps
As concluded from the work of Zack (1999), the gap between what the firm does know and what competitors know, is identified as the external strategic knowledge gap.
A company needs to identify the knowledge gap and subsequently formulate a knowledge strategy.
This strategy should be aligned with the business strategy. By doing so, competitive advantage is being established (R. Suresh). Some strategies are: Codification strategy (automation and application of IT), Personalisation strategy (building a learning strategy), Strategic management of intellectual capital (building, managing, and exploiting knowledge-related assets) and enterprise effectiveness strategy (applying all the available knowledge in the best interest of the firm).
Firms that want to compete successfully on knowledge, need to be able to answer the following questions:
1. What is your organization's competitive strategy - how do you want to "play the game?"
2. Given that strategy, what does your organization need to know to execute it successfully?
3. What does your organization currently know?
4. Comparing what your organization knows to what it needs to know, what is your organization's internal knowledge gap - its knowledge strengths and weaknesses?
5. What do you competitors know?
6. Comparing what your organization knows to what your competitors know, what is your organization's external knowledge gap - its knowledge opportunities and threats?
7. What is the pace of learning and innovation for your industry in general and for particular competitors?
8. How effective are your organization's capabilities for learning and innovating?
9. Comparing your organization's learning and innovation capabilities with those of your competitors and industry, what is your learning gap?
10. To what degree are your current knowledge management and learning initiatives focused on these strategic knowledge and learning gaps?
Source: M. Zack, 1999
3.4 Knowledge Management
3.4.1 About Knowledge Management
Knowledge management is nowadays very important for large organizations. Knowledge management is defined as “a newly emerging, interdisciplinary business model dealing with all aspects of knowledge within the context of the firm or organization, including knowledge creation, codification, sharing or transfer and how these activities promote learning and innovation. In practice it encompasses in an overlapping manner both technological tools (intranets, groupware, etc) and organizational routines or cultures (mission, vision, strategy, policy and best practices) for the attainment of the goals or mission of the organization.” (Gotcha, 1999, partly modified).
As globalisation reduces the competitive advantage existing between companies, the role of proprietary information and its appropriate management becomes all-important. A company’s value depends more and more on “intangible assets” which exist in the minds of employees, in databases, in files and in a multitude of documents (Bontheva, et. al, 2001). It is the goal of knowledge management (KM) technologies to make computer systems which provide access to this intangible knowledge present in a company or organisation. The system must make it possible to share, store and retrieve the collective expertise of all the people in an organization.
Knowledge must first be captured or acquired in some form. The knowledge acquisition is the bottleneck in the process. Once knowledge has been acquired, it must be managed, i.e. modelled, updated and published. Modelling means representing information in a way that is both manageable and easy to integrate with the rest of the company’s knowledge. Updating is necessary because knowledge is dynamic. Publishing is the process that allows sharing the knowledge across the company. It is envisaged that in the future, the content currently available on the Web (both Internets and Intranets) as raw data will be automatically annotated with machine-readable semantic information. In such a case, we will no longer speak of information retrieval but rather of Knowledge Retrieval because instead of obtaining thousands of potentially relevant or irrelevant documents, only the dozen or so documents that are truly needed by the user will be presented to them. (Bontheva, et. al, 2001).
3.4.2 Trends and future of KM
The Bright Planet Corporation published a white paper on “Six Major Trends Affecting Knowledge Management and Information Technology” (feb, 2004).
The six trends that are distinguished in the article:
1. The ‘Deep’ Web changes the game
2. The Growing Tsunami
3. Harvest the right stuff, connect the dots 4. Transform Digital content into knowledge 5. Flexible and responsive product expressions
6. Exploit Partner ‘Ecosystems’ using interoperable systems
The first trend concerns the three phenomena’s that took place as of 1996: Database technology was introduced to the internet, the web became
commercialized with phenomenal content growth and web servers were adapted to allow dynamic serving of web pages (i.e. Microsoft ASP and Java JSP technologies). Clearly, where obtaining high quality content is essential to knowledge acquisition and management, deep Web sites must be included in the harvest strategy. This leads to very different harvest technology from standard crawlers.
The second trend is the growing Tsunami. You must realize that the compound growth rate in Web documents has been more than 200% annually. COMPANY X estimates that corporate data doubles every six to eight months of which 85% is unstructured data. Internet is truly becoming the global repository for external and internal content.
The growing overwhelming content brings us to the third trend: “Harvest the right stuff”. Harvesting means: targeted, comprehensive, filtered, qualified, and all sources necessary, including internal documents, deep web documents, surface web documents, emails, etc. Because of the fast emerging quantity of unstructured and semi-structured data it is important to “connect the dots”, or in other words, to uncover the big picture. The reality is that knowledge management content sources and analytical tools continue to grow and evolve and may remain fragmented, along with the fact that content is gathered and maintained in different databases that are customized for their specific owners.
These imperatives imply the need for both consolidated repositories mediated via appropriate source brokers for relevant content and interoperable mechanisms for bringing in new tools and content.
The fourth trend is transforming digital content into knowledge, that is through the standard pathway:
data -> information -> knowledge. Unfortunately it not always possible to construct knowledge out of available content and therefore it is necessary that knowledge contributors adhere to a standard.
The fifth trend is Flexible and Responsive Product Expressions. Users and audiences for information possess a spectrum of needs and sophistication. Some need access and control of full functionality.
Others, with perhaps less sophistication, but still with requirements for relevant information access
require pre-packaged information channels with easier search and navigation tools. For knowledge
management software to be deployable across an enterprise, its product expressions or interfaces must be responsive enterprise-wide.
The sixth and last trend is “Exploit Partner Ecosystems using Interoperable Systems”. Nowadays, leading companies are no longer trying to be everything to everyone. Instead, they increasingly focus on doing one thing well, rather than spreading their energies over a vast array of offerings.
The article clearly shows that data available on the web and the corporate intranet is rapidly expanding.
Accompanied with the increasing need to capture the right knowledge out of these sources,
unstructured data crawlers are becoming more important and large companies are currently started to
think of implementing corporate knowledge portals with autonomous acquiring engines such as those
from Brightplanet, Autonomy and Verity.
Chapter 4 – Analysis
In this paragraph I will describe the analysis of the sales business process, the roles that are played and the requirements that can be identified based on the procedures. Besides that I will describe what the internal and external effects are on the creation process of proposals. Regarding these aspects, I identified the currently available useful sources and will discuss the missing ones. Together with the user requirements it is then possible to create a multiform system design.
4.1 Business Modeling
The first step in the analysis is to model the business processes. Davenport defines a process as "a structured, measured set of activities designed to produce a specified output for a particular customer or market. It implies a strong emphasis on how work is done within an organization." Understanding and enabling business processes is critical to providing relevant, timely and accurate information to individuals. As a component of an information enterprise, business processes must be understood in order to create a meaningful information environment. Business processes provide the logical flow and context in which data is generated, processed and analyzed.
4.1.1 Sales Process Overview
The first objective of the research is to clarify the processes that take place from opportunity to proposal. I discovered that only partly work descriptions are documented, but there is no overview of the sales process recorded. The documents which are available within COMPANY X are universal for the EMEA segment and thus do not bear a real perception of the actual process in The Netherlands.
The full process is divided into two segments: Opportunity Management and Solution Design.
Solution Design Opportunity Management
Signing Proposing
Qualification Validation
Identification
Figure 6 - From Opportunity to Signing
When an opportunity has been spot, it has to pass three stages: identification, validation and qualification. If the opportunity passes, the solution design phase can be launched, which is the creation of the proposal and winning the deal. The processes of identification, validation and
Business Modeling
Factors influencing
proposals Roles &
Responsibilities Requirements from procedures
User requirements
Identification of Information &
Knowledge sources
Chapter 5 - Design 1
2
3 4
5 6
qualification is discussed by means of the Signature Selling Method in paragraph 4.1.2. In the solution design phase a solution is developed and a proposal is created and priced.
Based upon interviews with the Business Operations Manager, the Offering Development manager and an ISMS Consultant, I constructed the following sales process chart which is managed by the ITS management team.
Proposals
Potential Cus tomer
Oportunity Owner
Offering Manager
NL Champions EMEA Champion
Architects
Bidteam
Resource Deployment SDC
QA
C&N
Pricing
Procurement
supports
Technical Solutions Manager Subject Matter Experts
OMDT -approves opportunity
Qualifying -qualifies (BTT 1/2/3)
-Qualifies (BTT 4/5)
-Request resources (BTT 4/5)
-creates
-supports
-supports -is sent to
-uses
-approves opportunity
Bidmanager
-cooperates with -manages
-is part of
-is part of
-selects
WWW W3 Notes Databases ITS Marketing External sources
-coordinates
part of
Opportunity Noticer
Opportunity Identifier Opportunity
Validation -has a
-notices
-opportunity is noticed
-opportunity is identified
-opportunity is validated Opportunity owner is being appointed
Customer Relationship Management (CRM) Application
SSM - Signature Selling Method
