Applying Knowledge Management to Build-to-Order Processes in Manufacturing and Service Companies

Zachariah, Dr. Thomas

Applying Knowledge Management to Build-to-Order Processes in Manufacturing and Service Companies

Business economics - Information Management

Summary

Knowledge management differs from expert systems as the latter is more attuned to integrating all available sources of data, information and knowledge into a single, unified system of record. Furthermore, the capturing of tacit and explicit knowledge and its use in streamlining business processes differentiates knowledge management systems from expert systems.
The purpose of this study is defining the practical, pragmatic and replicable approaches to knowledge management as it relates to the build-to-order and mass customization strategies of manufacturing and services companies in the United States. The overall research question guiding this study is: How is knowledge management being used today to streamline and make more efficient service strategies of manufacturing and services companies? The research is descriptive and empirical in nature because the primary data were collected using the survey method through fact finding technique such as a questionnaire. The results show the integration of increasing role-based knowledge management in the workflow of a company.
From this study, a knowledge sharing maturity model is derived, which illustrates how the level and extent of the exploitation of knowledge in build-to-order and quote-to-order strategies have a long-term impact on the financial performance. Based on these findings, a causal relationship emerges from how a company manages its knowledge in the build-to-order and quote-to-order strategy based on these findings, and they align well to a multi-stage maturity model as a result. One of the main objectives of the study is to determine how the customer churn rate can be reduced. By optimizing business processes, companies can improve increase customer satisfaction while reducing the Days Sales Outstanding levels over time.

Excerpt

Table of Contents

Abstract ...2

List of Figures ...6

List of Tables ...7

List of Abbreviations ...8

Chapter 1: Overview...9

Background Study ... 9

Research Design... 13

Research Questions ... 14

Summary ... 14

Chapter 2: Literature Review ...15

Defining Core Concepts and Frameworks for Build-to-Order Strategies ... 17

The Perfect Order Meets Knowledge Management ... 24

The Era of Intelligent Manufacturing and Six Sigma ... 24

Knowledge-Management and Product-Configuration Strategies ... 27

Defining KPIs and Metrics of Performance ... 31

Knowledge Management Contributions to Build-To-Order Strategies ... ................... 34

Chapter 3: Research Methodology ...39

Research Question ... ............. 40

Purpose of Research ... ................ 40

Research Design... ...................... 41

Population and Sample Strategy ... ........................ 42

Research Instrument... .......................... 43

Data Collection Procedures ... .............................. 44

Ethical Considerations ... ............................... 45

Data Analyses ... ......................... 46

Chapter 4: Analysis and Presentation of Results ...47

Research Question ... ........................ 47

Details of Analysis and Results ... ........................ 55

Summary of Results ... ....................... 58

Chapter 5: Conclusions and Recommendations ...59

Discussion of the Results ... ................. 60

Assessing the Build-to-Order and Quote-to-Order's Impact on Streamlining Complex Selling

and Service Strategies ... .............. 60

Analysis of Knowledge-Management Metrics relative to The Perfect Order ... ........... 69

Conclusions and Practical Recommendations ... 72

References ...76

List of Figures

Figure 1. Six Sigma DMAIC Framework. ... 18

Figure 2. The Hierarchy of Supply-Chain Metrics. ... 21

Figure 3. Financial Analysis of Knowledge Management Contribution to Product

Configuration Strategies ... 23

Figure 4. Manual Quote-to-Order Workflow Analysis... 35

Figure 5. Streamlined Build-to-Order Process with Integrated Knowledge Management. ... 36

Figure 6. Evolution of the Toyota Knowledge-Sharing Network... 62

Figure 7. Proposed Knowledge-Sharing Maturity Model... 63

Figure 8. Attaining Higher Levels of Production Efficiency by Integrating

Knowledge Management into Production Workflows. ... 72

List of Tables

Table 1 Installed ERP System Used to Support The Quote-to-Order Process ... 49

Table 2 Prioritization of Process Areas for ERP, Analytics and Knowledge

Management Integration Investment ... 50

Table 3 ERP-based Analytics and Knowledge Management In Use ... 50

Table 4 What type of Analytics in Use? ... 51

Table 5 Implementation Strategy for Role-based Knowledge Management to support the

Quote-to-Order ... 52

Table 6 ERP-based Analytics Applications for Knowledge Management Use Case Analysis ... 54

Table 7 Quote-to-Order Measures of Performance by Process Area ... 70

List of Abbreviations

BPM

Business Process Management

CFO

Chief Financial

CIO

Chief

Information

Officer

CMO

Chief

Marketing

Officer

CRM

Customer

Relationship

Management

DDSN

Demand Driven Supply Network

DMAIC

Define, Measure, Analyze, Improve and Control

DOM

Distributed Order Management

DSO

Days Sales Outstanding

EDI

Electronic

Data

Interchange

ERP

Enterprise Resource Planning

- Finished

Goods

KPI

Key

Performance

Indicators

MDM

Master Data Management

NPDI

New Product Development and Introduction

POI

Perfect

Order

Index

ROI

Return

Investment

ROIC

Return on Invested Capital

SaaS

Software-as-a-Service

SOA

Service Oriented Architecture

TQM

Total Quality Management

VoC

Voice of the Customer

WIP

Work in Process

Chapter 1: Overview

Supply chain management in a global world is challenged by new technologies. Therefore,

the high speed with which changes take place at the Distributed Order Management (DOM) and

Enterprise Resource Planning (ERP) forces many companies to assume sales strategies for contract

manufacturing and mass customization. In the sales strategy for contract manufacturing, there are

different variants of assemble-to-order: from those where only a small percentage of the product

is customized individually, to engineer-to-order, where most of the features of a product are based

on customer specifications. Study results indicate that with greater accuracy and speed of data

integration, and collaborative agreement in the supply chain, the accuracy of execution increases

(Novack & Thomas, 2004). Performance criteria in this area of supply chain management and

DOMs are the Perfect-Order criteria (Hofman, 2004).

This research study is to determine how companies integrate knowledge management

systems in build-to-order processes to achieve a flawless execution. The benefits of knowledge-

management systems and their alignment with customer requirements and classification schemes

have an accelerating effect on the accuracy and agility of build-to-order systems. Measuring the

integration of knowledge-management systems and build-to-order processes using the Perfect-

Order criteria give insights into how the profitability of strategies of product customization can be

enhanced by streamlined operations of product definition, quoting, pricing, and order execution.

Background Study

Through disseminating products and services on the principle of mass customization,

companies urgently need to integrate their extensive knowledge-management systems into the

pricing, product configuration, quotation, and support activities underlying their sales activities.

All these platforms and systems for knowledge management for pricing, quoting, product

configuration, and deployment of services supply the catalyst for transactions and product

availability. The supply chain has been found to be the principal source of knowledge that must

penetrate the corporate structure, and ensure synchronization of companies and general market

requirements (Dyer & Nobeoka, 2000). To ensure the viability of build-to-order, configure-to-

order, and engineer-to-order strategies, a high degree of process and information integration in

knowledge-management systems across the supply chain is necessary. The better these supply

systems are integrated into the build-to-order systems, online product configurators, pricing

systems, and service systems, the higher the probability that all individual contracts can be

executed and delivered flawlessly. According to the Perfect-Order concept, close integration of

the different supply systems, sales systems, and platforms such as web-based build-to-order,

configure-to-order, and engineer-to-order, along with configurable and knowledge-management

systems at an appropriate synchronization, comply with or even undercut predetermined customer

delivery dates. The Perfect-Order criteria were originally designed to measure supply chain

performance (Hofman, 2004). In integrating the operational capacity, the efficiency of cash-to-

cash process flows, and a company's ability to forecast demand accuracy, the Perfect-Order criteria

are a reliable performance indicator in traditional environments.

Over time, the Perfect-Order criteria enable the online sales strategies of mass

customization, contract manufacturing (build-to-order or configure-to-order) and construction-

oriented production (engineer-to-order) to be incorporated into the business processes of

manufacturers. The company Dell was largely responsible for the successful concept of online

sales of build-to-order PCs. In fact, the annual revenue of several billion which Dell recorded after

the system reached a global scale led many companies to pursue extending their strategies of mass

customization and contract manufacturing (Fields, 2006).

Many high-tech companies are interested in establishing performance criteria based on

Perfect-Order-based strategies that are designed to implement their build-to-order, configure-to-

order, and engineer-to-order strategies as efficiently, and make the companies as profitable, as

possible (Fields, 2006). The Perfect-Order criteria were initially applied to the items with the

highest inventory turnover, which was a high commodification, as inventory turnover was crucial

for profitability. In these early platforms, knowledge-management integration aimed to facilitate

data integration for products and product combinations. Dell performed the integration of

knowledge-management systems in product configurations only gradually, because each system

uses a different data format, different product definitions, and completely different ERP systems

(Fields, 2006). Therefore, Dell often introduces new products only after considerable delays in the

market, and the online systems for catalog and order management often have to be completely

redefined, to compensate for the lack of knowledge-management support for the supply systems

of the enterprise (Fields, 2006). Many other companies have since experienced similar problems

in introducing new products which, in some industries such as the high-tech sector, make up to

60% of total revenue in a new product line. In the initial launch of its online strategy, Dell found

that a lack of integration of knowledge-management systems, and the inefficiency of the build-to-

order, configure-to-order, and engineer-to-order strategies, meant the online sales strategies were

severely impaired. After Dell had learned the importance of knowledge-management systems and

supply-chain data from the business model, the company chose a new approach to managing its

strategies of product adaptation (Fields, 2006). Dell applied the Perfect-Order criteria to their

specific business needs and developed a series of balanced scorecards and performance criteria to

evaluate how effectively the company integrated its knowledge-management systems and supply-

chain performance, and the accuracy of executing its strategy of mass customization.

The Perfect Order Index (POI) effectively contributed to promoting customer satisfaction

and gave the company a base for evaluating their performance in terms of customer satisfaction

(Columbus, 2008). The POI results showed increased customer loyalty to the company via repeat

purchases, and a relationship between increasing Perfect-Order performance and long-term

increase customer profitability was recognized (Columbus, 2008). This relationship of Perfect-

Order performance with long-term customer loyalty and profitability can be seen in the areas of

complex manufacturing, high technology, special vehicles, and medical products and services

(Columbus, 2008).

The index shows the effectiveness of the use of knowledge-management systems for highly

complex engineer-to-order products with high profit margins, and cost-effective standard products

sold only based on price and availability. The use of Perfect-Order criteria to measure the overall

performance of a Demand Driven Supply Network (DDSN) is also of great benefit, because a

common industry-wide base is provided, and the cross-industry integration of these systems can

be evaluated (Barrett, 2007). The implementation speed of a mass customization strategy is

directly affected by the extent to which the knowledge-management systems, from catalog

management to expert systems, are integrated into guided selling and complex engineer-to-order

processes (Columbus, 2008). This study is to determine how knowledge-management systems can

lead, with supply-chain data, to a more accurate long-term coordination of build-to-order,

configure-to-order, and engineer-to-order sales strategies via the channels selected by a company.

The possibility of predicting customer satisfaction and loyalty based on the respective level of the

POI will ultimately revolutionize these three order systems integration and real-time systems and

platforms development (Columbus, 2008).

Research Design

To determine the impact of integrating knowledge-management systems and build-to-order

applications with Perfect-Order performance, the coordination and collaboration of leading

members from the community of high-tech manufacturing is required. Therefore, contact with the

American Electronics Association and other professional organizations that measure key financial

metrics was considered. As this study was exclusively conducted online, contacting the employees

of these bodies and members of these associations regarding participation in this research program

was crucial for its success.

The participants in this research were the Chief Financial Officers or heads of finance

departments of manufacturing and service industries, as they know the process and frameworks

introduced to apply knowledge to build-to-order, configure-to-order, and engineer-to-order

strategies and feature data to calculate the POI for their businesses.

The web survey tool Zoomerang was used to create the survey and was sent via email as a

link. The survey includes a questionnaire that relates to several comparative studies on knowledge-

management integration and product-configuration systems (build-to-order components), and the

current and future use of performance indicators for the supply chain. As far as possible within the

framework of online survey tools, the survey also worked with graphics to ensure ease of use and

a better understanding. Respondents also indicated how they used scarce resources for introducing

a new product, or the importance of knowledge-management systems for making price adjustments

during the life-cycle of a product. In addition, the POI was included in the questionnaire to find

out whether the companies surveyed established a relationship between Perfect-Order performance

and customer satisfaction.

Research Questions

The purpose of this study is to understand the application of knowledge management to

optimize the build-to-order process in manufacturing and service companies. The overall research

question guiding this study is: How is knowledge management being used today to streamline and

make more efficient service strategies of manufacturing and services companies?

These questions critically analyze the metrics used by companies to verify their

knowledge-management systems' performance. They also assess how effective knowledge-

management systems are for preventing incorrect orders and increasing customer satisfaction.

These questions are relevant to the overall research objectives, and help assess the processes that

are used in applying knowledge management.

Summary

The dynamics by which the performance of build-to-order products is influenced by

knowledge-management systems, and is carried out via Perfect-Order criteria related to the

collaborative agreement in the supply chain, are an important predictor of customer satisfaction.

The causal relationship between these interactive systems and long-term customer loyalty and

significant satisfaction for all enterprises operating in business sectors with rapid inventory

turnover is crucial, especially for goods such as high-tech products.

Chapter 2: Literature Review

Creating more opportunities for revenue growth with build-to-order, configure-to-order,

and engineer-to-order strategies are predicated on a well-defined and highly-integrated series of

strategies that unify selling efforts and supply chains. The intention of this literature review is to

evaluate how the standard metrics of performance included in the hierarchy of supply chain metrics

are accelerated through the efficient use of knowledge-management systems and strategies.

This chapter addresses the research objective of evaluating the contributory effects of

knowledge management to mass customization and build-to-order strategies within a

manufacturing company. It also addresses the second research objective, namely, to determine the

causality in knowledge-management system integration with build-to-order and mass-

customization system performance when varying integration technologies are used, these ranging

from Electronic Data Interchange (EDI) to RosettaNet and real-time XML integration. It is

important to note that, regardless of how integrated or all-encompassing a given standard is for

data interchange, the one common denominator agreed on by most businesses is how to manage

transactions. This is a key takeaway from this literature review. This chapter also discusses the

third research objective, that is, to define a series of dashboard metrics that can be used for

evaluating the level of knowledge-management system contribution to custom configuration and

quoting accuracy across all channels a manufacturer sells through. Using the Hierarchy of Supply

Chain metrics shown in Figure 2 (Hofman, 2004), a maturity model for integrating knowledge-

management systems with product-configuration platforms that enable the build-to-order and mass

customization process is also defined and presented (AMR Research Report, 2005). Finally, this

chapter also analyzes a framework for quantifying the performance gains of integrating

knowledge-management systems and product-configuration strategies.

The ability of companies to orchestrate the many disparate, legacy, and, often, stand-alone

systems that are required to optimize the build-to-order selling and production processes is

predicated on how effectively the core concepts, frameworks, and strategies contribute to the

efficient and profitable performance of build-to-order selling and production strategies on a global

scale. These concepts and frameworks can serve to galvanize a strategy in the most effective and

profitable direction, and provide a series of relevant metrics and key performance indicators

(KPIs).

A critical catalyst in the theory of operations relating to build-to-order selling and

production strategies is the reliance on Six Sigma methodologies that take variation out of core

processes. The DMAIC (Define, Measure, Analyze, Improve, and Control) Model is extensively

used throughout build-to-order selling and production strategies, to ensure consistency of

processes and profitability by mitigating the costs of previously unpredictable processes. With

these metrics, a heavy reliance on the supply-chain hierarchy of metrics is evident from the

literature review, and also the need to quantify build-to-order strategies' performance over time.

Six Sigma and DMAIC models are used to validate the scalability and reliability of a process, and

the KPIs from the supply-chain hierarchy of metrics provide quantified evidence of performance

gains. One of the most critical metrics is also Perfect Order, which is also discussed throughout

this chapter. Taken together, all these metrics serve as a unifying, galvanizing catalyst that brings

together all aspects of the build-to-order production workflows over time.

In evaluating the role of metrics and KPIs throughout the build-to-order process, it is also

important to concentrate on the lag-time of these factors in quantifying overall strategy

performance. It is common for the most financially-based metrics and KPIs to take up to nine

months or a year to become evident from performance analysis (Columbus, 2008). On the other

hand, the metrics and KPIs that are supply-chain centric and focused on transactions are typically

visible within six months or less of initial strategy implementation (Hofman, 2004). Therefore,

the literature review in this chapter defines a theory of operation that seeks to explain the lag-time

in these financial versus operational measures of performance, and explains how companies'

performance across industries varies drastically, depending on how they approach this concept of

build-to-order selling and production strategies.

Defining Core Concepts and Frameworks for Build-to-Order Strategies

The most fundamental elements of build-to-order selling and production strategies are the

series of strategic goals and objectives on which they are based, followed by the balanced

scorecards and approaches to measuring, monitoring, and modifying strategy performance based

on the findings. Taken together, the series of metrics must all contribute to a strategic view of the

entire process and its contribution to the enterprise's profitability to be effective (AMR Research

Report, 2003). Inherent in the best practices of build-to-order selling and production strategies is

the design of analysis, evaluation, and continual improvement of each area of the process workflow

to ensure continual improvement. The reliance on Six Sigma techniques for continually

measuring, monitoring, and modifying the build-to-order selling and production strategies

illustrate how pervasive the use of quality management and continuous improvement

methodologies is in this area (Davison & Al-Shaghana, 2007).

The DMAIC Model is specifically relied on as a methodology to capture the process

performance of build-to-order, assemble-to-order, configure-to-order, and engineer-to-order

strategies and workflows. Six Sigma has been heavily relied on, because of the innate functionality

and flexibility of the DMAIC Model for quickly determining the inter-process performance from

the customers' standpoint, while considering the constraints under which a business operates

(Raisinghani, Ette, Pierce, Cannon, & Daripaly, 2005). The Six Sigma DMAIC framework

illustrates the framework as it relates to a lifecycle-based approach in managing customer-centric

projects and programs, as shown in Figure 1 (Raisinghani et al., 2005).

Figure 1. Six Sigma DMAIC Framework.

The Six Sigma DMAIC framework is extensively used as one of the core functional areas

of measuring build-to-order performance, given its integration with Voice of the Customer (VoC)

elements. While Six Sigma is often used as a means to measure just process-centric performance,

it can also be used for evaluating the customer-oriented application responsiveness and accuracy

(Davison & Al-Shaghana, 2007). The best DMAIC-based implementations throughout the build-

Six Sigma DMAIC Process

Develop Charter and

Business Case

Map Existing Process

Collect Voice of the

Customer

Specify Customer

Requirements

Measure Customer

Requirements

Determine Process Stability

Determine Process Capability

Calculate Baseline Sigma

Refine Problem Statement

Identify Root Causes

Quantify Root Causes

Verify Root Causes

Institutionalize Improvement

Control Deployment

Quantify Financial Results

Present Final Project Results

and Lessons Learned

Close Project

Select Solution (Including

Trade Studies,

Cost/Benefit Analysis)

Design Solution

Pilot Solution

Implement Solution

Define

Measure

Analyze

Improve

Control

DMAIC

efine,

easure,

nalyze,

mprove and

ontrol

to-order strategies are heavily reliant on a continual update from customers on how well their

experiences relate to their expectations, with Six Sigma measuring the difference between these

two values (AMR Research Report, 2003). The ability of Six Sigma to be used for evaluating how

effectively a quote-to-order strategy aligns with customer expectations is evident in how DMAIC-

based results are often used to define which supply-chain metrics are used to evaluate overall

business performance. The DMAIC Model is a means to define which metric in the hierarchy of

supply-chain metrics is best to quantify the customer satisfaction, quoting the entire build-to-order

strategy and its profitability (Davison & Al-Shaghana, 2007).

Studies also suggest the correlation between customer satisfaction and the profitability of

companies which continually improve their build-to-order selling and production processes are

especially strong in B2B markets (Raisinghani et al., 2005). This is because combining the

DMAIC framework, its metrics of performance as they relate to process performance and continual

improvement, and integrating supply chain metrics focuses a company's efforts towards creating

an exceptional customer experience. Combining the DMAIC framework factors, use of selected

metrics from the hierarchy of supply chain metrics, and the use of the VoC components of Six

Sigma can transform a build-to-order strategy from being static and outdated to one which exceeds

customers' expectations. The triad effect of these factors can transform business models over the

long term by continually exceeding customer expectations as well.

Later in the chapter, the financial implications of excelling at build-to-order selling and

production strategy are discussed for financial results. However, it is important to consider that

only by having a stable Six Sigma platform with DMAIC-based strategies, anchored in supply-

chain metrics, with metrics that quantify customer satisfaction, can any build-to-order selling and

production strategy continually improve and contribute profits over time. The quantifiable nature

of the process workflows that comprise build-to-order have just as much an effect on customer

satisfaction as they do on the financial performance of firms; this point will also be reviewed in

this chapter.

Theorists have argued the lag-time in the financial metrics of quote-to-order strategies is

primarily attributed to customer expectations being slow in translating into customer lifetime value

over time, often nine months or even a year into the standardization on a build-to-order strategy

(Barrett, 2007). Six Sigma and the DMAIC Model are the foundation for evaluating process

performance and its impact on operations, and when these approaches in evaluating performance

are augmented with the hierarchy of supply-chain metrics, they become orders of magnitude more

effective in streamlining the quote-to-order process over time (Raisinghani et al., 2005). Each of

the attributes, KPIs, and metrics shown in Figure 2 (Hofman, 2004) add another element of insight

into the DMAIC-based methodologies on which enterprises rely to streamline their quote-to-order

strategies and supporting processes. This is a critical step in applying knowledge management to

optimize the build-to-order process, as it brings insight, intelligence, and guidance into the

planning, execution, monitoring, and measurement processes surrounding this complex selling and

production strategy. Another aspect of selectively integrating metrics and KPIs from Figure 2

(Hofman, 2004) to increase the effectiveness of knowledge management in the build-to-order

process is based on the inherent strengths and weaknesses of a given business model. Many of the

metrics and KPIs provided in Figure 2 (Hofman, 2004) are applicable only to manufacturing-

centric businesses. Creating an effective approach in defining which of the metrics in the hierarchy

of supply-chain metrics are the most relevant to a given business model takes insight, and forward

planning over time.

Details

Pages
Type of Edition: Erstausgabe
Publication Year: 2017
ISBN (PDF): 9783960676164
ISBN (Softcover): 9783960671169
File size: 2.1 MB
Language: English
Institution / College: Fernfachhochschule Schweiz
Publication date: 2017 (February)
Keywords: Knowledge Management Build-to-Order Quote-to-Order Product Configuration Intelligent Manufacturing Distributed Order Management Enterprise Resource Planning Optimize
Product Safety: Anchor Academic Publishing

Author

Dr. Thomas Zachariah (Author)

Applying Knowledge Management to Build-to-Order Processes in Manufacturing and Service Companies

Summary

Excerpt

Table Of Contents

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Author

Dr. Thomas Zachariah (Author)