Table of Contents
Chapter 1:
Introduction__________________________________________________3
Chapter 2: Literature
Review_____________________________________________ 6
Chapter 3:
Methodology_________________________________________________16
3.1
Step One: Performance Metric Collection ___________________________16
3.2
Step Two: Identify Information Requirement_________________________19
3.2.1
Task One: Identification of Use Cases_______________________19
3.2.2
Task Two: Identification of Classes and Attributes_____________22
3.2.3
Class Diagram_ ________________________________________23
3.2.4
Information Requirement_________________________________24
3.3
Step Three: Implementation______________________________________ 25
3.3.1
Relational Database_____________________________________25
3.3.2
Introduction to Online Benchmarking System: The Web Interface 32
3.3.3
Performance Analysis Based on SMART____________________34
Chapter 4: Website Documentation_______________________________________47
4.1 Purpose
of Documentation_______________________________________47
4.2
What you need to know_________________________________________47
4.3
ASP Code Structure____________________________________________48
4.4
A Walkthrough of Online SMART Application______________________52
4.5
ASP Databases________________________________________________ 57
4.5.1
ASP _________________________________________________58
4.5.2
ADO_________________________________________________59
4.5.3
The
4.6
System Evaluation_____________________________________________ 63
Chapter 5:
Conclusion__________________________________________________64
Future
Research__________________________________________________65
Appendix A: Performance Metric
List_____________________________________66
Appendix B:
Questionnaire______________________________________________82
Appendix C: Sample survey/Metric
Evaluation Package_______________________100
Appendix D: Feedback from Survey
Evaluation______________________________110
Appendix E: Use Case
List_______________________________________________124
Appendix F: Classes and
Attributes________________________________________129
Appendix G: Snap shots of Online
Benchmarking System______________________ 135
Reference____________________________________________________________142
CHAPTER 1: INTRODUCTION
In order to remain competitive, transportation service providers, such as trucking firms, must constantly evaluate their operations, management structures, information systems, and customer relations with respect to their competitors. One of the standard approaches used by industry to evaluate performance is known as benchmarking. Benchmarking is a process by which companies determine the best practices that lead to exceptional performance; however, benchmarking is a complicated and time-consuming process. First, the data necessary to support a benchmarking analysis can be difficult and time-consuming to obtain. For example, companies may be reluctant to share the data necessary to determine best practices because of proprietary or competitive concerns. Second, performance metrics required to perform the benchmarking can be difficult to determine and difficult to interpret their relevance to best practices. Third, finding the best practice in a subject area can be a lengthy task that requires expertise and analysis techniques. Fourth, communication with benchmarking partners must be effective and require full cooperation from all parties. As a result, a system is needed that can aid in the process of benchmarking. The system or tool should partially or completely solve the problems mentioned above. In particular, the system should allow the users to track the performance metrics in specific subject areas, such as customer service or product service performance. Since users are interested in identifying best practices and performance metrics, the system should facilitate the data collection process within these subject areas. In addition, the system must be designed in a user friendly way so that users can quickly apply and learn the system. In turn, communication of all parties involved is improved because the system allows a systematic approach to benchmarking.
There are many systematic ways of conducting a benchmarking study; however, an “online benchmarking” system is one of the innovative ways that one can use to benchmark a transportation company. This research examines the development of an on-line benchmarking system to facilitate the benchmarking of transportation providers. We have implemented the system in prototype form as an “on-line benchmarking system (OBS)”. In particular, the system allows transportation providers to recommend performance metrics, rate their importance, classify the metrics into service categories, and develop other specifications for a performance measurement system. In addition, the system allows for the on-line collection of performance metric values through an on-line survey process. The data is collected and stored in a database for later benchmarking analysis against other participating benchmarking parties in an anonymous fashion. Finally, the system facilitates best practice analysis through a multi-criteria process based on a balanced score card approach. The metrics are categorized according to the four categories in the balance scorecard approach to performance measurement.
This project addresses the need for timely, accurate and comprehensive information concerning the performance and capabilities of transportation and local delivery providers in the form of an on-line benchmarking database. In addition to an on-line benchmarking database, this project examines the development of innovative technologies and methodologies to allow the interactive analysis of transportation providers compared to best practices.
Benchmarking surveys
are one of the most popular techniques used to collect performance data. For
instance, a transportation provider may perform a survey to examine some
specific operations, such as, customer relations, delivery operations, or
e-commerce strategies of peer-group companies. In this project, we investigate
the development of a benchmarking system with online capabilities to facilitate
benchmarking survey and data collection. The system will not only contain
information from the survey but will also contain information about the
surveys, such as performance metrics, survey questions, subject areas, etc.
The primary goal of this project is to support innovation and dissemination of knowledge within the area of benchmarking analysis for transportation carriers. The following are contributions of the project.
§ Standardized performance metrics
for transportation carriers within all areas of operation.
§ Innovative tools for the display and comparison of benchmark results to
indicate the performance of Transportation Company.
In this report, we present the details of the collected performance metrics and the structure of the online benchmarking system in the context of trucking carriers. In addition, an online application based on Simple Multi-Attribute Rating Techniques (SMART) will be discussed in full. SMART is the method that we use to prioritize the performance of carriers. We begin with a review of literature relevant to the benchmarking of transportation service providers.
CHAPTER 2: LITERATURE REVIEW
According to Zivan (1992), then Xerox's vice president for logistics and distribution, a pioneer in developing benchmarking processes, defines benchmarking as the heart of the planning process for any company that holds customer satisfaction as its highest priority. Indeed, benchmarking is an integral part of any quality improvement process. Performance metrics are used to indicate the performance of an organization within a benchmarking analysis and within performance measurement systems.
According to Watson, et. al (1999), performance metrics can be defined as the analytical tools in the performance measurement process that take measurements, display results, and determine subsequent actions. A specific value of a metric indicates the performance of a specific area in an organization. There are many performance metrics that are important to a company’s operation. In general, an enterprise will have hundreds of potential performance metrics to be incorporated into their performance measurement systems. Because of the large number of potential performance metrics, it is often very useful to classify the metrics into subject areas. For example, Watson et al. (1999) proposed a comprehensive logistics performance framework and a best practice template in their work. A total of one hundred and twelve metrics were identified and categorized into four groups, which were “cycle time”, “quality”, “financial”, and “resource”. In addition, a list of eighty-two best practices was complied from two hundred and sixty six best practices that matched the appropriate metrics. In addition, their work “identifies benchmarking that links qualitative values (best practices) with quantitative measurement (performance metrics), across the value chain” (1999).
2) The identification of critical success factors or critical performance metrics (financial and operational metrics only) that directly affect the performance of companies.
3) The identification of a performance metrics list that are related to the financial and operational aspects of the company.
4) The identification of relationships between financial and operational performance metrics.
In order to measure performance, three different scores are used to distinguish successful and less successful companies. The scores are named BRAVO-1, BRAVO-2, and BRAVO-3. “BRAVO-1 is a score in the long term and consists of ratios concerning the growth, productivity, solvency and profitability of the company. BRAVO-3 is a score to measure the operational performance at segment level. This score is defined as the turnover per segment (or category) in proportion to the relevant costs in the segment. BRAVO-2 score is derived from the BRAVO-3 scores. This is done by taking the weighted sum of the BRAVO-3 scores over the segments in which the company operates. Generally, the authors use a weighting method to calculate the BRAVO score. However, no detail on the weighting method is given.
In the BRAVO project, the authors segmented or categorized all of the 150 companies into three different groups, which were “transportation”, “distribution” and “warehousing”. In addition, trucks were divided into general container trucks and specialized trucks. As a result, performance data (financial, operational) was collected according to the segmentation (categorization). Based on the segmentation of the trucking companies, the authors constructed two models, which were the “Transportation” model and “Distribution Model”. Both models attempted explicitly to identify the relationship between financial performance metrics and operational performance metrics. The models were constructed with performance metrics arranged hierarchically. In addition, mathematical relationships were primarily used as the indicators of the relationship that existed among the various metrics.
1) Perform a one-factor correlation analysis on all the measures to find out which operational measures are strongly associated with financial measures.
2) Compare the average value of the operational performance measures for the companies that are performing well.
3) Find the operational measures that have the highest impact</