Quality analysis for data in route optimization

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1 Introduction

1.1 Background

In the first quarter of 2009, Itella Oyj, the company in charge of postal services within Finland, initiated a project to optimize their delivery routes in both Early Morning Delivery and Daily Mail Delivery. The main goal of this project was to make more efficient the delivery processes due to changing trends in demands of conventional methods of information and mail communication. Adapting to these changing trends meant more than just the maintenance of financial growth but also conformation to higher standards for achieving a greener environment.

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Previously, route measurement had being done by regional planners who used conventional means of route measurement. This meant physically travelling to the various regions and measuring and calculating distances according to set parameters. This project intended to streamline this process by adaptation of new systems and integration with already existing ones which we shall look at later.

1.2 Purpose of the thesis

However, as it is with many new company projects in their initial stages; challenges are inevitable all sorts of problems are likely to be encountered. One of the major issues faced in the project is ensuring that the data used in the process of route optimization is in tip top shape. Problems with quality in the data have resulted in nearly sabotaging obstacles to the optimization process. These problems have resulted in delays in schedules and thus increase in costs for the company. There have also being high unbudgeted costs due to corrections of faults.

The purpose of this thesis is to examine and analyze current quality for data used in route optimization, and possibly formulate quality standards from the analysis done. The research questions are therefore broadly divided into four as listed below;

  • What is data quality?
  • What is the importance of certain quality levels for the company?
  • What are the methods used to describe and analyze data quality and data quality process?
  • What is the current level of quality and have the resources invested being worthwhile?

The above questions give a guideline of the issues we shall look more deeply into in this paper.

2 Literature Review

2.1 Quality

2.1.1 Introduction

This chapter captures the definition of quality and more specifically data quality and its importance to a company’s processes and benefits of good quality as well as issues that require paramount concern with regard to maintaining good Quality. The line between the data quality and process quality as we shall see is pretty thin and therefore these two elements shall be referred to many times in this document.

2.1.2 Defining Quality

To be able to understand data quality,

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