Customer Information Driven After Sales Service ... - RePub

2.6. Information Management in After Sales Service
(TQM) concepts of quality, i.e. fitness for its purpose. In the IS literature, data quality
is defined as a representation of various ontological characteristics of data (Wand and
Wang, 1996; Mallach, 2000). Some of these ontological aspects of data quality read as
follows: completeness indicates to what extent a data set contains all necessary values
(Ballou and Pazer, 1985). Accuracy is defined as the degree of agreement between an
observed value and an accepted reference value. Timeliness is an aspect that characterizes
whether the current data set or data value is out of date for its intended use.
In the IS literature, many researchers have attempted to devise a framework to rank
the impact of these various quality dimensions (Ballou and Pazer, 1985; Wang and Strong,
1996; Lee et al., 2002). These survey based studies are limited from an after sales service
perspective due to the following reasons: first, the surveys were performed mainly on IT
professionals who are not the end users of the data; second, the contextual implications
of the decision making situation are not considered during the rank assessment.
Data quality issues have received much less attention in supply chain management or
after sales service literature. Before we discuss the academic literature on data quality
in after sales service, we intend to highlight the type of data quality issues that are
encountered in practice. The sectoral studies by the Aberdeen group and Accenture
reveal the following data quality issues in after sales service (Gecker and Vigoroso, 2006;
Vigoroso, 2003; Dennis and Kambil, 2003).
• Lack of appropriate visibility into product information, inventory information, contract
information and customer location information.
• Disparate and incompatible data sources and decision support systems.
• Inconsistent naming conventions.
• Lack of supply chain wide integration.
These aspects hinder the deployment or continuous use of optimization solutions. Either
the solution is not deployed at all or even if the solution is deployed, the confidence
in the optimization solution’s performance is seriously undermined due to a “Garbage
In-Garbage Out” perception. In general, there are two primary factors that account
for data quality issues in after sales service, i.e. lack of appropriate technology adoption
and human-machine interaction. Despite the developments in IT, the cost of firm-widetechnology
adoption is still high. Many companies (large or small) are unable to reach a
47