Customer Information Driven After Sales Service ... - RePub

2. After Sales Service
and Suanet (2005)). But as we observe in Figure 2.5, the extent to which companies
use these analytics varies significantly. Dekker (1996) and Shapiro (2004) have reviewed
and discussed the barriers to the use of maintenance optimization models in practice.
Dekker (1996) cites the gap between industry and academia, data related issues, and a
lack of decision support systems, as three major obstacles. In addition to these aspects,
Shapiro (2004) has studied the impact of the behavioral context of the decision making
situation in the effective use of supply chain analytics and optimization techniques. In
the next section, we focus on the various information related issues that companies often
encounter in practice.
2.6.2 Barriers to the Use of Analytics: The Information Aspect
In this section, we discuss the challenges the companies often encounter while implementing
any after sales service analytic or optimization solutions from the literature.
We specifically focus on the limitations imposed by information acquisition and management
issues.
In a broad sense, the limited application of supply chain analytics from an information
management perspective can be classified into two distinct reasons: 1) the cost of
technology adoption and 2) data quality issues. In this section, we focus on the role of
data quality issues as a barrier to the effective use of optimization models and analytics
in after sales service management.
Data Quality Issues
It is universally accepted that the wide ranging application of supply chain principles in
practice would not have happened without the developments in information and communication
technologies. Owing to the developments in the IT era, organizations nowadays
are enriched with all kinds of process, product and customer data. Simultaneously, the
issue of information quality has also received significant attention in the Information
Sciences (IS) literature. In this section, we first highlight the academic discussions in
the field of IS to handle data quality issues. Subsequently, we explore the supply chain
management and after sales service literature on the topic of data quality issues.
Data quality problems have received considerable attention in the IS literature. The
notion of data quality in the IS literature finds its roots in total quality management
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