Customer Information Driven After Sales Service ... - RePub

2. After Sales Service
critical volume in after sales service business to make the technology adoption feasible.
The high costs of sophisticated IT solutions force companies to compromise, thereby
using no IT solution at all or settling for a less sophisticated solution. Often, companies
attempt to extend the use of their MRP system from manufacturing to after sales service.
MRP, or ERP systems, are tailored for high-volume, replenishment driven production
environments (Cohen et al., 2006). Although such actions save costs, they result in a
technology solution that is unable to capture the requisite details of the after sales service
system. As a result, either the required data are simply not available (no IT solution
in-place) or the data are available only at an aggregated level, which does not provide
the necessary level of visibility. Data quality issues do not disappear with appropriate
investments in IT solutions due to contextual aspects of data acquisition and management.
For example, aspects of inconsistent naming conventions or incorrect or vague
information originate due to human or systematic errors. We shall further discuss these
aspects in Section 3.6.1.
Discussion of data quality in the after sales service related academic literature is
scarce. In the area of network design or facility location, Ballou (1994, 2001) has discussed
the effects of data quality variations (in location information and transportation cost /
time information). In the broader context of supply chain management, Daganzo (1996);
Rogers et al. (1991); Korhonen et al. (1998); Fisher et al. (2000); Cachon and Fisher
(2000); Proudlove et al. (2007); Thonemann (2002) have discussed data quality issues in
inventory planning, production planning, forecasting, and service planning. Since, the
authors studied sectors other than after sales service, these studies do not accommodate
some of the primary characteristics of after sales service (such as customer heterogeneity,
varying service levels, slow moving demand rates, distribution network structure, and
field service).
We now conclude our discussion on the academic and practice literature for after
sales service. In the next section, we present a case study on after sales service practices
at IBM. As mentioned earlier in Section 1.3.3, after sales service operations at IBM
and more specifically IBM spare parts logistics is a frequently cited case in academic
literature as the state of the art in after sales service operations (Cohen et al., 1990;
Hammond and Dutkiewicz, 1993; Cohen et al., 1997; Fleischmann et al., 2003; Tang,
2005; Draper and Suanet, 2005; Candas, 2007; Kutanoglu, 2008). We should note our
objective from this case study is to present an illustration of business context in which
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