Effective benchmarking requires a frame of reference from a wide group of best-practice
warehouses for the measurement of performance. The primary hindrance for the implementation
of this method is gathering sufficient data for characterizing the best performance since
companies are sensitive for data-sharing, especially for proprietary information about a firm’s
operations or financials. However, due to the development of Internet technology the problem of
collecting data can be solved by Internet performance measurement tools. An ongoing
collaboration between academia and the warehousing industry has laid the foundation of the
iDEAs-W tool for Internet benchmarking by which through online collection and maintenance of
data, firms could get both individual firm evaluation, and industry-level trends. This
benchmarking tool can provide efficiency estimates, gap analysis (pie charts describing the
connection between partial productivity analysis and the efficiency estimates), and practice and
attribute information for the efficient production processes identified as benchmarks (Johnson,
Chen, and McGinns 2009).
Before introducing benchmarking, the organization should conduct a research to identify which
Key Performance Indicators (KPIs) and Key Performance Measurement (KPMs) to apply. This
will lead to the identification of performance gaps between the companies as well as to the
identification of the enablers of better performance of the leading companies. The results should
be applied for the sake of adaptation and improvement. (Anonymous, www.best-information.eu;
Watson 1993).
Despite appealing, this approach carries drawbacks. Firms may lack the analytical personnel or
tools to identify the best/worst performance, or proprietary firms might not be willing to share
information and by this, data collection or limitation problems may appear. Furthermore, in order
to ensure confidence that the industry-level benchmarking has identified the best/worst
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performance, a large enough data for a peer group is necessary, and the collection of this data
might be difficult