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  <title>DSpace Collection:</title>
  <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/2370" />
  <subtitle />
  <id>http://localhost:8080/xmlui/handle/123456789/2370</id>
  <updated>2026-06-23T06:33:54Z</updated>
  <dc:date>2026-06-23T06:33:54Z</dc:date>
  <entry>
    <title>Segmentation based product design using preferred features</title>
    <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/2871" />
    <author>
      <name>Gangurde, S. R.</name>
    </author>
    <author>
      <name>Akarte, M. M.</name>
    </author>
    <id>http://localhost:8080/xmlui/handle/123456789/2871</id>
    <updated>2020-12-12T11:06:47Z</updated>
    <published>2015-05-09T00:00:00Z</published>
    <summary type="text">Title: Segmentation based product design using preferred features
Authors: Gangurde, S. R.; Akarte, M. M.
Abstract: Purpose – The purpose of this paper is to present a systematic methodology for segmenting&#xD;
customers based on the preferred product features, its cost and worth, so as to facilitate the designer to&#xD;
develop a product that will simultaneously minimize product cost and maximize customer satisfaction.&#xD;
Design/methodology/approach – Post hoc – non-overlapping – non-hierarchical approach has been&#xD;
used for segmentation based on preferred product features by the customers. Allocation of product&#xD;
features to a particular segment is carried out by considering feature cost and customer worth for&#xD;
that feature. Automobile car has been selected as an example to demonstrate the methodology,&#xD;
where features data were collected from dealers and customer worth data were obtained by random&#xD;
generation method.&#xD;
Findings – Methodology facilitates creation of n number of homogeneous segments from a heterogeneous&#xD;
customer group based on the cost and worth of product features. Total product cost decreases though&#xD;
product variety increased due to segmentation.&#xD;
Originality/value – The proposed approach will help designers in segmenting (grouping) heterogeneous&#xD;
customers based on the preferred product features so that a most compatible (matching) product&#xD;
configuration for each segment, especially during product consolidation stage (beginning of the&#xD;
maturity phase of product lifecycle) can be developed to achieve maximum customer satisfaction.</summary>
    <dc:date>2015-05-09T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Optimization of hole-making operations for injection mould using particle swarm optimization algorithm</title>
    <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/2870" />
    <author>
      <name>Dalavi, A. M.</name>
    </author>
    <author>
      <name>Pawar, P. J.</name>
    </author>
    <author>
      <name>Singh, T. P.</name>
    </author>
    <id>http://localhost:8080/xmlui/handle/123456789/2870</id>
    <updated>2020-12-12T10:56:33Z</updated>
    <published>2015-06-17T00:00:00Z</published>
    <summary type="text">Title: Optimization of hole-making operations for injection mould using particle swarm optimization algorithm
Authors: Dalavi, A. M.; Pawar, P. J.; Singh, T. P.
Abstract: Optimization of hole-making operations plays a crucial role in which tool travel and tool switch&#xD;
scheduling are the two major issues. Industrial applications such as moulds, dies, engine block&#xD;
etc. consist of large number of holes having different diameters, depths and surface finish. This&#xD;
results into to a large number of machining operations like drilling, reaming or tapping to achieve&#xD;
the final size of individual hole. Optimal sequence of operations and associated cutting speeds,&#xD;
which reduce the overall processing cost of these hole-making operations are essential to reach&#xD;
desirable products. In order to achieve this, an attempt is made by developing an effective&#xD;
methodology. An example of the injection mould is considered to demonstrate the proposed&#xD;
approach. The optimization of this example is carried out using recently developed particle&#xD;
swarm optimization (PSO) algorithm. The results obtained using PSO are compared with those&#xD;
obtained using tabu search method. It is observed that results obtained using PSO are slightly&#xD;
better than those obtained using tabu search method.</summary>
    <dc:date>2015-06-17T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Benchmarking of Purchasing Practices using Kraljic Approach</title>
    <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/2816" />
    <author>
      <name />
    </author>
    <id>http://localhost:8080/xmlui/handle/123456789/2816</id>
    <updated>2020-12-03T06:18:09Z</updated>
    <published>2015-10-01T00:00:00Z</published>
    <summary type="text">Title: Benchmarking of Purchasing Practices using Kraljic Approach
Abstract: The purpose of this paper is to reduce impact on profit and supply risk, for&#xD;
strategic part by defining appropriate purchasing strategies using Kraljic portfolio model&#xD;
(KPM) approach</summary>
    <dc:date>2015-10-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Parameter optimization of Machining process teaching learning based Optimization algorithms</title>
    <link rel="alternate" href="http://localhost:8080/xmlui/handle/123456789/2815" />
    <author>
      <name>Pawar, P. J.</name>
    </author>
    <author>
      <name>Rao, V. R.</name>
    </author>
    <id>http://localhost:8080/xmlui/handle/123456789/2815</id>
    <updated>2020-12-03T05:57:34Z</updated>
    <published>2012-10-12T00:00:00Z</published>
    <summary type="text">Title: Parameter optimization of Machining process teaching learning based Optimization algorithms
Authors: Pawar, P. J.; Rao, V. R.</summary>
    <dc:date>2012-10-12T00:00:00Z</dc:date>
  </entry>
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