Analytics Driving Supply Chain Segmentation for Lenovo
Although segmentation strategies and their benefits are common topics in the academic literature, two realities contrast in the realm of supply chain management: While a vast number of companies still perceive operations as cost-centers, only a few have adopted profit-driver approaches. The second g...
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2019
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| Online Access: | https://hdl.handle.net/1721.1/121297 |
| _version_ | 1826208186655309824 |
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| author | Gosling, Luiz Urrutia, Javier |
| author_facet | Gosling, Luiz Urrutia, Javier |
| author_sort | Gosling, Luiz |
| collection | MIT |
| description | Although segmentation strategies and their benefits are common topics in the academic literature, two realities contrast in the realm of supply chain management: While a vast number of companies still perceive operations as cost-centers, only a few have adopted profit-driver approaches. The second group embraced segmentation to deploy end-to-end supply chain strategies that clearly match the segments’ requirements to the company’s capabilities, adding sustainable value in the process.
Lenovo Data Center Group (DCG), sponsor of this project, proposed an assessment of their Hyperscale Business Unit’s supply chain and how they could better serve their client portfolio. With that aim in mind, this capstone had three goals: (i) review current frameworks present in the supply chain segmentation literature; (ii) identify DCG’s key client and product groups in its portfolio through quantitative methods; and (iii) propose specific supply chain policy guidelines for each identified group, creating baselines for change.
We applied a clustering model to process DCG’s sales and operations records to not only identify clusters of clients and products, but also quantify their differences in terms of supply chain. Data was preprocessed and then ran through an EFA for dimensionality reduction without losing traceability for insights and discussions. Two dimensions were identified: Importance and Complexity. k-Means clustering algorithm was then applied on the resulting dataset, and four client-product clusters were identified. With results in hand, a workshop was conducted with experts from DCG to explore a policy-cluster framework and understand how policies are set according to products' and clients' nature.
Our analysis shows that four segments exist within DCG’s portfolio and operating segmented supply chains is likely to positively affect performance. A workshop identified guidelines behind distinct supply chain policies for each cluster and provided a framework for segmented strategies’ design, thus helping managers rethink supply chains to better fit given segments. Moreover, the framework enables a data-driven approach to segmentation, which can be deepened with further analysis – for example, micro-segmentation of clients. To this end, we believe that our work has significant practical implications for Lenovo DCG. |
| first_indexed | 2024-09-23T14:01:52Z |
| id | mit-1721.1/121297 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T14:01:52Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | mit-1721.1/1212972019-06-15T03:01:17Z Analytics Driving Supply Chain Segmentation for Lenovo Gosling, Luiz Urrutia, Javier Strategy Although segmentation strategies and their benefits are common topics in the academic literature, two realities contrast in the realm of supply chain management: While a vast number of companies still perceive operations as cost-centers, only a few have adopted profit-driver approaches. The second group embraced segmentation to deploy end-to-end supply chain strategies that clearly match the segments’ requirements to the company’s capabilities, adding sustainable value in the process. Lenovo Data Center Group (DCG), sponsor of this project, proposed an assessment of their Hyperscale Business Unit’s supply chain and how they could better serve their client portfolio. With that aim in mind, this capstone had three goals: (i) review current frameworks present in the supply chain segmentation literature; (ii) identify DCG’s key client and product groups in its portfolio through quantitative methods; and (iii) propose specific supply chain policy guidelines for each identified group, creating baselines for change. We applied a clustering model to process DCG’s sales and operations records to not only identify clusters of clients and products, but also quantify their differences in terms of supply chain. Data was preprocessed and then ran through an EFA for dimensionality reduction without losing traceability for insights and discussions. Two dimensions were identified: Importance and Complexity. k-Means clustering algorithm was then applied on the resulting dataset, and four client-product clusters were identified. With results in hand, a workshop was conducted with experts from DCG to explore a policy-cluster framework and understand how policies are set according to products' and clients' nature. Our analysis shows that four segments exist within DCG’s portfolio and operating segmented supply chains is likely to positively affect performance. A workshop identified guidelines behind distinct supply chain policies for each cluster and provided a framework for segmented strategies’ design, thus helping managers rethink supply chains to better fit given segments. Moreover, the framework enables a data-driven approach to segmentation, which can be deepened with further analysis – for example, micro-segmentation of clients. To this end, we believe that our work has significant practical implications for Lenovo DCG. 2019-06-14T20:42:29Z 2019-06-14T20:42:29Z 2019 https://hdl.handle.net/1721.1/121297 application/pdf |
| spellingShingle | Strategy Gosling, Luiz Urrutia, Javier Analytics Driving Supply Chain Segmentation for Lenovo |
| title | Analytics Driving Supply Chain Segmentation for Lenovo |
| title_full | Analytics Driving Supply Chain Segmentation for Lenovo |
| title_fullStr | Analytics Driving Supply Chain Segmentation for Lenovo |
| title_full_unstemmed | Analytics Driving Supply Chain Segmentation for Lenovo |
| title_short | Analytics Driving Supply Chain Segmentation for Lenovo |
| title_sort | analytics driving supply chain segmentation for lenovo |
| topic | Strategy |
| url | https://hdl.handle.net/1721.1/121297 |
| work_keys_str_mv | AT goslingluiz analyticsdrivingsupplychainsegmentationforlenovo AT urrutiajavier analyticsdrivingsupplychainsegmentationforlenovo |