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| − | <div style="background: #d9d9d9; padding: 12px; font-family: Impact; font-size: 18px; font-weight: bold; line-height: 1em; text-indent: 15px; border-left: #595959 solid 32px;"><font color="black">Limitations</font></div>
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| − | <tr><td width="10%" border="1" align="center">1</td>
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| − | <td width="45%" border="1>Data provided from the WMS might not be accurately representative of all transactions due to human error (miss scanning of barcode), lost / misplaced SKUs etc.</td>
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| − | <td width="45%">As the data size provided is large, analysis conducted on it will still be sufficiently accurate.</td></tr>
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| − | <tr><td width="10%" border="1" align="center">2</td>
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| − | <td width="45%" border="1>Current data analysis will only be conducted on 2 company products that the logistics company is handling.</td>
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| − | <td width="45%">The same analysis can be replicated for other company products as they will share some similarities in the nature of warehouse processing.</td></tr>
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| − | <tr><td width="10%" border="1" align="center">3</td>
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| − | <td width="45%" border="1>The project scope analyses the nature of warehouse processing of SKUs in Singapore. However, there are also other warehouses of the logistics company which are located outside of Singapore. They may have a different approach in warehouse processing.</td>
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| − | <td width="45%">Although the warehouses of the logistics company are located in different geographical locations, they share the same warehouse processing system. This thus allows the possibility of the team’s application to be replicated easily into them, should the logistics company choose to integrate their WMS with the team’s project application.</td></tr>
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| − | The complication of big data resonates strongly with the company, much like many others. The Warehouse Management System (WMS) captures data of logistics supplies in the warehouses of the company, such as quantity of movement, time of movement and identification codes of the packages stored. With extensive data provided by the WMS, analyzing of spreadsheets can be cumbersome and inefficient due to visualization impediments. The company is unable to effectively analyze and sculpt solutions as a result. <br/>
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| − | In addition, the company is in the midst of implementing a vertical lift high-tech system for its new warehouses in its upcoming Supply Chain City (SCC) project. In light of these recent developments, the company saw an opportunity to better analyze their WMS data to determine its Stock Keeping Unit (SKU)’s inbound rate, outbound rate, warehouse utilization trend, and ideally, by performing the aforementioned analysis, categorizing each SKUs into namely A, B, C categories. ‘A’ category refers to fast moving SKUs while ‘C’ category SKUs refers to slow moving SKUs. <br/>
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| − | With the analyzed results, the company hopes to determine the optimal warehouse location and vertical lift in which a particular SKU can be placed for picking. In addition, the company would also like to see the extent of productivity savings it can obtain with the adoption of a batch picking technique instead of an order picking technique. Order picking involves going into the warehouse to collect the supplies per order basis, while batch picking involves collecting for multiple orders in a batch.<br/>
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