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Less than a year ago, Deloitte’s management had been contacted by a major client. The client owned a retail business which involved over 30,000 products. One of these products are batteries with specific commodity codes provided by suppliers. These codes had to be checked manually for around 600 new products every month. Adding to that, other information needed to be added as well, such as such as the battery tax that applies in Belgium for products containing batteries. therefore, the client requested help from Deloitte in auditing the information entered by human staff members.
(Deloitte, 2018, p. 15)
Here comes SONAR in this task of automating the information and checking whether it is done correctly or not. SONAR is a tool that predicts the possibility of entered information relating to Value Added Tax (VAT) concerning the commodity codes on each battery. For the tool to work properly, the client provides Deloitte a data file holding many details. Starting with, the commercial product description, the VAT rate, the commodity code and a sign of whether each local charge applies through the process.
Also containing such as, the barcode and other data that can support with understanding the nature of the product. (Deloitte, 2018, p. 15)
The job of SONAR is to compare the information provided by the client to use the customs database containing all commodity codes, a textual description for each commodity code, and the applicable rate of VAT. After making a comparison, the results are calculated in a percentage to show the probability that the label added by the client is accurate.
Deloitte team visited a random shop with the client together and carried out a random check on a shelf of bicycle lights to examine the SONAR tool. After conducting a test on several bicycle lights, SONAR tool showed that something was somehow wrong or error concerning the battery tax. Afterwards, it turned out that it was a small battery included in the packaging after deep inspection, even though it wasn’t included in the description after the bicycle light label. Thus, it was a surprising experiment that something like this tool to have an immediate positive result. (Deloitte, 2018, p. 15)
In the end, SONAR was developed for a client, that mainly works well with databases containing at least 2,500 products, and a reference database must be available as well. but Deloitte team have confidence in the technology that is basic enough to be applied for other different problems. Moreover, SONAR allows a client to examine the information entered by humans is way more quickly and accurately. Finally, the good part of this technology is, the more one uses it, the more product information that becomes available, the more accurate the results will be. (Deloitte, 2018, p. 15)
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