Forecasting and optimisation of stock levels
Big data and predictive analytics can be used to optimise inventories and predict demand
By combining data science and big data, demand forecasts can be made much more efficiently.
Stocks can be optimised.
The results provide potential for reducing disposal costs.
Long-term parts requirements for parts relevant to driving is extremely complex, as spare parts must be kept in stock for up to 15 years. Therefore, too many parts are often ordered and have to be scrapped after the 15-year storage period. To calculate order quantities, the products are only considered within their product families. There is no information about correlations beyond product families, such as brakes and electrics.
Based on the current process, current methods and existing technologies, a new concept for a optimised ordering process developed.
Calculation of correlations between the quantities ordered in stock of different material numbers from different product families.
The correlations of the order quantities are shown in a QlikView app visualised to verify the results.
Through the combination of Data Science and Big Data can Demand forecasts be created much more efficiently. Over one billion different combination possibilities are identified as savings opportunities (out of 900 billion). The results provide potential for optimising inventories and reducing disposal costs.
Are you interested in your own use cases?
An automotive company would like to visualise various market-specific data in order to create a Competitive analysis for the US market.
There will be a interactive and Flexible application, including of different maps with two different views implemented.
Relevant markets are identifies, analyses and visualises. The dealer or the respective sales department have the possibility to compare the direct competition with their own product and to visualise the relevant data.
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