A UK-based multinational consumer goods company delivers health, hygiene and nutrition products to a global clientele through a complex supply chain network of 40,000+ retailers. They have also partnered with an ecommerce giant to push sales across product lines.
The ecommerce company’s SLA mandated adequate inventory stocking at any point of time and failure to do so would invite heavy penalties.
Legacy systems threw up inaccurate demand and sales forecasts and challenged the company’s ability to respond to fluctuating market conditions. COVID further disrupted stock availability, and adversely impacted the consumer goods company’s customer experience and online sales.
The consumer goods company hence partnered with Hitech to create an intelligent demand sensing and sales forecasting solution to:
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The team at Hitech Analytics designed Machine Learning-based demand sensing solutions to process voluminous data, provide algorithmic analysis and deliver intelligent supply chain related insights.
The solution focused on:
In the initial phase, our data specialists interacted with the company’s product and forecast managers to identify the actual gaps in their existing analysis and data models. They concluded that:
The client shared huge datasets on total inventory, number of daily site visits, number of people visiting the company’s portal on the ecommerce site, customer reviews on individual products, etc.
Our team changed the time series approach to a regression one. As algorithms were trained on specific datasets, we also changed existing parameters to lag parameters for data analysis. It helped us to establish a correlation between time duration and parameter’s impact.
We used Machine learning and regression algorithms like XG Boost and decision for accurate forecasts for shorter time periods.
Python, Excel and ML-algorithms
Using a cognitive demand sensing solution for various markets, the consumer goods company was able to:
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