Read how domain experts can interact with the analysis through browser-based data apps.
These are key decisions that many companies - particularly those in retail - have to make. An excess or shortage has a major effect on profitability and can cost retailers worldwide up to $1.1 trillion annually. Overstocking can lead to decisions like marking down the item’s price, which increases sales turnover. Having limited stock results in lost sales and dissatisfied customers who then purchase from the competition.
Inventory forecasting is a basic procedure for any business, particularly those in Consumer Packaged Goods (CPG). Stock, production, storage, delivery, and showcase – are all influenced by accurate inventory forecasting. However, an accurate forecasting model may not be everything that an organization wants. It may want to involve different stakeholders in the workflow. This is where KNIME partner Knoldus and their Knoldus Forecasting Platform (KFP), which is built using KNIME, comes into play.
With the KFP, data scientists create a model to forecast sales and tune it for accuracy. Decision makers then set parameters for the forecast based on their needs. The KFP is deployed on KNIME Server as a web application via the KNIME WebPortal. This makes it an easy to use tool for users who aren’t data science experts. Even without the technical details of how the forecasting works, they can customize the input and model parameters and visualize the result of each manipulation. Using inventory forecasting solutions to predict sales or stock consumption is not new. However, most organizations experience the following challenges:
To aid in solving these problems, KNIME Partner Knoldus built the Knoldus Forecasting Platform (KFP), a web application built using KNIME that allows decision makers and stakeholders to be as equally involved as data engineers and data scientists in creating a pipeline.
The KFP provides several advantages over historical forecasting solutions:
With this Guided Analytics application, companies can create data visualization dashboards and inventory forecasting models as well as generate forecasting for their business intelligently and collaboratively by:
Once the reports and visualizations are generated, data scientists, business users, and domain experts can collaborate on the final results.
INSERT IMAGE KNIME WORKFLOW FOR INVENTORY FORECASTING
Download workflow from KNIME Hub.
The free and open source KNIME Analytics Platform made the development, access, and management of this solution very easy due to the seamless integration with other technologies. For example the JavaScript Extension and Python Integration.
The range of customizable KNIME core nodes for data transformation helped in making tedious pre-processing and data cleaning tasks much simpler without needing to manipulate code. On top of the core transformation nodes, KNIME also provides nodes specific to time series data. The machine learning nodes provided assistance in training different models to compare their accuracy for best performance.
Once the solution was ready, it was very simple to deploy to KNIME WebPortal via KNIME Server to create a powerful web application. This enabled domain experts and decision makers to become part of the process.
This Innovation Note is available here as a PDF.
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