Case Study

Using AI models to optimize best-selling inventory.

About the client. A top destination for artistic minds of all backgrounds, our client sets the standard for creativity. With unique expertise acquired over three generations, the family-owned business inspires imagination and accompanies customers throughout their many projects.

How our client, a leading retailer increased profitability using real-time data and insightful reporting. To effectively compete in retail today, companies must give the customer what they are looking for. Our client partnered with KPI Digital to improve customer satisfaction and improve top-line sales.

Real numbers, real results.


Increase in online


Overall margin


Efficiency increase of
overhead operations


Increase in customer


Giving the client what they want.
Our client was challenged with satisfying customer expectations and preferences and balancing inventory and replenishment. Analytics suggested the company was losing approximately 25% of sales because selected items in-store and on-line often were out of stock. We worked with the retailer and enabled them to better understand each customer by matching supply and demand to improve top-line sales.

Using IBM Cognos helped our client to reduce human error and improve the efficiency of overhead operations.



We created a new data-mart to consolidate multiple data sources, including ERP, point-of-sale, warehouse inventory, online sales, and loyalty program information, into a single data repository.

The outcome was a collaborative planning, budgeting, and forecasting solution with a more sophisticated algorithm for replenishment. This improved solution enabled them to interpret seasonality and extreme sales with a friendly user interface and a long-term view of their business performance. We used the IBM Cognos Analytics Framework metadata tool to load data-mart data into models that then could be manually extracted into Excel for specific suppliers.

By automating this task and other manual processes, we enabled our client to reduce human error and improve the efficiency of overhead operations. This series of custom solutions enabled us to develop a deep understanding of their infrastructure, business culture, and financial objectives. In addition, solution improved customer satisfaction. Our client asked us for input on how to optimize warehouse inventory to address issues such as seasonality, the economy, changing customer preferences, gross margins, and product availability. We recommended adding predictive analytics, Al, and machine learning to produce more precise predictive models and gain insights into how inventory selection and customer behaviour contributed to sales. We advised them to implement an Al and automation solution.

Our strategy focused on developing seven statistical Al models in using existing data to create more accurate inventory forecasts. Using Machine Learning, these results forecast conditions for up to 24 months into the future. We also created a link to facilitate inventory planning, budgeting, and forecasting as well as a link to advanced analytics reporting. We provided actionable insights into market basket analysis, which enabled decision-makers to filter and track promotions and returns.

By using basket analysis, we provide real-time insights into our client’s product value, which is helping the company better manage sales overall and increase margins while controlling inventory costs.

Temporarily closing the flagship store and the effect on store inventory.
KPI Digital immediately responded and changed the ETL to predict valid product inventory when the store reopens. We created a User Interface (UI) to define the store that we could reference to feed the Al forecast. This allowed for classes and subclasses of products to enter sales from different stores based on percentage of adjustments, multiplied by the rate of expected clients when the store reopens.


Our client is continuously perfecting their internal expertise using analytics to develop actionable insights. Al models have helped them increase best-selling inventory and reduce slow-selling inventory – increasing profitability.

They are also focusing on advanced analytics to improve inventory margins. Precise product availability also is helping the company dramatically improve online sales. Online traffic to their eCommerce site is continues to rise and they were able to temporarily close and reopen the flagship store with minimal disruption and accurate inventory using Al and forecasting!