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Industry: Manufacturing
Solution: Analytics Hub

Client Profile:

Client is a national manufacturer of precision sheet metaling.

Client Challenges:

Due to several disparate data sources, the client faced major issues in relation to forecasting and meeting product demand for each of their major customers. Without visibility into an integrated demand forecast by part number and by week, the client lacked the ability to allocate production schedules to meet critical delivery dates, which adversely affected production efficiency and client satisfaction.

Messina Approach:

After a series of discussions with the supply chain and production support managers, Messina Group quickly recognized that the client lacked a consolidated production forecast that spanned across all customers. The first step in solving this issue was creating a comprehensive data platform that integrated demand forecasts from each of their major customers, along with inventory production schedules that provided figures for On-Hand and In-Process quantities.

Messina Group then created custom ETL processes to combine demand forecasts from each of the major customers with the production data from JobBoss, the client’s shop management solution. By utilizing the pre-built EDW design features of the Analytics Hub, Messina Group rapidly developed a custom, cloud-based EDW and flexible dimensional models to power self-service reporting and analytics.

To place these rich insights into the hands of the production support and supply chain managers, Messina used Microsoft Power BI to create robust Inventory Management dashboards and custom work-spaces.


Within weeks, the client was already reaping heavy benefits from the Analytics Hub solution, as quicker, faster, and more accurate decision-making became commonplace throughout the organization. Whereas once there was no clarity into the consolidated inventory demand, now supply chain managers had instant visibility into looming issues with production and supply.

Furthermore, production managers could quickly allocate production schedules towards inventory with lower forecasted balance. Sales representatives also began sharing these insights directly with their customers, increasing customer satisfaction by improving inventory delivery, timeliness and notifications.