Highlights
- Reduced significant waste in manufacturing and inventory teams, leading to significant financial savings.
- Saved days of manual work every week through automated analytics and reporting processes.
- Developed over 20 automated BI reports, enabling real-time data-driven decisions across various business units.
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The Problem
A leading food manufacturer in Australia was facing several challenges related to data management and decision making. The company did not have a single source of truth for their data, as it was scattered across disparate systems. This made it difficult and time-consuming to manually compile the data and create reports. The lack of a unified and efficient data management system made it challenging for the leadership team to run the business as effectively as they knew it could be run if they had good data, analytics, and automated reporting.
The Opportunity
The challenges faced by the food manufacturer presented an opportunity to create an automated analytics and reporting solution that could consolidate their data into a single source of truth. This would not only streamline their data management processes but also enable them to make data-driven decisions in real-time. The goal was to transform the food manufacturer into a data-centric organisation, enhancing their IT and Data capability, and ultimately improving their operational efficiency and effectiveness.
The Solution
Mantel Group, in partnership with Microsoft, developed an automated analytics and reporting solution for the food manufacturer. The solution involved building over 20 reports in PowerBI from an enterprise data warehouse built on Microsoft Azure using 59 source tables, 140 SQL models, and 223 automated tests. The solution provided almost real-time insights to various business units, enabling them to make optimal data-driven decisions.
The benefits of this solution were as follows:
- Enabled real-time data-driven decisions across various business units.
- Improved efficiency by automating analytics and reporting processes, saving days of manual work every week.
- Enhanced data trustworthiness and accuracy, providing a single source of truth.
- Enabled the sales team to track sales, budgets, and shortfalls in real-time, identifying areas of improvement.
- Saved the finance team days of manual work by automating reports.
- Enabled the manufacturing and Inventory teams to make accurate data-driven decisions, reducing significant waste and potentially saving millions of dollars a year.
- Allowed the procurement team to accurately track spending and identify areas of potential savings.
As a result, the subscription program’s new brand and associated benefits were successfully established in a frictionless and intentional manner, effectively encouraging users to sign up without compromising the current user experience of each individual retail brand.
Our Approach
The approach to the solution was designed and delivered in small increments that delivered maximum value to the food manufacturer’s end users. The project was kicked off with a Discovery & UX Design phase, which set up the successful delivery of the whole project. This phase included reviewing the current use cases and requirements developed by the company’s BI team, digging into source data, and starting to form a view on data models and establishing a data dictionary.
This was followed by a two-week Experiment phase, where the team provided a testing ground for users to get the first glimpse of what’s possible with the first use case, a Sales dashboard. In this phase, the team started to dig further into the technical requirements and set up the required infrastructure.
The team then moved into the Delivery phase, where they delivered the first minimally viable product (MVP), which was the Sales team’s first report. This report enabled managers and the 100+ sales force to have almost real-time data on hand in the field to make optimal data-driven decisions.
The team continued to deliver the remaining use cases in Finance, Manufacturing, Inventory, and Procurement creating over 20 reports whilst building out the Enterprise Data Warehouse in nine delivery sprints over a six-month period. The principles that guided the approach to building the company’s modern cloud data platform were enterprise scalability, a preference towards performant tools and services, built-in security, managed services, DevOps-oriented tools and services, ELT (Extract-Load-Transform) over ETL (Extract-Transform-Load), and the use of proven but exciting technology that data teams love to use.
