Industry intelligence
Optimize planning, inventory, production, logistics, supplier performance, and anomaly detection across operations.
Connected industry view
From fragmented operational signals to timely, governed decisions.
A practical starting scope
A proposed approach to discuss with your team. It is not a claim about a completed client project.
Published external case studies
These are Microsoft-published customer stories, not GGMS projects or client endorsements. Summaries describe the publisher’s account; the lessons are our interpretation. Follow the source for its full context.
Kraft Heinz
Microsoft describes Kraft Heinz working with Microsoft and EY to move legacy reporting to Power BI on Azure. The story connects platform work with the sales team's need to identify opportunities across retail customers.
Our reading: start with the sales review and the customer hierarchy before choosing dashboard layouts.
Read the Microsoft story about Kraft HeinzBusiness pressure points
We begin with the business decisions that matter, then connect the data, controls, analytics, and workflows required to support them.
What we enable
Connect demand, supply, inventory, orders, and logistics into a decision-ready operating view.
Measure throughput, downtime, quality, yield, and material movement at the right level of detail.
Track reliability, lead time, cost, quality, and risk across the supplier ecosystem.
Potential project areas
Priorities are selected around business value, data readiness, adoption, risk, and the ability to integrate insight into real work.
Delivery approach
Align on decisions, processes, measures, controls, and the people who will use the solution.
Map source systems, create trusted models, and build quality, lineage, security, and ownership into the foundation.
Deliver practical dashboards, forecasts, models, alerts, and workflows around priority use cases.
Integrate with daily work, enable teams, monitor adoption and performance, and improve the solution over time.
Technology landscape
We integrate with the platforms already carrying your processes and data, then add what is needed for scale, governance, analytics, and AI.
Security and responsible AI
Role-based access, data minimization, lineage, quality controls, auditability, model monitoring, and human review are designed according to the sensitivity and risk of each use case.
Outcomes to work toward
These are project objectives, not measured client results. Agree the baseline, reporting window, and attribution method before evaluating improvement.
Supply Chain
Tell us where decisions are slow, visibility is incomplete, or manual work is holding teams back. We will help shape a practical starting point.
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