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Case study

Improving the Manufacturing Efficiency of a Multinational FMCG Company5.0 rating5 Stars Rated On Clutch

Improving the Manufacturing Efficiency of a Multinational FMCG Company

Industry

Manufacturing & Industrial Engineering

About Client

  • A global FMCG company headquartered in the United States with over 40 years of experience with a focus on the production and distribution of
    convenience foods, snack foods, beverages, household products, personal care products, and more.
  • Manufacturing units and distribution centers are spread across North America.

Problem STATEMENT

Our client was facing challenges with low Overall Equipment Efficiency (OEE) such as:
  • High levels of unplanned downtime due to equipment failures and maintenance issues.
  • Inefficient use of resources, leading to low production output.
  • Difficulty in identifying the root causes of equipment failures and maintenance issues.
  • Inability to make data-driven decisions due to lack of real-time data insights.
The operations generated a vast amount of data, which was collected manually, making it time-consuming and prone to errors. The data was not leveraged to its full potential in making informed decisions.

Solution

To address the challenges faced by our client we proposed a solution that involved data engineering and business intelligence:
  • We extracted all the data from various sources, cleaned it, removed redundant segments and duplicates and formatted it as per the industry standards.
  • We built a centralized enterprise data warehouse to store the cleansed and structured data. The process of data collection from the sources was automated via custom connectors and APIs. The centralized data warehouse made it easier to access, analyze and gain insights from the data.
  • To monitor Overall Equipment Effectiveness (OEE), downtime and production output in real-time, we used Microsoft Power BI to create interactive dashboards.
  • We incorporated alerts in the dashboards to ensure that the maintenance team was notified of anomalies or issues at the earliest. The alerts were triggered when certain metrics fell out of the acceptable ranges that indicated a potential issue in the manufacturing process.

Business Impact

Our solutions helped the client achieve several outcomes, including:
  • OEE increased by 15% in a quarter after the implementation of our solution.
  • Production output increased by 7% as the OEE improved.
  • The downtime was reduced significantly by half as compared to the previous financial year.
  • The Power BI dashboards enabled the stakeholders to get real-time insights into their operations, allowing them to make informed decisions, identify bottlenecks and take corrective action.

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Frequently Asked Questions(FAQs)

How do I know if a solution like this is right for my business?

A solution like this is a good fit if your teams spend more time collecting and reconciling data than acting on it, if reports from different departments do not match, or if decisions depend on delayed or manual reporting. It also fits when your data is growing in volume or sources, and your current setup struggles to keep up.

DataToBiz starts with a discovery call to review your goals, data sources, and reporting gaps. You get a clear view of the expected outcomes, effort, and timeline before any commitment.

Can DataToBiz customize the solution for my industry and current data setup?

Yes. Every solution DataToBiz delivers is configured around your industry, business processes, and existing data environment. The team assesses your current systems, data quality, and reporting needs first, then designs a solution that works with what you already have instead of replacing it. This applies across industries such as manufacturing, retail, healthcare, finance, and logistics, and across setups ranging from legacy databases to cloud data platforms.

Which tools, platforms, and data sources does DataToBiz work with?

DataToBiz works across the major analytics, cloud, and data engineering stacks. For BI and visualization, this includes Power BI, Tableau, and Looker. For cloud platforms, it includes Microsoft Azure, AWS, and Google Cloud. For data warehousing and processing, it includes Snowflake, Databricks, and SQL-based databases. Data sources include ERP and CRM systems, spreadsheets, APIs, IoT and sensor data, and third-party applications.

If your stack includes a tool not listed here, the team can assess compatibility during discovery.

How long does a project like this take with DataToBiz?

Most projects with DataToBiz take from a few weeks to a few months. The timeline depends on how ready your data infrastructure is for the initiative and how many systems need to connect. Smaller solutions go live quickly, while larger programs roll out in phases so you see value early. After an initial discovery call, you receive a timeline with clear milestones.

How does DataToBiz keep my data secure and compliant during the entire engagement?

Security and governance is part of the plan from the first conversation. DataToBiz works within your security policies and follows the regulations that apply to your industry. The project usually Access stays limited to the people who need it, and confidentiality terms cover the engagement from kickoff to handover.

What support and results can I expect after the solution goes live?

The work does not stop at go-live. Your team gets a proper handover and guidance on using the solution, and DataToBiz stays available for questions and improvements. The business impact of the project is measured against the goals set at the start, so you can see exactly what changed for your company.

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