Driving Enterprise Performance with Manufacturing Data Webinar

Driving Enterprise Performance with Manufacturing Data Webinar

The Impact of Data Visibility and Industrial Analytics for F&B/CPG Companies

Leading manufacturers are building “connected factories" to provide enterprise-wide visibility into production and unlock operational insights by transforming their data into advanced industrial analytics. These companies expect that by combining manufacturing data, financial data, and even data external to the company, they can drive higher performance and uncover “hidden” opportunities for improvement in both their manufacturing operations and in their corporate performance.

What's the impact?

LNS Research examined the use of manufacturing data in a survey with nearly 400 respondents, seeking to answer the questions:

• How does ubiquitous access to manufacturing data impact performance for companies in the Food & Beverage (F&B) and Consumer Packaged Goods (CPG) industries?
• What operations show the most dramatic improvements?
LNS Research also looked at the relationship between traditional MES/MOM systems and the deployment of newer IIoT platforms and advanced industrial analytics to extend the benefits of these systems, and the role of both types of systems in these dramatic performance improvements. And finally, the survey examined the most successful companies to identify common best practices and approaches.
Watch this webinar to learn:
• The leading use cases for manufacturing data visibility and industrial analytics in F&B/CPG industries
• The measurable impact of enterprise data visibility on key operational metrics
• Fundamental, architectural definitions of industrial analytics and data model requirements
• How F&B/CPG companies are deploying data lakes as part of their digital transformation initiatives, and how missing the need for contextualization of data may lead to failure of these initiatives
• Recommended best practices for deployments of these technologies from both a top-down and bottoms-up approach