01
Fragmented plant systems
SCADA, PI, PRiSM, Spotfire and internal tools were used independently across business lines, making consolidated planning difficult.
Case study · Chemicals & industrial gases
Global S&OP and Demand Forecasting
Creating a centralized S&OP control environment with predictive demand forecasting across a network of more than 100 production plants.
A multinational chemical manufacturer operated more than 100 plants across Canada and the United States, with each business line using different systems for customer demand, production, maintenance and inventory. NoèmeX created a standardized data and BI environment for S&OP, aligned local data to corporate standards and introduced predictive forecasting to support more proactive production and inventory decisions.
The challenge
What the organization needed to change
The public page keeps the core business problem visible so prospective clients and search engines can understand the engagement without requiring a form submission.
02
Inconsistent data models
Local structures and master data did not align with corporate standards, creating recurring discrepancies.
03
Manual forecasting
Demand planning relied heavily on manual processes, limiting the ability to anticipate changes in customer demand.
04
Weak governance and visibility
Shadow IT and inconsistent BI practices made it difficult to maintain trusted S&OP metrics across the network.
What we did
A structured path from fragmented operations to a governed solution
This summary explains the major workstreams. The downloadable case study can carry the deeper implementation narrative, diagrams, frameworks and technical detail.
01
Discovery and user research
Documented planning needs, KPIs and data requirements across user groups and business lines.
02
Data integration and standardization
Integrated plant and enterprise sources into a centralized BI suite and mapped local structures to corporate master data.
03
Governed S&OP intelligence
Standardized metrics and reporting so executive and operational users could work from the same underlying definitions.
04
Predictive demand forecasting
Applied machine learning and predictive analytics to historical customer and operational data to support demand, production and inventory planning.
Business systems / environment
SCADA · Plant Information (PI) · Aveva PRiSM · Tibco Spotfire · Oracle R12 · SAP · Internal systems
Benefits and results
What changed after implementation
Only outcomes supported by the documented case material are shown here. Where the source describes future AI opportunities, they are not presented as delivered results.
The solution was designed around a production network spanning more than 100 plants in Canada and the United States.
The detailed implementation describes automated integration providing accurate operational metrics every hour.
Predictive forecasting improved the organization’s ability to align production and inventory with expected customer demand.
A centralized control environment reduced silos, strengthened governance and improved cross-functional collaboration.
Related capabilities
Explore the services behind this transformation.
These links connect documented delivery evidence to the focused NoèmeX capabilities most closely associated with the work.
Where this work connects
Follow the case into the organization, leadership agenda and industry context.
The same evidence can be entered from several points in the site: by industry, by function or by executive decision context.
Business functions
Executive priorities
Chemicals & industrial gases
Explore the broader industry context, operating pressures and related NoèmeX capabilities connected to this type of transformation.
Explore industry →Continue exploring
Business challenges
Problems this case can inform.
These links connect the delivery evidence back to the business problems organizations may be trying to solve today.
Your Contact Persons
Connect directly with the people responsible for this work. Explore their experience and contact options.
Zahra Fathisalout
Founder · Eng., CSP, CMLAI
Corporate governance · ASC-C.Dir. candidate
Works across strategy, enterprise transformation, data, AI and governance, bringing operational engineering experience to complex transformation decisions.
View profile →
Brendan Young
B.Com, Director of Operations
Connects transformation design with operating discipline, drawing on logistics, fulfillment and high-throughput operational leadership.
View profile →
Detailed case study
Download the detailed case study.
Explore the transformation in greater detail, including the operating context, implementation approach, systems environment and key outcomes.
Requests submitted through this form are handled under the NoèmeX Personal Data Charter, Cookies and Trackers and our General Terms and Conditions of Use (GTCU).
Please verify your email
We’ve sent you an email to confirm your address. Once your email is verified, your request moves forward, and if there’s a fit we’ll get back to you within 1 to 2 business days from NoèmeX. If you don’t see it, check your spam folder.