Billerud Transforms Manufacturing Forecasting, Capacity Planning and Margin Analysis With SAP and Quickbase

The Solutions

A Quickbase Manufacturing Forecasting and Production Planning Platform

Quandary Consulting Group significantly expanded Billerud's existing Quickbase forecasting environment to create a more comprehensive manufacturing planning and decision-support platform. The solution brought together sales forecasts, production grades, machine assignments, standard costs, production rates, operating calendars, transportation costs, contractual commitments, capacity constraints, revenue forecasts, and profitability calculations. Rather than evaluating these factors independently, Quickbase connected them within a common data model and this created a continuous planning framework: Sales Forecast → Production Grade → Machine → Production Rate → Capacity → Cost → Revenue → Margin

Creating a Consolidated Forecasting Data Model

Quandary first expanded Billerud's existing forecasting data architecture. Forecast information was consolidated into a structured model that supported summarization and matching through defined unique keys. This underlying architecture gave the application a stronger foundation for calculating production requirements and connecting forecasts with operational data; and instead of maintaining isolated pieces of planning information, Billerud gained a framework where downstream calculations could reference the same underlying forecast structure.

Connecting Sales Grades With Production Grades

Customers buy products based on commercial product definitions and manufacturing operates according to production specifications. These two very different perspectives had to come together to meet in order for Billerud to be as productive and cost effect as possible; therefore, Quandary incorporated a Production Grade-to-Sales Grade mapping structure into the Quickbase environment.

This created a defined relationship between forecasted sales demand and the production grades required to manufacture those products. The platform also accounted for sheeter loss when translating sales requirements into production requirements, this helped Billerud move from what customers expect to purchase to what manufacturing actually needs to produce.

Mapping Forecasted Demand to Manufacturing Assets

Once demand was translated into production requirements, Billerud needed to determine where that production belonged. Quandary configured Quickbase to manage primary machine assignments associated with sales locations and forecast data and the expanded scope also incorporated primary and secondary machine assignments for production grades, giving the model greater flexibility when balancing manufacturing demand. This meant that instead of treating machine allocation as information disconnected from forecasting, machine assignments became part of the planning model itself.

Production Rate Modeling Calculates Tons Per Hour

Not every product runs through a manufacturing asset at the same speed, since production grade directly affects throughput. Quandary established production-rate structures that allowed Quickbase to determine forecasted tons per hour using the appropriate matching criteria. The system incorporated production rates by production grade and connected those rates with forecasted demand and this gave Billerud the information required to translate forecasted tonnage into machine utilization.

Automating "Time on Grade" Calculations

Knowing the production rate made another critical calculation possible: Time on Grade. Quandary automated the calculation of how much manufacturing time forecasted demand required on the assigned machine and this transformed demand from a sales quantity into an operational capacity requirement. This meant that instead of simply seeing 10,000 forecasted tons the organization could now understand how many machine hours those 10,000 tons consume. This connection between forecasted demand and required manufacturing time became fundamental to capacity balancing.

Modeling Monthly Machine Capacity

Available manufacturing capacity is not static and machines operate within real-world schedules. Planned outages reduce available production time. Supply conditions change. Production rates vary. Operating assumptions shift month to month.

Quandary incorporated monthly outage calendars and standard-rate adjustment tables by machine into the forecasting model, this allowed capacity calculations to reflect more than theoretical maximum throughput. The system modeled capacity according to days in the month, supply changes, machine availability, and relevant production rates. This gave Billerud a much more realistic foundation for comparing demand with available manufacturing capacity.

Comparing Unconstrained and Constrained Production Forecasts

One of the most powerful elements of the solution was the distinction between unconstrained and constrained forecasts; an unconstrained forecast represents the demand the organization wants to fulfill and a constrained forecast represents what the manufacturing network can realistically produce given capacity and operating limitations. Quandary built reporting that compared the two, gave Billerud the ability to identify:

• Unconstrained forecast volume
• Constrained production volume
• Machine capacity
• Forecasted output
• Dropped tonnage
• Capacity gaps

Graphs and tables provided additional visibility into machine capacity and forecasted output and this turned capacity constraints into measurable business information.

Protecting Contractual Customer Commitments

Not every forecast should carry equal weight when capacity becomes constrained because certain contractual commitments must be protected. Quandary incorporated an input structure allowing Billerud to identify contractual commitments that should be protected within the production model. This allowed the balancing process to reflect business priorities rather than treating every forecasted ton equally and the model therefore became more than a mathematical capacity calculator, it incorporated the commercial realities driving manufacturing decisions.

Automating Cost Modeling

Production feasibility is only one part of the decision, Billerud also needed to understand the cost of fulfilling forecasted demand. Quandary expanded Quickbase to incorporate standard production costs for both production and sales grades, along with direct and indirect cost calculations. For web and sheeted products, this model supported:

• Direct costs
• Indirect costs
• Standard production costs
• Freight costs
• Transportation costs per ton
• Total modeled costs

The system used standard EPS download information for web products as a foundation for efficient quarterly cost refreshes, therefore, cost information became connected directly to production forecasting rather than living in a separate analysis.

Incorporating Freight and Transportation Costs

A product's profitability isn't determined at the mill gate, t the rate and cost of transportation as matters a lot. Quandary incorporated freight cost calculations and mill/plant-to-ship-to transportation costs per ton into the model and this gave Billerud a more complete understanding of the financial impact of serving forecasted demand. For Billeurd, this meant that instead of analyzing production cost alone, the system helped evaluate the total cost associated with producing and delivering the product.

Integrating SAP Data Into Quickbase

Billerud's enterprise data already existed across systems, including SAP and rather than forcing users to manually recreate that information, Quandary incorporated SAP data into Quickbase as part of the working forecasting model. This strengthened the relationship between enterprise data and the operational forecasting environment. This create the situation where Quickbase became the decision-support layer where relevant SAP information could be combined with forecasting logic, manufacturing assumptions, and business rules.

Profit Per Ton and Profit Per Hour Transform Capacity Decisions

When manufacturing capacity is constrained, the question is not simply: "What can we make?" and the more strategic question becomes: "What should we make?" Quandary incorporated automated calculations for Profit Per Ton and Profit Per Hour, this gave Billerud a profitability lens for evaluating competing production requirements. Two products may generate similar revenue per ton but consume radically different amounts of machine time and by incorporating profit per hour, Billerud gained a way to evaluate the economic productivity of limited manufacturing capacity. This transformed capacity planning from a volume exercise into a margin optimization exercise.

Margin Modeling Connects Production With Profitability

Quandary expanded the Quickbase platform to calculate expected margin per ton and margin per hour. The resulting reporting connected forecasted production with financial performance and meant that Billerud could evaluate constrained and unconstrained scenarios - not only according to tonnage - but according to the expected profitability generated by those scenarios. With this new workflow developed, this created a stronger bridge between: Sales Planning → Manufacturing → Finance

Revenue Forecasting Shows the Financial Impact of Capacity Constraints

Lost production capacity isn't just an operational metric, it also represents potential revenue; therefore, Quandary incorporated reporting comparing: Unconstrained Revenue → Constrained Revenue → Enterprise Revenue Gap. This solution also provided summary reporting by machine, customer, and product for forecasted output, revenue, and margin on a month-over-month basis. This gave leadership greater visibility into the financial consequences of production constraints. Which meant that leadership went from saying, 'we don't have enough capacity', to having a deep understanding of how much forecasted revenue and margin are affected, where the constraint exists, and which customers or products are involved.

Dashboards Turn Manufacturing Data Into Decision Intelligence

The expanded model was supported by reporting and dashboards designed to make complex forecasting information easier to consume. Leadership and operational teams gained structured views into:

• Forecasted demand
• Production capacity
• Machine assignments
• Tons per hour
• Time on grade
• Constrained versus unconstrained volume
• Dropped tonnage
• Production costs
• Revenue
• Margin
• Profit per ton
• Profit per hour
• Machine/customer/product performance
• Month-over-month changes

The result was a more unified manufacturing intelligence environment.

Modernizing the Existing Verso Forecasting Environment

An important element of the project was that Billerud did not need to discard everything employees already understood. Billerud wanted to keep the same look and feel, but a deep cleanup of the existing Verso systems while maintaining the familiar look and feel employees had already been trained to use. Quandary, therefore, modernized the underlying application while preserving continuity for users, the cleanup included improvements around:

• Actual-versus-forecast reporting
• Price and volume forecast accuracy
• Customer master data automation
• Automated concatenated fields
• Forecast filtering
• Grade management
• Removal of redundant tables and fields

This approach reduced unnecessary application complexity without forcing employees to relearn the entire operating environment.

Automating Customer Master Data Maintenance

Forecasting accuracy depends heavily on clean customer information. Quandary's scope included automation designed to review invoice history for new sold-to, price-driver, ship-to, and end-user numbers and add relevant information to the customer master and this reduced the dependence on manual master-data maintenance and strengthened the data foundation supporting forecasting.