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How Tillamook Eliminated the Gap Between an Eight-Year Aging Horizon and a Plan It Could Execute On Tillamook County Creamery Association (Tillamook) is a farmer-owned cooperative headquartered in Tillamook County, Oregon, that manufactures and sells high-quality dairy products — cheese, yogurt, ice cream, sour cream, butter, cream cheese, and frozen meals — under the Tillamook brand. Founded in 1909, the co-op has grown to include 90 farming families and 200 items across seven categories. Starting in 2017, Tillamook expanded from a regional West Coast presence to a national footprint, adding major retailers such as Costco, Walmart, Kroger, Safeway, and Albertsons, and launching a deli program in 2019. The complexity of Tillamook’s product portfolio compounded the growth challenge. Cheese — the company’s largest and highest-margin category — requires aging from nine months to eight years before going to market. Planning what to produce today to meet demand two to eight years from now, across a rapidly expanding customer base and a company-wide 99% fill rate objective, required a planning infrastructure that Excel-based processes could no longer support.
Industry
Food and Beverage
Solution
Logility Supply Chain Planning
Challenges
» Demand planning, inventory optimization, manufacturing planning, replenishment
Benefits
» $4.2M saved in spoilage and obsolescence » 75% decrease in finished goods inventory » 99% company-wide fill rate achieved and sustained through national expansion » 85+% forecast accuracy » Expanded sales volume: products, customers, and distribution points » Increased visibility of new items across production locations
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"We have to figure out today what we think we're going to sell two years from now and eight years from now, and have the right cheese in the right place at the right time." Elaine Videau Senior Planning Manager, Tillamook County Creamery Association
The Challenge Tillamook’s strategic growth ambitions had outpaced its planning systems. The co-op was forecasting at the item level rather than the item-location level — a limitation that degraded signal quality as the customer base expanded and distribution complexity grew. Planning, finance, and sales each worked from their own numbers, with limited budget collaboration and no mechanism for consensus forecasting. The manual, Excel-driven planning process that governed replenishment, production scheduling, and inventory management introduced errors and consumed labor that should have been devoted to exceptions and value-adding decisions. The consequences were measurable: forecast accuracy in the 70–80% range, finished goods inventory levels that were too high, spoilage and obsolescence costs that compounded across the perishable product portfolio, and a fill rate that fell short of the 99% company-wide target. With national retail expansion underway and co-manufacturer relationships to manage, the gap between what the planning system could do and what the business required had become a strategic constraint. Before Logility, the planning gap looked like this: • Forecasting at the item level — not item-location — degraded signal quality as the distribution expanded • Planning, finance, and sales worked from separate numbers, with no consensus-based forecasting mechanism • Manual, Excel-driven replenishment and procurement caused errors and consumed planning capacity • Finished goods inventory was too high relative to demand, with spoilage and obsolescence compounding • Fill rates fell short of the 99% target, and visibility into new items across production locations was limited • Long-horizon cheese aging required reliable multi-year demand signals, which the existing system couldn’t provide
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The Solution Tillamook deployed Logility Supply Chain Planning to replace its Excel-based planning processes with a unified, AI-powered environment spanning demand, inventory, manufacturing, and replenishment planning — integrated across its internal plants and 14 co-manufacturers. The platform enabled Tillamook to transition from item-level to item-location-level forecasting — providing planners with the granular demand signals needed to align inventory investments, production, and distribution across a national customer base. Statistical and sales forecasts were integrated so planners could compare system-generated numbers with sales team input and build consensus. Seasonal inventory prebuild logic — critical for categories like ice cream with pronounced demand spikes — was managed through time-phased inventory policies aligned with service goals. Long-horizon visibility required for aged cheese categories was embedded in the planning model rather than approximated manually. Replenishment orders that had been generated manually in error-prone spreadsheets were now released automatically into the ERP system — freeing the planning team to focus on exceptions and value-adding decisions rather than on manual order processing. Capacity planning and what-if scenario analysis for capital expenditure decisions shifted from judgment to data-driven analysis. Three capabilities defined the shift from manual to intelligent planning: Item-location demand forecasting and consensus planning. Forecasting shifted from the item level to the item-location level — providing planners with the granular signals needed to synchronize inventory, production, and distribution across a national retail footprint. Statistical and sales forecasts were integrated into a single environment, enabling planners to compare and reconcile numbers and build cross-functional consensus. The planning team moved from working with separate numbers to working with a single number. Time-phased inventory planning aligned with seasonality and service goals. Inventory policies were set at the product and location levels, with seasonality intelligence enabling prebuilds that matched the demand shape — including the ice cream demand spikes that required building inventory weeks ahead of peak. Long-horizon aged cheese planning was embedded in the model, giving the co-op a structured way to decide today what to produce for markets two to eight years out. Automated replenishment. Replenishment orders were generated automatically and released directly into ERP, eliminating the manual, error-prone spreadsheet process and freeing the planning team to focus on exception management and value-adding decisions.
"Building more accurate and more granular forecasts has allowed the supply chain team to have more credible conversations, drive consensus forecasting and have a seat at the executive table." Elaine Videau Senior Planning Manager, Tillamook County Creamery Association
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“With Logility, we have a plan for intentional and profitable national growth.” Jake Anderson Vice President of Supply Chain, Tillamook County Creamery Association
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The Results The impact was measurable across every dimension the team had identified as a constraint. Forecast accuracy improved from 70–80% to 85%, enabling the co-op to synchronize inventory, production, and distribution with significantly greater precision. Finished goods inventory decreased by 75%, and spoilage and obsolescence costs fell by $4.2 million. Fill rates reached 99%, closing the gap to the 99% company-wide target and sustaining that level as the customer base expanded. The documented outcomes: • $4.2 million saved in spoilage and obsolescence as more accurate demand signals reduced overproduction and expiration risk across a perishable product portfolio • 75% decrease in finished goods inventory, driven by time-phased inventory policies aligned with actual demand and seasonality • 99% company-wide fill rate achieved and sustained during national expansion • Forecast accuracy improved from 70–80% to 85%, enabling the planning team to lead consensus forecasting and to have credible conversations with finance and executive leadership • Expanded sales volume, products, customers, and distribution points were supported without adding planning headcount — automation absorbed the complexity of growth • Improved visibility into new items across production locations, enabling the co-op to manage launch timing and inventory positioning across a growing SKU portfolio The broader shift was organizational. The planning team moved from managing errors and filling out spreadsheets to leading credible, data-driven conversations with sales and finance — and, as a result, earned a seat at the executive table. That’s not an efficiency gain. It’s a change in the purpose of supply chain planning.
Looking Ahead With supply chain planning embedded as a core operating capability, Tillamook has the foundation to scale without increasing complexity. The roadmap ahead builds directly on what’s already running — extending the platform into the next generation of planning intelligence across four areas • AI forecasting with advanced analytics — moving from statistical baselines to AI-driven demand sensing across a growing national SKU portfolio • Automated forecasting and block planning — reducing manual intervention in production scheduling and long-horizon cheese-aging decisions • Continuous network optimization — dynamically aligning inventory positioning, production allocation, and distribution as the network expands • Reduced cybersecurity risk — modernizing the technology infrastructure that underpins the planning environment The goal is to create a supply chain in which the gap between what the business knows about its demand position and what it can do about it closes continuously — across every SKU, every aging horizon, and every retail channel.
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About Logility, an Aptean Company Logility, an Aptean company, delivers purpose-built supply chain software and expertise across Plan, Make, and Move — demand planning, manufacturing execution, network design optimization, and transportation — unified by a single AI orchestration layer that turns signals into execution across every function. Backed by 30+ years of supply chain domain expertise and a global team of practitioners, Logility serves manufacturers and distributors worldwide. Learn more at logility.com. COPYRIGHT © LOGILITY 2026. ALL RIGHTS RESERVED.
REVISED JULY 1, 2026