When the pandemic began, manufacturers ramped up production of medical devices. We worked with a medical device company whose inaccurate demand forecasts overestimated the demand for ventilators, resulting in overproduction and the company locking up over $160m in excess inventory.
We used Faculty Frontier to generate hyper-accurate demand forecasts, enabling them to create more precise production plans to mitigate overproduction and set far lower SKU-specific safety stock targets to slash the inventory they hold.
The company operates in over 100 countries
Inaccurate demand forecasts resulted in the overproduction of medical devices generating a significant financial crisis. The company had locked up millions worth of capital in excess inventory and halted cash flow.
They needed to dramatically improve inventory management across the entire company to alleviate their financial crisis and mitigate excess inventory in the future. But their ‘top-down’ approach to planning wasn’t moving excess inventory fast enough. The company also attempted to improve its forecasting to prevent overproduction, but without a data science team, progress was too slow. All the company could rely on was ‘quick wins’ to find additional savings, and these were nearly depleted.
Faculty Frontier transformed demand and supply planning for consumables by creating production plans that actively reduced inventory levels and whittled down safety stock levels.
In October 2021, we rapidly deployed our technology to generate accurate demand forecasts to reduce the company’s excess inventory, optimise planning from the ‘bottom-up’ and improve its future inventory management.
We improved production planning
Thanks to more accurate predictions of demand, the company was less likely to overproduce and was able to create optimal production plans that accounted for current inventory levels. We enabled them to shift the excess inventory while still leaving a buffer of safety stock.
We connected C-suite and operational teams
We gave executives a far more targeted view of operational data by surfacing alerts showing products consistently overproduced, enabling them to simulate key decisions across the supply chain. This helped the company prepare for supply chain shocks.
We improved demand forecasts
Our high-quality algorithms generated forecasts that produced a range of likely outcomes, allowing planners to make data-driven decisions. This gave them far greater confidence in forecasts and pushed back against adjustments recommended by commercial teams.
Via Frontier, the medical device company can now optimise inventory allocation for every SKU individually and at the right frequency (i.e. daily, weekly, monthly) depending on the product type and the business need. This enables the company to swap their blanket safety stock levels of 13 weeks for much lower SKU-specific targets (an average of 5.5 weeks), helping to avoid ‘stock-outs’ without the extra carrying costs.
Frontier could unlock $52m in savings across two years by helping the company manage trade-offs between inventory and service. Significant cash flow problems will be improved by better inventory allocation.
Within six weeks we had corrected the cause of the problem, providing our customer with a sustainable solution that could unearth huge savings with every production plan. The robust scenario planning capabilities can help the executive team prepare for demand and supply shocks, putting the company in a far better position to handle future volatility.
Capital locked up in excess inventory
Case study: Supply Chain
Reducing excess inventory and safety stock for one of the world’s largest ventilator manufacturers
They offer over 27,000 products
02. This led to suboptimal planning that simply sought to ‘smooth out’ production plans to avoid spikes and overestimated safety stock
01. Poor-quality algorithms generated forecasts that couldn’t handle demand spikes, and these algorithms were skewed by adjustments from commercial teams
03. Disconnected executive and operational teams led to an inaccurate view of stock, so the company couldn’t set realistic safety stock targets.
Average reduction in weeks of safety stock levels
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