Balancing operational costs and customer satisfaction is tougher than ever.
In today’s automotive aftermarket, manufacturers face a growing challenge. Managing thousands of SKUs with unpredictable demand patterns has become increasingly complex, especially when operating under significant cost and resource pressures. The result is often a delicate balance between operational costs and customer satisfaction. Too much stock ties up valuable capital, while too little stock risks customer dissatisfaction and lost revenue.
Aftermarket manufacturers must navigate this complexity while delivering the right parts at the right time. Without advanced planning and visibility, inventory planning becomes a guessing game that can lead to excess stock of obsolete parts and critical shortages of high-demand items.
Aftermarket chaos leads to inventory bloat
One of the most pressing challenges in the aftermarket is inventory bloat caused by erratic demand. A component manufacturer may stock up on parts for a soon-to-be discontinued internal combustion engine model. Without precise demand forecasting, these parts—also known as “dead stock”—sit idle in warehouses, consuming space and capital.
Dead stock not only ties up financial resources but also adds ongoing storage costs. Meanwhile, high-demand items may be unavailable when customers need them, harming service levels, and damaging relationships with distributors, repair shops, and end customers.
Inventory misalignment has several consequences for aftermarket manufacturers:
- Reduced cash flow efficiency due to capital tied up in obsolete or slow-moving parts
- Increased warehouse costs from storing dead stock
- Lower customer satisfaction when critical parts are unavailable
- Challenges in maintaining optimal safety stock across thousands of SKUs
Key performance indicators, such as parts availability, inventory as a percentage of sales, distressed inventory, and customer satisfaction scores are directly affected by these challenges.
Why traditional planning systems fall short
Most aftermarket manufacturers rely on legacy planning systems that cannot handle the complexity of modern aftermarket operations. Thousands of SKUs, combined with erratic demand and lifecycle-driven product changes, overwhelm traditional tools. Forecasts based on historical sales alone cannot predict sudden spikes in demand or the effects of discontinuing specific components.
Without adaptive, AI-enabled systems, planners are forced to overstock items to avoid shortages or risk stockouts for fast-moving parts. Both outcomes hurt operational efficiency and customer satisfaction.
