The Untold Complexity of Spare Parts Warehousing

Choosing the right warehouse solution sounds simple on paper until reality walks in with a nut bolts, a truck axle, and ten different order types. Usually, a solution is shaped by order profiles, SKU attributes, storage conditions, ABC profiles, DOH (days on hand), and the extent of operational quirks like labeling or kitting. Every industry has its own dominant pain point:

• Food and perishables: short DOH and temperature control, smaller picking windows.

• Fashion: Varied demand cycles and mixed fulfilment (D2C + retail).

• FMCG: Extreme skew in sales velocity.

• E-commerce: Wide SKU base and high throughput, low batching and orderline complexity.

• Battery and solar products: Over sized, fire safety considerations, awkward units that ignore pallet standards because… why not?

And then comes the automobile spare parts industry, the overachiever of operational complexity. It brings every possible challenge from every vertical and dumps them into one warehouse. Suddenly, you’re handling:

1. A massive variety of item sizes and attributes

2. Separate ABC classifications per storage category

3. Multiple order types (STO, dealer orders, D2C, exports…)

4. Multiple storage environments including temperature control

5. Long DOH due to regulations

6. Frequent labelling, relabelling, prepacking, and compliance tasks

You’re forced to solve this like a jigsaw puzzle, you break it apart logically and then stitch it back into a complete and meaningful picture. That’s where the real artistry lies.

Step 2: ABC Analysis: Do it Separately (Not Universally)

Don’t fall into the common trap of doing one ABC analysis for all items. Instead, ABC needs to be calculated per storage type:

• ABC for bulk floor SKUs

• ABC for pallet SKUs

• ABC for tote SKUs

• ABC for small-parts storage

This gives you a meaningful dataset where order behaviour is tied to storage reality, not an oversimplified theoretical charts and averages. At this point, your dataset splits into multiple logical families… each representing one combination of: SKU type × order type × storage mode. That’s when you begin designing picking, storage logic, consolidation rules, and material flow for each subset.

Common Failure Points No One Notices Until Too Late

1. High DOH Items in Prime Locations
   Are slow-moving, long-term SKUs filling your automation or high-value bin space? If yes, that’s rent being burned and automation sitting idle.

2. Prepacking Design Flaws
   Have all pack formats been mapped?
   Do workstation counts match throughput from time-and-motion studies?

3. Order Consolidation Bottlenecks
   If picking speed varies across storage modes, should orders be split?
   It may increase outbound shipments, but will reduce waiting times and delays cost far more.

4. Workstation Fungibility
   Are my work stations capable of handling multiple type of order profiles right from 5 units per line to 1000 units per line? Because intra hour peaks are reality of warehouse, you cannot dedicate stations by order type and when peak for a certain category of order occurs the other stations will sit idle. Consider providing provisions to pick full bins for larger orders to avoid bottle necks at picking stations.

5. Wave Planning by Order Type
   Separating waves can be a good idea because, waves essentially will batch orders together and when operator starts working on a STO orders they will take more time to complete it and other smaller store orders will get delayed creating bottle neck.

6. MRP and Identification Challenges
   Because of long DOH, many SKUs aren’t labelled until outbound.
   If three identical-looking nuts have different grades, one-SKU-per-tote or partitioned bins might be the only sanity-saving option.

Final Thoughts

Automobile spare parts warehousing isn’t difficult because the technology is unavailable, it’s difficult because the operation refuses to fit inside a uniform logic box. It demands segmentation, tailored flow design, elastic capacity at workstations, and discipline in SKU classification and operational planning. If you treat everything the same, the system collapses. If you treat everything exactly as it needs to be handled, the system becomes predictable even though its building blocks aren’t.

That’s the thrill, designing order out of engineered chaos.