When it comes to machine layout, workplace setup or storage space, a prior ABC analysis of the frequency of events or value, volume, weight, etc. is a good start for making it efficient.
The ABC analysis is based on the principle of Pareto law, sometimes called 20/80 (20% of causes accumulate 80% of effects) and defines three classes A, B and C:
- Class A: items accumulating 80% of the effect
- Class B: items accumulating the next 15%
- Class C: items accumulating the last 5%
According to 20/80 law, A class items are few (20%) but concentrate the main part (80%) of the effect. In the case of an inventory, few class A items will accumulate the main value or biggest weight, will move the fastest, will earn the most profit, etc. depending the parameter considered.
B class is made of relatively large share of part numbers or items accumulating altogether only 15% all occurrences. The numerous items of C class make a tail accumulating the remaining 5% of occurrences, despite their great number.
Application example: Inventory and picking management
Let’s consider a warehouse where operators prepare kits or orders from parts or products stored on shelves. The shelves are traditionally set up in a U-shaped layout, providing a logical path for picking.
Let’s assume the part numbers are placed on the shelves in an alphanumerical order, the first being AA000 and the last ZZ999, to simplify picking and make it straightforward. Picking lists are consistently printed in alphanumerical order, the operator walks the U and picks up the articles (part numbers) accordingly to his list.
This system is pretty simple, but yet not very efficient. For each preparation of few articles, operators have to walk the whole U. If the stored items are big volumes or if the U cell is widespread, the time spent in walking and transportation becomes significant.
Remember time passed to move or transport material or parts is considered non value operation, a waste in the lean thinking way.
Applying the rules of ABC analysis to our inventory, and focusing on the “stock turns” index or “frequency of picking” per part number, we’ll see an A class (probably around 20% of all items) accumulating 80% of all pickings.
The A class items being so often picked, it is only common sense to place them the closest to entry-exit (green zone) in order to reduce reaching distance, hence time spent picking.
Stored quantities must be consistent with picked quantities, therefor and despite the fact these items represent only a limited share of the whole (numbers of part numbers), the A class deserves duplicate storage space on the shelves.
Statistically in fewer demand, B class items are placed behind A class (orange zone) and C class items (the least picked) stored even further (red zone).
Now, thanks to new layout, picking time and distances are globally reduced. In most cases, the journey in the U cell will be restricted to the front (green zone). In few cases only, statistically less frequent, the operator will have to walk the whole U cell.
On top of time and distance reduction, we can imagine the light of our green zone should be on constantly, but orange and red zones could be equipped with sensors or switches to light them up only when somebody is in. In the same way, for the comfort of workers, heating or air conditioning isn’t necessary in zones where they seldom go. These are opportunities to reduce energy expenses.