Chapter 18
Inventory strategy by A/B/C category
There are too many companies who implemented ERP but the On-time delivery to customer and the inventory turn-over is still not improved as expected when bought ERP application, why?
There are 2 reasons for this failure, one is transactional data accuracy like inventory; another one is master data related like the inventory strategy set up etc., for sire the master data accuracy is also very important to impact the ERP application, but our focus will be on inventory strategy.
We know that there is no anyone who is completely same as the other people in the world, it means that each of us has our own character which is different from the others.
Same as individuals, materials parts are having different purchase lead time, BOM usage, commonality, demand fluctuation, unit price, scrap & attrition rate etc. etc. , which decide that we need to implement different inventory strategy for different part #.
Here we mainly focus on the discussion of setting up different inventory strategy by categorizing materials into A/B/C.
Chart 18-1 A / B / C category
Roughly 10% part# occupies 70% of the total inventory value, that’s A parts and C parts on the opposite situation 70% part # with 10% total value. B parts are in the middle.
In terms of inventory strategy for different category, we usually focus on below two areas:
- OF - order frequency (or order interval )
- BS - buffer stock set up
Theoretically, order frequency should be decided by EOQ – economic order quantity, of which the formula is as below:
EOQ = Square Root {( 2 x annual demand x order handling cost ) / (unit price x annual inventory carrying cost)}
For example, for part # A,
- per forecast the annual demand will be 1,000,000 pieces
- order handling cost is 80 us$ per order
- unit price is 20$ per piece
- annual inventory carrying cost we use 20%
then the EOQ = sqrt (2 x 1,000,000 x 80 / 20 x 20%) = 6,325 pieces
the order frequency for this part should be OF = 1,000,000 / 6,325 = 158 times, which is meaning roughly every 2 days (365 days per year divided by 158 times) there should be PO placed for this part or we can say that 3 times delivery per week.
Same as part # A, if there is part # B, of which the unit price is only 5$ per piece, no change for the others, then the EOQ will be 12,649 pieces and roughly 1 order per week.
Same for part # C, if the unit price is 0.5$, the order frequency will be roughly every 2 weeks.
But Today’s situation is because the forecast / MPS is usually loaded with weekly bucket, and there is no detailed calculation on part # for EOQ, we just place PO triggered by MRP on weekly basis for all parts no matter A, B or C. This is basically,
- For A parts, we are creating more inventory because of less order frequency as while reducing the rescheduling opportunities because of bigger ordered quantity per batch;
- Too many order handlings for those B / C parts, which is costing us too much on workload as well as headcount like planners, buyers;
- For C parts, also we may face shortage because of same strategy as A and B, but we all know that C parts usually have more attrition / scrap than others.
Then in terms of buffer stock strategy, for different A/B/C category, we also need to consider different strategy. As we discussed in chapter 14, buffer stock is different from safety stock, which is mainly used for coping with the demand fluctuation and in terms of master data set up in ERP, usually we just prolong the in-house handling cycle time on purpose from 1 day for instance to 2 or more days to get the buffer stock arrival earlier than normal lead time. For example, for A parts, if we put 1 day as buffer stock, we may set up 2 days for B parts and C parts with 5 days even more, example shown as chart 18-2.
Chart 18-2 buffer strategy set up
发表于：
2009-06-28 11:52 程晓华 阅读(1532)
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