Page 18 - UnderstandingJanSanRedistribution_flipbook

Stage One: How to measure fill-rates better to have a base from which to improve?

Initially fill-rates were not being measured at all. The first solution was a simple, software and inside-sales-
routine fix that allowed every item that a customer wanted to order be entered against what was in stock
to then generate a statistical report on fill-rates (and “the shorts”) by item categories from most picked
to dead. Actual fill-rates for all levels of items were surprisingly lower (by over 10 percentage points) than
what everyone had imagined. And, the weak fill-rates were, in turn, one of the causes for creating too many
unprofitable small orders as well as unmeasured, customer dis-satisfaction and potential defection rates.

Stage Two: What were the hard and soft costs of solving “short” line items?

ABC’s vets minimized the impact of the lower, actual fill-rate scores, because ABC was “so good at solving
the shorts” a number of ways. With deeper analysis, each way had its economically dysfunctional aspects.
For example:
1. A customer would call and ask if ABC had an odd item. If ABC didn’t have it, the customer would
call other distributors to find it. A quick survey by Deuce discovered that if ABC could have had
the odd item, the customer would have also ordered other commodity items on the same order.
Demand for both the odd and the common items were not being captured in the computer which
would then, over time, raise suggested inventory investment and fill-rates on those items. So, if the
customer was a top 20% most profitable account, inside sales people were coached to ask what
the rest of the order was and input that demand into the computer even though the entire order
was still lost. And, the inside sales reps would mention that this extra step was aimed at “improving
the (important customer’s) future service quality”. Deuce’s assumption was that: if ABC was going
to invest more in inventory it might as well be in the specific items that best customers wanted.
2. If a customer wanted 5 widgets, but ABC had 3, the inside reps were trained to encourage the
customer to back-order the balance and not let “the other two get away”. But, this created two sets
of small order transaction costs for both ABC and the customer.
3. The new routine is to ask if the item is for internal supplies, if so, may the line be shipped complete
for 3. Then, the next order can be for the normal 5, assuming stock has been refreshed. By not
backordering a small order, if possible, a set of extra transaction costs are saved for both parties.
This policy is working!

4. Except…what if the customer needed all 5 right away? There were several options. ABCs first reflex
was often to ship the balance from its satellite branch, if possible. But, Deuce reasoned that this
solution also created two sets of order transaction costs for both ABC and the customer, and the
costs were even higher for shipping from the other location. There was extra freight and some
inter-branch bickering costs over doing stock checks and timely shipment for “someone else’s
customers”. And, the demand history for such shipments was incorrectly staying with the shipping
branch, so that the originating branch’s demand history would be chronically under-counted and
the shipping branch over-counted. How can a computer help buyers forecast item demand better
if we don’t feed the right demand data into the right location?
5. Option two for solving “shorts” was to offer customers substitute items for the 2 short or even all
5 on a superior quality solution, but at the inferior product price. Many customers liked this option,
but again the demand for what the customer wanted to buy was left with the substituted item, so
the computer forecasting would recommend buying more of the substituted item and less of what
customers really wanted – a “vicious feedback cycle”.

Stage Three: How to improve fill-rates immediately for the least incremental, net cost?

Deuce taught everyone the importance of and the how-to’s for making sure that demand for what the
customer originally wanted was captured at the location from which they wanted to buy it. He also
implemented the following service programs – all with measurable tracking records:

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