I have heard this phrase a lot in recent months in a business
context. It’s so visual, I love it!
It’s not quite enough though. It’s pretty simple to understand that every
project must be able to pay for itself and deliver a return. Is the juice worth the squeeze?
It’s also true though that no organization has infinite
resources of time or money. If you have
10 projects that you could do but only enough resources to handle 3, you must
prioritize those projects that help you meet your objectives (growth,
profitability, market share). What
has the most bang for the buck?
So with these 2 phrases in mind, it should now be
easy…right? (Can you hear the sarcasm)?
In the past, I have been guilty of figuring out something
can be done and then wanting to rush ahead to do it. I have been there, I have done it and I have
been surprised when a manager along the way was disinterested in my project or
actively trying to shut it down.
My enthusiasm was high, I had the capability to deliver
but my ability to see the bigger picture from a business perspective was lacking. Perhaps it’s a gap in my formal educational
process that focused on business math and statistics rather than finance or
managerial common sense? I’m afraid I am
not alone however; many analysts and process improvement folks seem to suffer
from the same condition.
I think it’s fair to say that my working experience has
largely corrected this: a spell in finance working on investment appraisal
helped; running a logistics development team that generated more good ideas than
we had capacity to work on helped too.
Being responsible for delivering financial results may have been the
clincher.
Analysts can be blinkered, I admit it, but this inability to
see the big picture is not restricted to technical folks. Managers see the complexity of analytics and
have their own knee-jerk reactions. Some
perceive high-risk and immediately, perhaps unconsciously, discount the net
benefit they are likely to receive. In
other cases I have seen an almost cult-like view that is without justification
and disconnected from the results that could have been predicted (“do this and good stuff will happen”).
In both cases managers are limited by their inability to
estimate cost and/or benefit. This is
where a good analyst can really help.
Note that for prioritizing most projects we do not need extreme
precision in cost or benefit. Really
good projects do not have to scrape a return, the poor ones are usually
struggling to hit whatever hurdles your finance team has put in place (e.g.
NPV, IRR or payback). What you need is a reasonable estimation backed up by sound
analytics and whatever benchmarks you can lay your hands on. Let’s take a few examples:
Project A: Implementing an
upgrade to the warehouse management system that converts all current
paper-based processes to run on the existing computers.
·
Based off a time-study, this is likely to save
10 minutes per order
·
The network of distribution centers process
approximately 3000 orders per day
·
Cost to implement is estimated at 13 weeks of
development time.
·
Warehouse labor costs about $25/worked-hour
including all benefits
·
Development labor costs around $100/worked-hour
including all benefits
Annual Savings:
10 x 3000 x 365 = 10,950,000 minutes/year
= 182,500
hours / year
=
$ 4,562,500 / year
One-off Costs:
13 x 40 x 100
= $52,000 / year
Summary:
Without calculating NPV or IRR or Payback, I
think we can clearly see that this would be a very, very good project. Focus on the savings per person (forgetting
that there are a lot of them and you can easily miss finding this opportunity)
Project B: Report
Automation: in our sales office, our analysts
currently spend around 10 hours each, every week, preparing standard reports
from Point of Sale (POS) data.
Automating these reports would be very popular, removing a tedious,
repetitive part of the work. Automation
of each report takes about 1week of developer time.
·
We have 10 sales analysts producing 40 reports
·
Sales analysts typically cost about
$70/worked-hour including all benefits
·
Development labor costs around $100/worked-hour
including all benefits
Annual Savings:
10 x 10 x 52 x 70 = $364,000
One-off Costs:
40 * 40 * 100 = $160,000
Summary:
Our annual savings do outweigh the costs… or do they? For the savings to be real we have to stop
paying for these hours (the equivalent of 2.5 people) or be able to reinvest
them into other work that also generates a return. Will we?
Frankly report automation is more reasonably justified by eradication of
error and consistency of output that makes it easier to manage the thing you
are reporting on – perhaps $Billions in sales.
Project C: Use Point of Sale
(POS) data to improve the forecast accuracy of the forecast we build for
manufacturing planning.
·
Our current Forecast Accuracy is 75% for 1 month
out.
·
We believe that incorporating POS data into the
forecasting process could improve forecast accuracy.
(This is where your analyst
should help, because you really need a lot more information, knowledge of how
inventory buffers uncertainty, a decent model, a pilot and good benchmarks to
figure out what this is worth)
A small pilot project using POS
data and shipment history from the last 3 years to predict sales for last year suggests
we could improve forecast accuracy by 3 – 7 percentage points.
Finished goods inventory is what
buffers the manufacturing plant from uncertainty in demand. With a better forecast you need less safety
stock (see [How much inventory do you really need ?] for more details and a handy inventory model). Using the inventory model:
·
The safety stock portion of our overall
inventory is currently 1.8 weeks of supply.
·
A 5 percentage point improvement in forecast
accuracy (from 75% to 80%) is worth about 0.4 weeks of supply.
·
From Finance we understand that 1 week of supply
is worth approx. $12 million at cost.
·
Our weighted average cost of capital is 12% so projected
working capital savings are ~ $1.4 million
·
We may be able to save on storage costs too
(assuming they are variable not fixed).
Converting inventory in storage pallet positions we estimate saving
about 20,000 pallet positions at a current cost of $5 per pallet per month.
(20,000 x 5 x 12) = $1.2 million
(20,000 x 5 x 12) = $1.2 million
·
Note: there
are no ongoing savings to handling costs as we have reduced inventory not
throughput (or sales would have dropped too).
A one-off saving in handling while inventory levels fall could be
included but would be relatively immaterial.
Our pilot project has also helped
us understand exactly how we can enhance the forecasting process with POS data
and allowed us to cost the necessary changes to the forecasting system at approx.
$1 million in one-off cost. So we end
up as follows:
Annual Savings:
$1.4 million in cost of working
capital
$1.2 million in variable storage
costs
One-off Costs:
$1 million
Summary:
This juice is (probably) worth the squeeze. With a payback around 2.5 years it should be
on our list of viable candidates. Remember
that the pilot said that accuracy improvement was in a range of 3 to 7
percentage points. We evaluated the
average here. At 3 points the costs will stay the same, the savings would only
be 60% (not so good).
Does it give the best bang for the buck? Well we will have to line it up against all other
projects competing for our resources to know that. My guess… probably not.
By the way, the inventory modeling exercise also said that you have 0.5
weeks of unnecessary inventory in the system.
Perhaps it would be better to start by trying to eradicate that.
The bottom line for you is that you should consider using this sort of Analytical
Estimation in deciding which of your projects make the cut. A good estimate is a lot better than a bad
guess.
BTW - from recent experience, I can confirm that beetroot
juice is most definitely not worth
the squeeze J
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