Executive
Summary
The competitive environment for every industry grows increasingly intense.
Fast, reasonably accurate information about the impact of a software investment
decision grows more critical. Many decision-makers look for an exact forecast
of return on investment (ROI) from the purchase of a supply chain management
application. At least four very real challenges make such perfect information
elusive. Commonly, executives meet these challenges with responses that
are not carefully considered. The challenges and the corresponding reactionary
refrains are as follows:
- Limited
time exists to perform analysis - "We need to know now!"
- Business
analysis skills are lacking - "We are looking for the vendor to tell
us!"
- The data
to perform the analysis are almost always not available in the corporate
databases - "We have tons of data, but we don't have it broken down
like that."
- It is
always difficult to predict the future … like forecasting, certain
laws about a prediction of ROI will forever hold true…
- the
prediction will always be wrong
- the
prediction will always change for as long as the analysis continues
- someone
is going to be held accountable for the prediction
- "Just
give us the bottom line!"
After
a quick look at these issues, one might question the effort to undertake
the analysis to predict an ROI, as well as the validity of the outcome.
Perfect, or even complete, information may not be feasible, but if a few
basic principles are followed, some analytical work can provide an understanding
of the potential for bottom line impact. It can also yield insight into
the root causes of undesirable symptoms from which your business may be
suffering.
The
reactions of some decision-makers to each of the four challenges that
are listed above provide a convenient outline for exploring a more thoughtful
and strategic approach to evaluating a potential investment in supply
chain management software.
About
This Note: This is a four part note, each part addressing one of the four
challenges.
Part
One covered "We need to know now!"
Part
Two covered "We are looking for the vendor to tell us!"
Part
Four contains links to the prior parts.
Part
3. "We have tons of data, but it is not telling us what we need to know."
Performing the Data Analysis
If
the data exist, you need to trace a symptom, like excess work-in-process
(WIP) inventory, to the root cause such as forecast error that drove production
of the wrong product. Once that is done, then powerful, but relatively
simple analysis can be performed by collecting the data from the data
warehouse, or wherever it is stored, by putting it into a spreadsheet
and then creating a cumulative distribution (see Figure 1) of the symptom
by reason code.
Figure
1.
More
commonly, however, the data cannot be readily segmented by root cause.
This is probably because the symptoms and the root causes have not been
identified and linked. Using a simple fishbone diagram (see Figure 2),
a few folks who know the business processes involved can probably identify
symptoms and trace them to possible root causes. Naturally, a skilled
facilitator (possibly a consultant) will help, but you can also learn
by reading up on the idea1 and by doing it.
Figure
2. Cause and Effect (Ishikawa or fishbone) diagram with potential
root causes marked with capital letter reason codes.
Click here
to view larger image
Once
the root causes have been identified, then a system of recording the incidents
by reason code has to be put in place. In some cases, while occurrences
will not be tied to a reason code or other explanatory data, there will
be some data that can be used as an approximate surrogate to estimate
the order of magnitude of the root cause. In those cases, you can get
to an answer sooner, albeit a less precise one.
As
an example, forecasting may be coming from sales. You can probably measure
the accuracy pretty well by saving the forecast and then by comparing
it with orders or shipments. What is harder to determine is how much better
your purchasing, manufacturing and distribution would have been if forecasts
were 50% more accurate, or what the bottom line benefits would have been.
But by making some observations like how often a job had to be interrupted
to start another one based on a canceled order or a forecast that was
wrong, you can begin to build a collection of data that will be the foundation
for answering that question. Then, by creating a cumulative distribution
that shows the schedule changes by reason code, you will get an understanding
of the size of this problem. Both inventory turns and customer service
will go up if you can create a plan that is more flexible, responsive
and accurate by attacking the root cause. That root cause might very well
be the fact that visibility into the future demand ends with your enterprise.
Additional information may be available from your customer, but you do
not have access to it.
Footnotes
1. A basic production/operations management text
such as will probably help. One good author is Stevenson whose text has
been published by Irwin.
Refine
Your Forecasting
By using software tools that help you forecast and work together with
others inside your organization, and even with your customers, the forecasts
may become more accurate. You can make an assumption on how much improvement
might be possible. Then, hypothetically, reduce the schedule changes due
to forecast errors by that amount. Research average WIP and reduce that
by the same factor. Put a procedure in place to track premium shipments
that are paid by your company by reason code. Take the premium freight
that is caused by bad forecasts to the bottom line.
Then,
since you made an assumption that forecasts could be 50% more accurate,
you will need to perform some sensitivity analysis. Vary the 50% and see
what the results tell you. This kind of simulation model can be created
with a spreadsheet tool.
Data
Analysis Challenge Summary
Borrowing the Pareto and Ishikawa tools from TQM practices can help you
find data and create information that you did not know was there, but
the speed with which this kind of analysis can be performed increases
with the availability and accuracy of data. Recent developments in software
that support data warehousing, activity based management (ABM) and business
scorecard metrics, as well as cause and effect relationships, can enable
ongoing evaluation and direction of a given investment decision. In addition,
activity based costing (ABC) can provide detailed actual costing data
that may increase the breadth and depth of decision support from a supply
chain management application.
This
concludes part three of a four part note.
Part
One covered "We need to know now!"
Part
Two covered "We are looking for the vendor to tell us!"
Part
Four contains links to the prior parts.
About
the Author
MARK WELLS has extensive experience on many aspects of supply chain management
from within the industry, as a supply chain consultant, and as part of
a software development organization. He has CPIM certification and an
MBA from Drexel University where he has also taught operations management
and operations research. He currently works for the applications development
division of Oracle Corporation, focusing on supply chain planning.
He can be reached at mark.wells@oracle.com.