1. 22 Supply Chain Management Review • September/October 2016 scmr.com
TECHNOLOGY LEADERS SUPPLY CHAIN 4.0 SOFTWARE TALENT
By Shawn Tay
Everyone’s talking about analytics, but supply chain
managers want to know if they can drive innovation in their
operations. HP’s analytics team explains how.
Shawn Tay is based
in Singapore, where
he is one of the
managers of supply
chain and operations
analytics for HP. He
can be reached at
shawn.tay@hp.com.
TechnologySpotlight:
Google the term “advanced analytics” and
you get back nearly 23 million results in less
than a second. Clearly, the use of advanced
analytics is one of the hottest topics in the
business press these days and is certainly
top of mind among supply chain manag-
ers. Yet, not everyone is in agreement as to
just what the term means or how to deploy
advanced analytics to maximum advantage.

2. scmr.com Supply Chain Management Review • September/October 2016 23
An example of commodity analytics could be a study on
why shipments are late. In this study, the analyst collects the
following data into a spreadsheet:
Arrived
Late
Late
Processed
On time
Late
Orders
5
95
TABLE 1
Late orders
by processing status
Source: Shawn Tay
FIGURE 1
Results of processing time analysis
Source: Shawn Tay
Processed on time 5%
Processed late 95%
At HP, the Strategic Planning and
Modeling team has been utilizing
advanced analytics for some 30 years to
solve business problems requiring inno-
vative approaches. Over that time, the
team has developed significant supply
chain innovations such as postpone-
ment and award winning approaches to
product design and product portfolio
management.
Based on conversations we have with
colleagues, business partners and cus-
tomers at HP, three questions come up
regularly—all of which this article will
seek to address.
1 What is the difference between
advanced and commodity analytics?
2 How do I drive innovation with
advanced analytics?
3 How do I set up an advanced analyt-
ics team and get started using it in my
supply chain?
Advanced analytics vs.
commodity analytics
So, what exactly is the difference
between advanced analytics and com-
modity analytics? According to Bill
Franks, author of “Taming The Big
Data Tidal Wave,” the aim of commod-
ity analytics is “to improve over where
you’d end up without any model at all.
A commodity modeling process stops
when something good enough is found.”
Another definition of commodity ana-
lytics is “that which can be done with
commonly available tools without any
specialized knowledge of data analytics.”
The vast majority of what is being done
in Excel spreadsheets throughout the
analytics realm is commodity analytics.
And based on that data, the analyst concludes the following:

3. Analytics
24 Supply Chain Management Review • September/October 2016 scmr.com
The analyst’s recommendation? Fix the orders
being processed late in order to stop the late
shipments.
However, an analyst (or data scientist) versed in
predictive analytics would ask a different question,
such as “does late processing predict late orders?”
In this case, the analyst would have to gather fur-
ther details: For instance, how many orders were
processed late versus on time, and how many orders
arrived on time versus early. That collected data set
would look like the table below.
FIGURE 2
Breakdown of orders by
arrival status and processing time
Source: Shawn Tay
Processed on time
Arrived on time
Yes
500 orders
50%
Yes
495 orders
99%
No
5 orders
1%
Arrived on time
Yes
500 orders
50%
No
95 orders
19%
Yes
405 orders
81%
Processed
On time
On time
Late
Late
Arrived
On time
Late
On time
Late
Orders
495
5
405
95
TABLE 2
Late and on time order
data by processing status
Source: Shawn Tay
In actuality, whether an order is processed late is not a
predictor of whether that order arrives on time. So while fixing
order processing time is generally a best practice, it should be
recognized that in terms of fixing late orders, 81% of the effort
is overkill—or wasted effort.
If we look at it from a decision tree perspective, we would
get Figure 3.
Late
95 orders
9.5%
FIGURE 3
Decision tree using only processing time
Source: Shawn Tay
Order late
Processed on time
Late
100 orders
10%
On time
5 orders
1%
Processed on time
On time
900 orders
90%
On time
495 orders
49.5%
Late
405 orders
40.5%
What we want is to develop a model that allows us to
predict late orders without catching a lot of on-time orders.
However, the test that has been created (orders processed
late) not only captures the orders in the box that is circled in
red (95 late orders) but also catches the orders in the green
circle (405 orders processed late but arriving on time). In
other words, this is a test with a high error rate and we need
to make further refinements.
At this point, let’s assume that a subject matter expert sug-
gests that having advanced border clearance has a big impact
on delivery performance. The data is refreshed with advanced
border clearance and the model refined so that the decision
tree now looks like Figure 4.
To present the information in a way that would
be easier for human interpretation, the data scien-
tist might put it in the format of Figure 2.

4. scmr.com Supply Chain Management Review • September/October 2016 25
As you can see, the test now captures the
majority of late orders, and at the same time
excludes the on time orders. We now have a test
that can predict 90% of the late orders (90 orders
circled in red) while only catching 5 of the on
time orders. This is a very significant decrease
from the 405 on time orders caught in error in
the previous approach.
We also see that there is a cluster of late orders
which this model does not identify (circled in
green), which suggests that further study with addi-
tional data could be done to refine the model to
predict those failures. The result would provide the
analysis output seen in Figure 5.
Management now has a robust analysis from
which to make a decision. If an order is going to be
processed late, it is essential that advanced border
clearance be secured for that shipment.
Driving supply chain innovation with
advanced analytics
The preceding was a simple example of the differ-
ence between predictive analytics and traditional
commodity analysis. In reality, the variables that
Late
90 orders
9%
FIGURE 4
Decision tree using advanced
border clearance and processing time
Source: Shawn Tay
Order late
Advanced border clearance
Late
100 orders
10%
Processed on time
Late
2 orders
1%
On time
3 orders
1%
Processed on time
On time
5 orders
1%
Yes
5 orders
0.5%
No
95 orders
9.5%
Advanced border clearance
On time
900 orders
90%
Processed on time
Late
2 orders
0%
On time
495 orders
49.5%
Processed on time
On time
400 orders
50.5%
Yes
495 orders
49.5%
No
400 orders
40.5%
Late
5 orders
1%
FIGURE 5
Comparison of on time
performance against predictors
Source: Shawn Tay
Arrived late
100
Processed on time
and/or advanced
border clearance
Processed late
and no advanced
border clearance
Arrived on time
900
10%
90%
99%
1%
drive a supply chain result would be unknown. The
analyst would have taken a data set of shipments
that could have dozens of fields (different variables
like processing time, weather, clearance status etc.)
and thousands of rows (orders) and entered them
into machine learning or data mining software. The
software would then run a decision tree algorithm
to generate the tree.
...the test now
captures the
majority of late
orders, and at
the same time
excludes the on
time orders. We
now have a test
that can predict
90% of the late
orders while only
catching 5 of the
on time orders.

5. Analytics
26 Supply Chain Management Review • September/October 2016 scmr.com
The depth of the insight drawn from that analysis
would be highly dependent upon the skill of the analyst,
the quality of the data and how much data is available. In
this case, the skill of the analyst is not just technical skill
(i.e. how well they know the software and statistics), but
also supply chain knowledge. Solid operational knowledge
allows an analyst to understand or interpret the results of
the analysis and to communicate those findings in a man-
ner that would allow the findings to be actionable.
Supply chain innovation comes from finding drivers
of supply chain results that are not widely known in the
industry, and then executing process change around those
drivers. According to Rich Karlgaard and Michael Malone
in Team Genius, the optimal size of an analytics team that
can create that level of innovation would be seven to nine
high-performing analysts. We have also found this to be an
optimal number at HP as it seems to be the point where
there is enough diversity of skills to allow for cross special-
ization collaboration and idea sharing without running into
the issue of too many cooks spoiling the broth. While you
might start with a smaller team in order to develop an inno-
vation engine, the team should be incubated to this size to
develop an organization’s analytics innovation capability.
Advanced analytics: Getting started
Three ingredients are key to getting an advanced ana-
lytics initiative underway. These are: having the right
people; collecting high quality data and; obtaining the
best tools at the right price. This assumes that
the organization is already open to the idea
of advanced analytics but continues to have
a healthy amount of skepticism that can be
addressed through insight and the value cre-
ated by an analytics team. That skepticism can
quickly be addressed by having the analytics
team drive new insights into the business that
translate into cost saving efficiencies or rev-
enue generating opportunities.
1 The right people
Capturing the value of data through
advanced analytics is very much about having
people with the right mix of business knowl-
edge, technical knowledge, innate curiosity and
storytelling skills. Much is made now about
the shortage of data scientists, but if you’re
just starting off in advanced analytics, hiring
Ph.Ds with top tier data science educations
may be overkill. In addition, if you don’t have
the systems in place to provide them with the
data they need, they may find themselves doing
data engineering: finding out where the data is
hidden, cleaning that data and setting up basic
infrastructure. A freshly minted data scientist
looking to make big waves may well find this
work unchallenging and it may prove difficult to
retain your analytics team.
Instead, try looking first for talent within your
organization: Ask if there are any experienced
analysts doing top-notch analytics work who
can be trained up in advanced analytics skills.
Using advanced analytics to solve supply
chain problems
We list below three very common uses of analytics to solve sup-
ply chain problems, not only within HP, but also in supply chains
across many companies. While we have used analytics across
many supply chain issues, these three can be rapidly executed
to show quick wins.
1 Quality control
Traditional methods of quality control involve looking at which
metrics on an assembly line would predict the failure of a prod-
uct. Advanced analytics looks at how combinations of passing
measures would result in failure.
For example: A product has two metrics to measure quality.
Metric A must be passed within 10% of the target and Metric
B must pass within 5% of its target. Advanced analytics can be
used to identify that if Metric A is in the 8% to 9% range, the
product will fail if Metric B is in the 3% to 4% range.
2 Labor retention
In his book “Predictive analytics: The power to predict who will
click, buy, lie, or die,” Eric Siegel highlights the use of advanced
analytics by HP to develop flight risk scores that determine the
likelihood an employee will leave the company and to then utilize
these scores to identify high value—high flight risk employees
that the company would have to work to retain.
3 Customer service improvement
With internet connected products, it is possible to tell the usage
of a product. Through the use of clustering algorithms, you can
group customers with similar characteristics. You can then look
at the usage records of a customer and identify whether there
is a problem with their product when compared with their peer
group (and fix it before it becomes a business issue) and also
analyze their usage of supplies and/or support services to see if
their product is running optimally.

6. scmr.com Supply Chain Management Review • September/October 2016 27
Someone like that would be more familiar with
the political landscape of your company; they
would also know where to find the data gems
and which political quagmires they should
avoid. Depending on the complexity of your
company, a fresh hire could take a year to learn
their way around your company. Training a
strong internal candidate may take a lot less
time. Colloquial evidence points to it being eas-
ier to train analytics skills then to develop a can-
didate’s business experience and institutional
knowledge. This also has the additional benefit
of creating a promotion path, which would sup-
port retention of your best analytical minds.
2 Good data
It’s very trendy for business executives to
jump into Big Data initiatives. However, when
you’re first starting off and trying to build small
victories in order to scale up the analytics
practice, clean data will be more valuable than
Big Data. One hundred thousand records with
40 or 50 variables fits just fine into commonly
available spreadsheets. For larger datasets or
multiple linked datasets, a relational database
(like MS Access) can do the job.
While it would great to create a data
lake using a tool such as Hadoop, the first
step will likely be proving that analytics can
create value for the company. Early on, the
majority of analytics work is data engineering: The team
is finding the data, cleaning it, aligning the different
sources and ensuring that the data is usable. As you
scale up with success, more sophisticated systems can
be brought in as needed.
3 The right tools
Getting the right tools doesn’t have to mean spending
a lot of money. There are a number of very powerful, open
source analytics tools, like R and Weka. Trial versions of
commercial software will allow your team to take a test drive
to see if the tools can deliver value for your organization.
When it comes time to scale up, consider Cloud appli-
cations that allow you to pay for only the capacity you
need. For organizations just starting off, this option could
allow for quicker and cheaper execution.
Let a track record of business success drive the deci-
sion as to whether to buy a tool or invest in hardware.
Most importantly: don’t underestimate the value of open
source applications; you may be amazed at what you can
get done with a motivated team of analysts and open
source software.
Start small, start with what you have
In the examples above, I focused on predictive ana-
lytics. However, advanced analytics is a much bigger
sphere: graphical analytics, advanced modeling and geo-
analytics are just a few examples. While getting started
seems intimidating, you can launch a project with a few
low cost, easy steps as illustrated above. Starting small
with what you have (and what you can get for free)
allows you to build knowledge on what works for your
organization. Get the small wins that open the door,
then steadily push for resources to score bigger wins.
Advanced analytics are a way to capture business value:
they should make, not cost, money. jjj
Analytics innovation case study:
The HP Strategic Planning and Modeling
(SPaM) team
The HP Strategic Planning and Modeling team was identified
in the Harvard Business Review article “Building an Innovation
Factory” as a best practice in innovation. Several factors have
been identified as key to the success of this team, which spe-
cializes in developing analytics solutions to business problems.
One important measure of that success is the significant number
of patents won for algorithms as well as awards in the field of
operations research and analytics. These success factors should
be duplicated to create a successful analytics innovation team.
1 Department should be politically neutral. This can be
created by having the team report to the highest possible level
within the supply chain organization so the team is not perceived
to be part of one faction or another within the supply chain orga-
nization. Traditionally the SPaM team reported to the senior vice
president of supply chain.
2 Focus on business problems. The team creates analytics
solutions for high-importance and high-value business problems.
Resources are focused purely on developing innovative solutions
to existing problems.
3 Disseminate key learnings through the organization.
One of the mandates of the team is to disseminate best practic-
es and lessons learned through the organization. This allows an
analytics team to leverage its efforts to drive more business value
from solutions developed.
In keeping with Team Genius, the SPaM team is historically 10
to 12 team members, in sub-teams of five to seven that are stra-
tegically placed where they can have the most impact for HP.