Affordable Analytics for Small
and Medium Business
Cost Modeling with Predictive Analytics
improves forecast capability of a midsized manufacturing company
SimaFore 2011 White Paper Series
Table of Contents
Affordable Analytics for Small and Medium Business ........................................... 1
Table of Contents.................................................................................................. 2
Executive Summary .............................................................................................. 3
1.
Business Analytics for SMBs: Background ..................................................... 4
2.
Objectives of Cost Modeling ........................................................................... 5
3.
Using Predictive Analytics for Cost Modeling ................................................. 6
3.1
Business Objective: Enable rapid data-driven cost forecasting................... 6
3.2
Data Sources: Mostly public domain ........................................................... 7
3.3
Analytics Process: A 4-step approach......................................................... 7
3.4
Model Deployment: An App-based approach.............................................. 9
3.5
About Cloud computing and deployment .................................................. 10
4.
Conclusions: ROI from using predictive analytics......................................... 11
References ......................................................................................................... 13
About SimaFore .................................................................................................. 13
Contact SimaFore ............................................................................................... 13
About the Authors ............................................................................................... 13
Business Analytics for SMM
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Executive Summary
In recent years, a trend has emerged – predicated on the availability of large
amounts of data - wherein data analysis and statistical methods are being used
in nearly every business function. It is being applied from customer acquisition
and retention, to risk management, to demand forecasting. There is a new allencompassing name for this discipline – business analytics. As before the
pioneers have been large companies. Thus, business analytics is an oft-used
and well-worn term in companies like Bank of America, Progressive Insurance,
Amazon and Google, for example. Small and mid-sized businesses (SMBs)
seem to be largely under-represented in this new wave. However, this trend is
likely to change in the near future. Two major drivers for this are the availability of
affordable technology via Open Source and affordable computing via the Cloud.
In this report, we will demonstrate how an SMB can implement and deploy
predictive analytics for a common business application - Cost Modeling and
Forecasting. We will discuss how L&L Products, a mid-sized custom chemical
compounding company specializing in structural composites is leveraging
analytics to improve their cost forecasting. The primary questions addressed
include:
1. How can an SMB affordably develop objective and reliable models that
can be used for cost forecasting?
• This report describes a systematic process for doing so
2. How can a SMB leverage open source technology and cloud computing to
address similar business problems?
• We will demonstrate that through our process, this task is easy enough
to be applicable to any business problem that an SMB may have to
deal with.
3. What is the return on investment from using predictive analytics for cost
modeling?
• Predictive analytics can reduce the time to develop accurate cost
estimates from several days to a few hours, as L&L is aiming to do
Using this report
SMBs can use this report to understand how cost modeling may be implemented
in their own business. They can get informed on of how to leverage open source
technology and cloud computing to help deploy such solutions. Additionally, they
can extrapolate and identify other functional areas that can benefit immediately
from the type of affordable analytics described here.
Business Analytics for SMM
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1. Business Analytics for SMBs: Background
Business Analytics is “the extensive use of data, statistical and quantitative
analysis, explanatory and predictive models, and fact-based management to
drive decisions and actions.” [1] While this is a comprehensive definition of
business analytics, a much better appreciation can be had by looking at some
actual cases where analytics has made a difference. Some relevant examples in
the context of this report pertain to small and medium businesses, specifically:
•
A family owned Midwestern dairy farm [2] uses analytics to
 Root out used milk-bottle return fraud at dairy stores
 Quickly identifies and corrects manufacturing glitches by
mining customer complaint data
 Gain valuable insights on what factors would improve customer
satisfaction and retention
•
A family owned vacation home rental business in the South [3] uses
analytics to
 Track more than 15 competitors including large hotel chains to
make the most efficient resources allocation
 Manage more than 1100 vendors for a variety of service needs
ranging from lawn maintenance to pool and spa service.
•
A specialty clothing retailer [4] uses analytics to answer key marketing
questions such as
 When has a prospective buyer looked over the items in the product
catalog?
 How many times has each item in the catalog been viewed? What
is the ratio of product views to sales?
High capacity and process integrated data collection, sophisticated analysis tools
and expertise were three key advantages that large businesses traditionally have
over their smaller competitors.
So how can a SMB adopt some of these game changing technologies?
Today, SMBs have easy access to one of these three key ingredients – data.
Sophisticated analytics tools are also becoming very affordable, thanks to open
source [5]. With the maturing of cloud computing, deploying these solutions is
also becoming less of a challenge.
A central missing element for SMBs today is the expertise to build and manage
these models. In the next several sections we address how to fill in this piece of
the puzzle and answer the question raised above using one example application:
cost modeling and forecasting with predictive analytics.
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2. Objectives of Cost Modeling
Cost Modeling is a common activity for any business. Cost modeling is a
prerequisite for cost forecasting. However it can be a very challenging activity.
What is Cost Modeling? Why should an SMB worry about cost modeling?
Product development and manufacturing requires transforming raw materials into
finished goods. This requires obtaining the raw materials, processing them to
produce the desired goods, storing and distributing the finished goods. Obviously
all of these activities involve cost from the business' point of view. But these
costs are not constant and fluctuate on a daily, monthly or quarterly basis.
Let us use the example of a small and medium manufacturing (SMM) business to
demonstrate this. The typical customer for such a company is a large original
equipment manufacturer (OEM) such as an automotive or aerospace company.
OEMs however do not allow an SMM to vary the price of the finished good in
tune with the market prices of raw materials or processing costs. In order to
produce a part profitably, and yet compete successfully in the market, an SMM
must have solid estimates of input factors' (material, labor and transportation)
cost variability and how these will impact the ultimate cost of the finished part.
Cost Modeling allows one to capture all these costs, relate them to the final cost
of the product and helps develop accurate forecast ranges for the input factors'
costs.
Two major challenges in cost modeling
The first challenge is the information required for building an effective cost
model: it typically resides with multiple stake holders in the business as well as
outside the business. The challenge is to successfully integrate these disparate
sources of information, capture them in a "living" database to build effective
models.
The second challenge is the presence of uncertainty in the information. This
uncertainty could come from both the supply side and demand side. When
production volumes must swing between 100,000 to 200,000 parts, it adds
additional life-cycle costs. How do you account for these variabilities and still
come up with a cost estimate that allows managers to make a decision about
production? Under such situations, one needs to focus more on cost distributions
than single point cost estimates. But when optimal purchasing prices and
volumes are of interest, tracking and forecasting these strategic point values is
essential
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3. Using Predictive Analytics for Cost Modeling
3.1 Business Objective: Enable rapid data-driven cost forecasting
The key to successfully applying analytics is to correctly describe the business
problem and establish meaningful objectives for the analysis. In this application,
cost modeling was employed by L&L’s Purchasing team to develop estimates of
the overall cost of a given product to the company so that the Sales and
Marketing teams could develop winning and profitable bids.
Without predictive analytics however, these cost models were “built” by visually
comparing several dozen input factors which could potentially affect overall costs
of a product and combining this with past experience or “gut-feel” to develop
heuristics for cost forecasting.
Cost Model before using Predictive Analytics
Figure 1. Cost Modeling included tracking 40+ input factors such as indices from Bureau of Labor
Statistics, Petrochemical prices, Shipping indices, related commodity prices, Producer Price
Index, etc. The work was tedious and subjective.
Predictive analytics was employed to address two main business problems:
1. Build a reliable model that connected the factor inputs to product costs
2. Verify if this model then could accurately forecast product costs
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The objective was to provide a robust cost model with quantitative basis that
eliminated cumbersome and error-prone visual tracking of cost indices and
replace it with an automatic process and more objective data driven decision
making, with contributions from fewer, more clearly related factors.
3.2 Data Sources: Mostly public domain
From a technical perspective, the objective can be translated as follows:
 Build a model that connects factor inputs to product costs
 Verify the accuracy of this model on a test sample of data
 Employ this model to make predictions about product cost when key factor
inputs vary.
The actual data consisted of 42 time sensitive independent variables or
“predictors” and 7 dependent variables or “responses”. This data was available
on a monthly basis for the previous 48 months.
Data used to build the Predictive Analytics model
Figure 2. Cost Modeling data set. Nearly 80% of the data was public domain
information. The rest of the data came from purchased sources and in-house
manufacturing information.
3.3 Analytics Process: A 4-step approach
The sequence of steps followed a process starting with establishing and verifying
the validity of the model is a well-known 4-step process which includes Data
preparation, Variable Reduction, Model setup and Model Validation. The
schematic in figure 3 shows the different steps within this process. There are
typically several competing algorithms which can effectively perform the task of
modeling. In such a situation, inputs from the end-users of the model, the
Purchasing team in this case, becomes very important in the final model
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selection. The choice depends upon two factors: can the model structure be
easily understood and is it providing value. In this case, the choice of the
algorithm was dictated by the first factor.
4-Step Analytics process of building the cost model
Figure 3. Based on input from the end-users, a Forward Stepwise Regression model was chosen
for its easy interpretability.
This 4-step approach is fairly standard and can be implemented for any business
issue that can be addressed with predictive analytics. However, this is also the
part of the analytics implementation where expertise is most needed and can
consume significant amount of time and resources that SMBs seldom have.
SimaFore consultants are in the unique position of being industry veterans who
straddle several industry verticals and the analytics horizontal. It is SimaFore’s
goal to make analytics accessible and affordable to the SMB sector. To this end,
SimaFore’s analytics portal, visTASC, is aimed at developing awareness for
analytics consumers (SMBs) and helping analytics users (analysts) build
competency.
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3.4 Model Deployment: An App-based approach
For companies which do not have the analytics tools and expertise, and cannot
afford to spend the resources to acquire them, SimaFore offers a very practical
alternative. SimaFore will implement the 4-step process described above on a
consulting model where our experts will work closely with the SMB teams. The
objectives of this part of the solution are:
 to crystallize the business problem,
 identify, collect, clean, and organize the pertinent data
 build and validate the model.
SimaFore experts will develop the model using algorithms provided by open
source tools, such as RapidMiner, R and KNIME, which will result in a substantial
cost save for the SMB. The modeling technology chosen is completely
transparent to the business. Once the model is developed and accepted by the
SMB as a valid representation of the business problem, the solution can be
hosted on a public cloud by SimaFore. The SMB will have access to the model
via a basic dashboard or a Custom App as illustrated in figure 4. The app is
specific to a business problem that is addressed by the predictive analytics
model, such as the one described in this report.
Screenshot of a customized App dashboard
Figure 4. Dashboard of a predictive analytics model App that business users can use to simulate
what-if scenarios and generate predictions for parameters of interest. The model resides in a
cloud server and is updated/validated regularly.
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3.5 About Cloud computing and deployment
Innovation, it is said, happens when two distinct but related technologies merge.
This merger generates value that is greater than the individual value each would
separately bring. This has been the case since the gasoline internal combustion
engine merged with petroleum refining leading to the democratization of personal
transport, all the way to the more recent peer-to-peer sharing of documents with
high density memory chips, leading to the mp3 player.
Gartner technology consultants, who develop the technology hype cycle, have in
2011 positioned predictive analytics at the “mature” stage of its growth. However
cloud computing is still evolving and going through its “peak of expectations”
phase. But it is quite possible that with the merger of predictive analytics with
cloud computing, the resultant technology will catapult itself to the “productivity”
phase much earlier than cloud computing alone.
What is Cloud Computing?
Cloud computing refers to any activity where the computational resource
(memory, disk space and/or CPU time) is shared by several users and the
physical hardware itself is located miles (or several continents) away. By allowing
multiple users or “tenants” share resources, the provider can extract more value
from a fixed investment, as not all users may need all the resources at the same
time. This will in turn allow the cost of the resource to be spread across multiple
payers resulting in significant reduction in cost for each tenant.
Merging basic analytics models with cloud computing
The potential applications of the model described in this report are ideally found
in businesses which are beginning to discover predictive analytics. The
computational resource demand for the analytics model described in this report is
minimal. However for other more complex models, the demand – mostly memory
and CPU, can be quite substantial. James Taylor of Decision Management
Solutions [7] recommends that the ideal cloud computing solutions for such
applications tend to be pre-packaged. This means that the model is pre-built and
solution is readily deployed via a dashboard. Whatever minimal computation is
needed takes place in the cloud. This is precisely the scenario described in this
report.
App Dashboard
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4. Conclusions: ROI from using predictive analytics
As was shown in Figure 1, the process of building price estimates for complex
parts was very cumbersome. Data from several disparate sources had to be first
assembled. Then detailed charts had to be generated for all the 40-odd variables.
The final portion of cost estimation involved digesting this information, relying on
past experience and estimating a composite number for the ultimate cost drivers.
The process was tedious and prone to subjective assessments. In our case, it
took L&L purchasing team typically eight to twelve hours to develop a detailed
cost model.
Using predictive analytics for this process results in two main advantages:
1 Reduction of 40+ parameters to a core set of 4 manageable variables more
easily accumulated and forecast
2 Rapid evaluation of the impact of changes to these parameters on the final
cost drivers
This has resulted in a significant reduction of cost forecast development times
along with increases in forecast interpretability. Additionally, since the model has
been tested and validated, there is very little room for subjectivity in decision
making. It can take a few minutes to run a report for a new cost forecast and
going from data to decision making will be in the order of hours rather than
weeks.
Cost Modeling effort - Traditional versus Analytics based
Cost Modeling Effort
Effort (hrs)
12
10
8
6
4
2
0
Traditional
Predictive Analytics
Figure 5. Advantages of using analytics include not only time saved in developing a new
cost forecast, but also reducing the subjectivity in decision making process.
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The National Center for Manufacturing Sciences (NCMS) estimates that within
the industry, of the 300,000 manufacturing companies, 95% are considered to be
“small and medium” which account for two thirds of the total employment in the
sector [6]. Advanced analytics tools for engineering and design (such as
modeling and simulation) are effectively used by the larger companies (e.g. Ford,
Boeing, Caterpillar, etc.) to reduce time to market and improve product quality.
97% of the companies which use advanced analysis tools say that they cannot
function without these tools.
However the smaller and medium sized companies – the so-called “missing
middle” have very little or no access to these productivity tools: whether they are
for engineering modeling and simulation (M&S) or business optimization, and are
being left behind in the competitive race. Business analytics tools are essential
for commercial leadership. In a survey conducted by SimaFore [8] among 100+
manufacturing companies in the summer of 2011, it was seen that Cost
estimation and purchasing activities, followed by Sales and Marketing were
the most receptive functions for analytics tools among the SMMs.
There is clearly a need to provide accessible analytics of the type described in
this report. This can be a potential game changer for competitiveness of the
manufacturing industry.
The key takeaways from this report can be summarized as follows:
•
•
•
Using a combination of well-established data analytics process, open
source technology and cloud computing, we can build affordable analytics
tools customized for specific business needs
Employing predictive analytics for common business problems, such as
cost estimation and forecasting can significantly reduce the time to go
from data to decision making while simultaneously improving adoption and
accuracy
Adopting the data-driven approach that predictive analytics enables will
reduce the subjectivity in decision making
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References
1. http://practicalanalytics.wordpress.com/2011/08/13/analytics-as-a-serviceunderstanding-how-amazon-com-is-changing-the-rules/
2. Business Analytics: An inside perspective – Analytics in action at
Oberweis Dairy. Insights from a webinar in the SAS Applying Business
Analytics Series, February 2011.
3. http://www.informationweek.com/news/smb/hardware_software/22940262
0?pgno=1
4. http://www.noblivity.com/blog/2011/03/redefining-small-business-analytics/
5. http://www.informationweek.com/news/smb/hardware_software/22940309
1?pgno=1
6. http://www.ncms.org/index.php/multimedia/podcasts/
7. Predictive Analytics in the Cloud, James Taylor, CEO, Decision
Management Solutions, 2011
8. http://www.simafore.com/download-whitepaper-smb-analyticssurvey?utm_campaign=costmdlwp&utm_source=whtppr
About SimaFore
SimaFore was established with the express objective of helping small and medium businesses find the right analytics
tools for the right problems in order to convert data into information assets. Our team of experts come from different
backgrounds but the analytical techniques each have applied in their experience have several common threads:
using data to understand cause and effects, building models to simulate systems or processes, and using a
combination of analytics tools for forecasting. Our team shares a common vision - to help businesses remove the
complexity involved in applying and deploying analytics. To make analytics accessible and affordable to anyone who
has data. We invite SMBs to sign up for a preview of our specialized analytics portal here.
Contact SimaFore
330 E Liberty Lower Level
Ann Arbor, MI 48104, USA
248-686-2100
http://www.simafore.com
info@simafore.com
About the Authors
Bala (BR) Deshpande has 15 years of experience in the biomedical, automotive and consulting
areas. He holds an MS in Mechanical Engineering (University of Utah), an MBA (University of
Michigan) and a Ph.D. in Bioengineering (Carnegie Mellon University). Before founding
SimaFore, he was involved in engineering consulting, software publishing and automotive
product development for more than 12 years.
Follow him on Twitter at http://twitter.com/#!/complexman
Connect with him on LinkedIn: http://www.linkedin.com/pub/br-deshpande/6/302/861
Steven Reagan has 18 years of experience in the methods and practice of computational
methods. He holds a Ph.D. in Mechanical Engineering (The University of Virginia). He is
currently the manager for computational modeling at L&L Products, before which he was
involved in crashworthiness research at Ford Motor Company. Some of his research and
modeling interests include use of high performance computing for the numerical modeling of
nontraditional problems/processes, statistical design exploration, nonlinear transient modeling,
and optimization.
Connect with him on LinkedIn: http://www.linkedin.com/home?trk=hb_tab_home_top
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