The document discusses rethinking supply chain analytics. It notes that traditional supply chains rely on structured data and Excel, but new forms of analytics using technologies like blockchain and cognitive computing will disrupt current approaches within 5 years. Barriers to adopting new analytics include a lack of alignment between business and IT teams and a need to re-skill employees. The document recommends testing new technologies through agile sprints, embracing disparate data through schema-on-read, and placing analytics at the core of digital transformations.

1. Rethinking Supply Chain Analytics
Market Insights
01/23/2017
By Lora Cecere
Founder and CEO
Supply Chain Insights LLC

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Contents
Research Methodology
Open Content
Disclosure
Executive Overview
Current State
The Future of Analytics
Barriers
Recommendations
Conclusion
Appendix
Other Reports on Analytics
About Supply Chain Insights LLC
About Lora Cecere
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Research Methodology
New capabilities from analytics are redefining processes and reshaping supply chain strategies. The
terms are new, and many of the concepts are a radical departure from traditional thinking. In this
report, we share insights from a recent research study that combines both business users’ and
vendors’ (technology providers, consultants, and third-party logistics providers) views.
A study overview is shared in Figure 1, and detailed demographics are outlined in the Appendix.
Figure 1. Study Overview

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Open Content
This report is shared using the principles of Open Content research. It is intended for you to read,
share, and use to improve your supply chain decisions. Please share this data freely within your
company and across your industry. All we ask for in return is attribution when you use the materials in
this report. We publish under the Creative Commons License Attribution-Noncommercial-Share Alike
3.0 United States and you will find our citation policy here.
Disclosure
Your trust is important to us. In our business, we are open and transparent about our financial
relationships and our research processes; and, we never share the names of respondents or give
attribution to the open comments collected in the research. This research was 100% funded by the
Supply Chain Insights team.
In the development of our research, our philosophy is, “You give to us and we give to you.” As a part
of this philosophy, we share the data, and if interested, our insights, with all the respondents. We are
committed to delivering thought-leading content. It is our goal to be the place where visionaries turn to
gain an understanding of the future of supply chain management.

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Executive Overview
Analytic strategies are at the core of digital innovation. It is a building block in digital manufacturing,
autonomous supply chains, and digital path to purchase. New forms of analytics are defining new
capabilities.
Traditional supply chains do not sense. They respond. The response is usually late, and out of step
with the market. Today’s supply chains are dependent on structured data and Excel spreadsheets.
Despite spending 1.7% of revenue on Information Technology (IT), Excel ghettos are scattered
across the organization. Most organizations are held hostage by long and grueling ERP
implementations only to find out at the end of the project that the business users cannot get to the
data.
The traditional supply chain paradigm is an extension to the three-letter acronyms which dominated
the client-server architected world of the 1990s—ERP, APS, PLM, SRM, and CRM—while the more
enlightened business user understands that analytics are not an extension of yesterday’s alphabet
soup.
Historically, analytics has only meant reporting. In contrast, today, analytic strategies are at the core.
As analytics capabilities morph and change, analytics technologies are at the core of the architecture,
sandwiched between the conventional applications and workforce productivity tools as shown in
Figure 2.
Figure 2. Analytic Strategies at the Core of Digital Transformation

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Current State
Today, the focus of analytics implementations is on data visualization, unstructured data mining, and
data lake technologies. As will be seen in this report, this is rapidly changing. Within five years, the
most disruptive technologies will be Blockchain and cognitive computing. New forms of analytics will
make many of today’s technology approaches obsolete. Few companies, mainly early adopters, are
working in these areas.
Figure 3. Current Investment Focus for Business Users

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As shown in Figure 4, user satisfaction with the current state of analytics is low. A large opportunity
lies ahead.
Figure 4. Current Satisfaction Rates with Analytics
The Future of Analytics
While the levels of investment today in Blockchain and cognitive computing is low, five years from
now these technologies will be major disruptors (see Figure 5). Blockchain will redefine lineage,
supply chain finance, track-and-trace, and visibility, while cognitive computing will redefine workflows,
master data management, decision support, and demand insights. The number of solutions is
endless, but the technologies are not ready for prime time. These capabilities are still only appropriate
for the early adopter willing to invest in co-development.

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Figure 5. Predicting the Future and Analyzing Disruption
Barriers
In a digital transformation, teams redefine the atoms and electrons of the supply chain. This includes
process flows, conversion and transport processes, and new capabilities enabled by new forms of
analytics. Imagine a supply chain that senses, learns, and adapts in response to the market, one
where data is available at the pace of business. It is hard to imagine because it is so different from
today's reality.

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Figure 6. Coalescence of Technologies to Drive a Digital Transformation
To imagine a future built on new forms of analytics requires throwing away current paradigms and
learning new techniques. Embracing the possibilities has many challenges. For business leaders, the
greatest issues, as shown in Figure 7, are the alignment between business and IT, employee skill
levels, and management of the rate of change. These issues are not trivial.
Over the last decade, the gaps between business and IT teams grew. The reasons are many.
Contributing factors include business process outsourcing, downsizing, right-sizing, cost-cutting and
baby-boomer retirements. Client-server technologies are quickly becoming legacy. Business leaders
are frustrated with long deployments, and IT leaders are struggling with staffing. Many built strategic
plans believing that consultants could help them with specialty skills, but the market is flooded with
generalists. IT is managed as a cost center, and business leaders are seeking value. Is it any wonder
there is a disconnect?

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Figure 7. The Challenges for Companies Attempting to Implement New Forms of Analytics
The opportunity is high. The pace of change, and the shift of market dynamics, in the last three
decades is unprecedented. Traditionally, the focus on IT was implementation. Today, with the
evolution of cloud-based deployments, the need is configuration and evolution. In short, how can
companies maximize the value of current systems? The pendulum is shifting, requiring business
users to be more fluent in the world of IT, and technologists to have a greater technology
understanding.
It is not easy. Most organizations have downsized IT and line-of-business teams. The result? A
decline in the effectiveness of IT systems implemented over the last decade. Neither team has
excess resources, and the business struggles to speak the language of IT, while the IT resources
quest to know more about the business. Most are at a stalemate.
In this process of moving forward, I am often asked, “How can we deploy new forms of analytics

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without fixing the basics?” The caution is that perhaps new forms of analytics are the answer to fix the
basics. To understand the possibilities, carefully define what "fix the basics" means and start to
brainstorm how new approaches can make a difference.
Next, start to question existing paradigms. The resultant architectures are less dependent on manual
touches and licensed software. They are less ERP-centric.
Build with the goal in mind, and with clear definitions. Focus on the automation of meaningful use
cases that improve business usability and return on investment. Data lakes and clouds offer new
alternatives to reduce costs and improve value.
Figure 8. Criteria Driving Analytic Strategies

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Recommendations
In the process of forming an analytics strategy, question existing paradigms.
Question the Role of Enterprise Resource Planning (ERP). In the exploration of new possibilities,
question long, multi-year ERP implementations. The lengthy deployments are an opportunity cost to the
organization. In a digital transformation, the role of ERP shifts to be less central. The ERP system
becomes a system of record for financial transactions however, the role within the supply chain
diminishes. In the digital transformation, ERP is not the system of record for B2B, quality specifications,
or supply chain planning.
Go Fast to Go Slow. Test the possibilities of new technologies. Test and learn through small agile
sprints. Fail forward and invest in learning. Avoid conference room discussions based on slideware, and
work with technology innovators to test and learn. Today, companies struggle to test new approaches
(see Figure 9). Invest in a team of resources to drive process innovation through new forms of
analytics.
Figure 9. Current State of Analytic Strategies
Agile Sprints Require Agile Procurement Processes. While traditional IT projects are based on RFP
selection criteria and long, drawn-out selection criteria, the test-and-learn agile sprint enables the
organization to test-and-learn nimbly. Set the expectation that it is ok to fail.

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At the Beginning of a Project, Shake Hands Affirmatively with Master Data Management. A
common misconception is that a digital transformation needs to start with the cleansing of master data.
Instead, the digital transformation embraces disparate data and leverages machine learning to
harmonize and synchronize data sources. Grab the tiger by the tail. Use "schema-on-read"
technologies and rely on prescriptive, and cognitive, computing to drive insights. The current issues
with master data were largely driven by the focus on transactional systems requiring "schema-on-write"
(fixed data structures/hierarchies).
Rethink the Possible. While the traditional analytical approaches were based on analytics as an add-
on to what I term alphabet soup (ERP, APS, WMS, CRM, SRM, SCE), in a digital transformation
analytics are at the core. While the current focus is on data visualization using descriptive analytics,
slowly the industry is embracing unstructured data and new forms of analytics to redefine decision
support. The goal is to power data-driven processes that move at the speed of business. For most
businesses this is a great opportunity.
Conclusion
New forms of analytics offer great potential and opportunities. The greatest value will happen when
companies consciously test-and-learn, and redefine processes based on new capabilities. Core to
this journey is the alignment of IT and business with a focus on value and business outcomes.

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Appendix
In this section we share the demographic information of survey respondents, as well as additional
charts referenced in the report to substantiate the findings. The participants in this research answered
the survey of their own free will. There was no exchange of currency to drive an improved response
rate. The primary incentive made to stimulate the response was an offer to share and discuss the
survey results in the form of Open Content research at the end of the study.
The names, both of individual respondents and companies participating, are held in confidence. The
demographics are shared to help the readers of this report gain a better perspective on the results.
The demographics and additional charts are found in Figures A–E.
Figure A. Respondent by Role

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Figure B. Overview of Company Demographics
Figure C. Characteristics of Respondents by Industry

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Figure D. Characteristics of Respondents by Role within the Company
Figure E. Respondent by Role

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Other Reports on Analytics
Readers may gain added value by accessing complimentary reports on the Supply Chain Insights
website:
Putting Together the Pieces Supply Chain Analytics, September 2016
Big Data and Analytics, February 2015
The Art of the Possible, February 2013

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About Supply Chain Insights LLC
Founded in February 2012 by Lora Cecere, Supply Chain Insights LLC is in its sixth year of operation.
The Company’s mission is to deliver independent, actionable, and objective advice for supply
chain leaders. If you need to know which practices and technologies make the biggest difference to
corporate performance, we want you to turn to us. We are a company dedicated to this research. Our
goal is to help leaders understand supply chain trends, evolving technologies and which metrics
matter.
About Lora Cecere
Lora Cecere (twitter ID @lcecere) is the Founder of Supply Chain Insights LLC and
the author of popular enterprise software blog Supply Chain Shaman currently read
by 15,000 supply chain professionals. She also writes as a Linkedin Influencer and
is a a contributor for Forbes. She has written five books. The first book, Bricks
Matter, (co-authored with Charlie Chase) published in 2012. The second book, The
Shaman’s Journal 2014, published in September 2014; the third book, Supply
Chain Metrics That Matter, published in December 2014; the fourth book, The
Shaman’s Journal 2015, published in August 2015, the fifth book, The Shaman’s Journal 2016,
published in June 2016 and the sixth book, The Shaman’s Journal 2017, published in July 2017.
With over 14 years as a research analyst with AMR Research, Altimeter Group, and Gartner
Group and now as the Founder of Supply Chain Insights, Lora understands supply chain. She has
worked with over 600 companies on their supply chain strategy and is a frequent speaker on the
evolution of supply chain processes and technologies. Her research is designed for the early adopter
seeking first mover advantage.