Insights on driving a digital transformation based on research on new technologies, advisory work with clients, and quantitative research projects. This is a short handbook to help companies get started on their journey to define the digital supply chain.

1. Driving Digital Supply Chain
Transformation
A Handbook for Action
05/21/2017
By Lora Cecere
Founder and CEO
Supply Chain Insights LLC

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Contents
Research Methodology
Disclosure
Executive Summary
Step #1: Define the Digital Supply Chain
Step #2: Build a Guiding Coalition to Tackle
Next-Generation Processes
Step #3: Invest and Understand New Technologies
Sidebar
Recommendations
Conclusion
Appendices
Cognitive Computing Use Cases
Hyperledger Use Cases
Related Research
About Supply Chain Insights LLC
About Lora Cecere
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Research Methodology
This report is based on research on new technologies, advisory work with clients, quantitative
research projects, and interactions with manufacturing companies attempting to define a digital
supply chain transformation.
Disclosure
Your trust is important to us. In our business, we are open and transparent about our relationships. In
this research process, we never share the names of respondents, or give attribution to open-ended
comments collected during the research.
Our philosophy is, “You give to us, and we give to you.” We collect data from a private network of
qualified participants and openly share the results.
This report is written and shared using the principles of Open Content research. It is intended for you
to read and share freely with your colleagues, and through social channels like LinkedIn, Facebook
and Twitter. When you use the report all we ask for in return is attribution. We publish under the
Creative Commons License Attribution-Noncommercial-Share Alike 3.0 United States and our citation
policy is outlined on the Supply Chain Insights Website
.

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Executive Summary
American productivity in the third industrial revolution stalled in 2004. Why? Costs exceeded value.
We are now at the start of the fourth industrial revolution. The question for supply chain leaders is,
“How can the fourth industrial revolution have a long-lasting and positive impact?”
Figure 1. Annualized Growth Rates of Total Factor Productivity
This is a tough challenge. Most organizations focus on product innovation. In contrast, process
innovation is tough. While new technology options abound, most companies are late adopters,
saddled with limited budgets and the need to meet fixed Return on Investment (ROI) thresholds. As a
result, in today’s supply chain organizations the focus of projects is on yesterday's technology stack
of ERP/APS/CRM and SRM. (In this report, we are going to term this ‘yesterday’s alphabet soup of
traditional technologies’.)
These technologies improved the efficiency of functional processes, but they failed to improve overall
supply chain effectiveness. Today only 12% of publicly-traded companies are able to drive
improvement at the intersection of operating margin and inventory turns; and, business user
satisfaction with traditional technologies is low.

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There is a radical difference between automation and digital transformation. While automation makes
today’s processes more efficient, the digital transformation asks the question of "What is possible?"
This includes value chain disintermediation, changes in routes/methods, the redefinition of
manufacturing, and the use of different forms of data and advanced analytics. The successful digital
transformation team is questioning the atoms, electrons and flows of today's processes.
1. Atoms. Can substrates be manufactured with yeast? What is possible with reclaimed materials?
What are the possibilities with 3D printing?
2. Electrons. What is the impact of cognitive computing? Is there a need for manual manipulation of
master data with the evolution of artificial intelligence? Can an ontology enable process
automation? Can a community share resources/assets within a community (like Uber)? What is the
role of Hyperledger? How can the Internet of Things (IOT) transform processes?
3. Business Models and New Processes. Should goods flow through distributors, or directly to end
users? With a series of layered value-added services? What does an outside-in process look like?
How can new technologies help to sense true demand? What are the opportunities with new
business models?
Companies pushing digital transformation road maps understand that the answer to the future is not
in the automation of traditional supply chain processes. There is a clear understanding that historic
practices are no longer best practices. Digital transformation is very different by industry. No two are
alike. Here are some examples:
1. Redefinition of Medical Device Supply Chains. In traditional supply chains, medical implants
arrive at the hospital in the ‘trunk of a car’. This ‘trunk stock’ of implants is a critical component of
knee and hip replacements. With operating room uncertainty, many times multiple implants are
taken to the operating room. Often, in the haste of surgery, it is unclear which implant was used in
the operation. Many companies are trying to change the operating theatre to be more digital.
Instead of ‘trunk stock’, the concept is to 3D print the implant in the hospital based on digital films.
2. Replenishment at the Speed of Business. In the redefinition of coffee service, a restaurant
installed machines that consumers can use. The machines aggregate and transmit usage hourly
using the Internet of Things. The signals are used to drive nightly replenishment.
3. Contract Manufacturing Based on Digital Signals. A contract manufacturer with a long lineage in
innovation is redefining the processes to include 3D printing based on digital images; real-time
manufacturing updates based on Apache Spark; planning using cognitive computing; and business-
to-business messaging using Hyperledger.
4. Digital Manufacturing. The manufacturer segmented processes into those that could be 3D
printed (samples and special requests), processes that are better suited for collaborative robotics
(repetitive motion and non-value added tasks), and then transformed traditional manufacturing
using sensors and Apache Spark to redefine maintenance. Instead of taking machines down for

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maintenance based on predetermined times, the company now uses inputs from factory floor
automation to drive downtime for maintenance. (For example, when a pump motor’s temperature
rises to a certain level after a predetermined run time, maintenance is triggered.) In addition, the
factory is being converted to a paperless operation through the use of wearables and factory-floor
automation.
Note some similarities? The processes are being redefined, outside-in, through a confluence of
technologies. Each is different. It is not the evolution of traditional processes or the continuation of
Information Technology (IT) standardization.
For a supply chain leader, the tough part of this transition is determining when to automate and refine,
and when to transform. Most companies are still in the middle of process automation and
standardization. In today’s organization, both are needed. There is much still to do on IT
standardization and process evolution. Business users question when to stop automating and when
to start driving a digital transformation. In driving a digital transformation, there is continuous tension.
Let’s take an example. The movement of sales orders and purchase orders to hands-free, a goal of
the 1990s, is still low (see Figure 2). This poses the question, “Should companies invest in greater
B2B automation or experiment with Hyperledger and software robotics?” There are hundreds of these
questions.
Figure 2. Current Automation of B2B Processes
In this handbook we share insights to help you improve decision making and spur new thinking. We
recommend a three-step process.

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Step #1: Define the Digital Supply Chain
Ideate with a focus on a goal in mind. Solve real-world problems. Don’t dabble in experimentation.
Explore options realizing that there is no one right answer. Define what digital means for your
organization. For most organizations, the definitions usually fall into seven variants:
1. Autonomous Supply Chain: automation of supply chain processes through cognitive learning and
artificial intelligence, eliminating labor, and reducing the need for people to touch data.
2. Value Chain Uberization: a platform to enable shared resources across a community.
3. 3D Printing: localization of manufacturing through the sharing of digital images using additive
manufacturing.
4. Internet of Things: the use of machine-to-machine streaming data to improve supply chain
outcomes.
5. Multi-Tier Networks and Redefinition of B2B: the building and execution of multi-tier networks for
data sharing, collaborative workflows, and improved decision making. This includes a discussion of
blockchain, network canonicals, and interoperability.
6. Cloud-Based Computing. The promise of the cloud is the federation and democratization of data,
in both private and public clouds, with the potential to lower the cost of ownership.
7. Outside-in: The use of unstructured data—social sentiment, warranty, and quality data—to
improve organizational productivity in order to align processes outside-in, to orchestrate market-to-
market. (Market-driven is a more mature definition of demand-driven1
.)
In the process, challenge the group to think broadly, and fund the effort. (Do not limit the effort by
forcing it to have to conform to a set of Return on Investment goals. Instead experiment with the goal
in mind.) Hold the group accountable to seven basic principles:
1. The efficient supply chain is not the most effective.
2. Automation is not the same as a digital transformation.
3. Process innovation is different than process improvement.
4. The effort cannot be technology for the sake of technology. Define and focus on value.
5. There are no best practices. Instead, there are historic processes.
6. Innovate with innovators. Best-of-breed technologists are innovating faster than the market. To
drive innovation do not hold the group hostage to IT Standardization objectives.
7. There are no sacred cows. Give the group the freedom to think past today to push forward for
tomorrow. Learn from the past, to unlearn what?
1
What Drives Inventory Effectiveness in a Market-Driven World, Supply Chain Insights,
http://supplychaininsights.com/portfolio/what-drives-inventory-effectiveness-in-a-market-driven-world/, May 22, 2017

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Step #2: Build a Guiding Coalition to Tackle
Next-Generation Processes
The digital transformation is an opportunity to challenge and invigorate the workforce. Large
companies with strong brands and traditional process approaches have the least satisfied employees.
Let’s take a look at the data.
In a recent survey on Supply Chain Talent among 500 respondents, the most satisfied respondents
were technologists and academics by profession, Baby-Boomers by age group, and those with the
ability to work from home. These results are shown in Figure 3. The least satisfied are those in
manufacturing/distribution work teams.
Figure 3. Relative Satisfaction
One of the areas causing dissatisfaction is the lack of thinking and innovation on next-generation
supply chain processes. With the passing of the generational baton, Millennial workers struggle to get
access to opportunity to make a difference. Traditional supply chain thinking is a deterrent.
What does traditional supply chain thinking look like? It comes in many forms. Respondents report
concern about the usability of traditional supply chain systems. They want supply chain systems that
mirror their mobile phones and tablets in their personal life. They also want clear career paths with

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cross-functional and upward mobility. As shown in Figure 4, and consistent with our interviews, many
supply chain middle managers feel stuck in insignificant positions with limited career mobility.
Figure 4. Top Supply Chain Challenges
Build talent to staff digital processes. Today's manufacturing organizations struggle to find talent at
the intersection of analytics and planning. While academics are more likely to feel that the positions in
greatest demand are in the areas of demand planning and data science, supply planning is equally in
demand. With more and more outsourced manufacturing, companies are struggling to build a
comprehensive understanding of manufacturing and supply for today's leader. The use of internships
and cross-functional projects help, but this gap will continue to grow with the retirement of Baby-
Boomer leaders. Use the digital transformation as an opportunity to build talent and train the
workforce on new skills.

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Figure 5. Positions in the Highest Demand
Step #3: Invest and Understand New
Technologies
In the design of the digital supply chain, evaluate process design based on a confluence of
technologies. Spend time to understand what is possible. Today’s processes are based on the
technologies of the 1990s. Challenge the team to reskill to define the art of the possible. To get
started, reference Table 1.
While Enterprise Resource Planning (ERP) is still the system of record, the importance of traditional
ERP providers shifts with a focus on cloud, streaming and open source analytics. Design analytics
solutions to stretch across systems through clouds, to move at the speed of sensor and telematics
data in streaming data architectures, and fuel systems of insights through data lakes. While traditional
processes were limited through simple rules that were based on simple if-then statements, the
evolution of cognitive computing enables learning systems dependent on multiple ifs to multiple thens
that shift as the business changes.

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Table 1. Shifts in What Is Possible Based on Technology Evolution
Look for process automation using the confluence of new technologies. Explore the possibilities of the
technologies shown in Figure 6. The greatest value is coming from the combination of multiple
technologies. For inspiration reference the use cases for cognitive computing and Hyperledger in the
Appendices.
Figure 6. Confluence of Technologies
Sidebar
An example of the current and future state of planning is shown in Table A. This is an example of a
digital transformation.

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Table A. The Digital Transformation of Planning Based on Cognitive Computing and Voice Recognition Software

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Recommendations
Build the Digital Supply Chain Transformation with the goal in mind. Start with a focus on an initiative
of cross-functional importance. In the process, keep these seven recommendations in mind:
1) Manage Hype Cycles. Avoid fads. No one technology—despite how promising it sounds—
is the end-state. Form a process innovation team and stay grounded in expectations.
2) Innovate. Form a Scrappy Team and Test & Learn. The fastest innovation happens
when companies form scrappy teams focused on test and learn. These teams usually have
team members from all generations of the workforce. Individuals are selected based on
their levels of process understanding and their willingness to challenge the status quo. Start
small and drive innovation.
3) Get Good at the Use of New Forms of Analytics. New forms of analytics are central to
most transformations. Invest in talent to learn new analytical techniques.
4) Educate. Expose employees to new types of thinking. This includes internal technology
fairs, visits to innovation centers, attendance at conferences and webinars, internal sharing,
and formal training. Realize that this type of training will not be the standard supply chain
courses.
5) Challenge and Question the Status Quo. As a leadership team, be open to the outcome.
Charter the digital transformation at the operating leadership team level and encourage the
group to question the status quo. Avoid business process outsourcing and the use of large
consulting organizations.
6) Learn from Other Industries. Insurance and financial industries are innovating faster in
analytics than manufacturing and distribution industries. Likewise, the Department of
Defense is driving major innovations in cognitive computing and streaming data
architectures. Learn from all industries. Do not focus only on innovation within your
industry.
7) Move at the Speed that Matches Your Cultural DNA. Not all companies are early
adopters. If your company is typically a late adopter, follow industry innovation and adopt at
a comfortable speed. In the industry, we find three times more late adopters than early
innovators.

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Conclusion
Today we do not know what is possible, but we do know that the supply chain of the future will not
look like today’s supply chain. Tomorrow’s supply chain will be transformed through digital innovation.
Early adopters will gain competitive advantage and drive new business models. However, driving a
digital transformation is not for the faint of heart. It requires funding, as well as courage to question
the status quo. Initiatives with the highest probability of success are championed at a senior level
based on a series of important goals.

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Appendices
Cognitive Computing Use Cases
Cognitive computing is the simulation of human thought processes using a computerized model. It
is based on a number of technologies including data mining, pattern recognition, and natural
language processing to mimic the way the human brain works in supply chain processes.
• Data Mapping. The average company has 5 to 7 Enterprise Resource Planning solutions. While
the solutions are sourced from the same provider, each has a different data definition. It is difficult to
map data from one to the other. The use of cognitive learning with a rules-based ontology enables
the mapping of data with different context.
• Automation of Master Data Management and the Replacement of Data Standards. Today
master data is hard-coded. The data definitions are manually determined and mapped. They are
inflexible. As the business changes, the data becomes obsolete. With cognitive learning we redefine
master data. It is adaptive.
• Redefinition of Rules. In the first generation of planning technologies the rules were single “ifs” to
single “thens.” Rule sets –like ATP, VMI, Transportation Routing, Allocation and Assortment logic–
are simple. Too simple to meet the business need. The problem is they are not robust enough. An
order is not an order. A customer is not a customer. An item is not an item. Each status needs
mapping based on business rules. Cognitive learning will redefine current supply chain rules.
Today’s supply chain does not play by the rules; however, we try to constrain and limit the options
through rule definition that limits the possibilities.
• Rethinking Demand Management. Today companies try to get very precise on imprecise data.
Rows and columns define forecasting. Companies lose visibility on the patterns and demand flows.
Cognitive learning solutions provide systems of insights that can combine profile pattern recognition
along with learning on unstructured data. Examples include the number of google searches on an
illness or symptoms, which is a predictor of the spread of an illness and subsequent prescription
sales. Social sentiment on Twitter and Facebook combined with point-of-sale data drives insights to
understand regional sales in days. Today it takes weeks.
• Planning Master Data. I only know a handful of companies that have a planning master data layer
feeding their supply chain planning engines. Most companies install supply chain planning
solutions, stabilize the implementation, and then forget about them. The data parameters of lead
times, cycle times, and rates quickly become outdated. In most cases this data is a variable, not a
constant. For example, moving rail through Chicago in the winter is not the same as the summer.

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Unloading a container in Long Beach varies by the season; yet, the planning systems have a fixed
value.
• Rethinking Decision Support. Building Systems of Insights. Cognitive computing, layered on
existing decision support tools—revenue management, trade promotion management, demand
planning, production planning, transportation planning and supply network planning–drives deeper
insights. The goal is to stabilize the current solutions, and layer cognitive solutions on top of more
traditional solutions like Logility, JDA, and SAP APO.
• Listening Posts. Today’s analytics drive answers on the questions we know, but we cannot track
the important data that answers the questions which we do not know. Examples are many. Why are
consumers raising concerns on quality? What do patterns tell us?
• Quality. Many production environments—coolers, dryers and distillation columns—are complex
with many variables. Once the variables exceed 5 to 7 it is hard to model outcomes without
collinearity. Cognitive learning enables new forms of insights to better control quality.
• Customer-Centric Supply Chains. Cognitive learning is ideal to map customer policies to
fulfillment. The dynamic nature of inventory to order matching is difficult. This is an area of great
opportunity.
• Network of Networks. The connection of data between trading partners using cognitive computing
along with Hyperledger.
Hyperledger Use Cases
Hyperledger is a digital ledger in which transactions made are recorded chronologically and publicly
through an immutable record. It goes by many names. Hyperledger is the Open Source form of
blockchain.
1. Community Registry. Today, network registration is onboarding to every network as an individual
or as a company. It lacks the system of reference of division/company or company/industry. What if
we could have a community registry where we have a single sign-on which could be accessed by
all value networks? (Analogous to network DNS.) This numbering schema would be carried in
blockchain messaging, enabling writing information once, and safe/secure communication across
the network.
2. Replacement of EDI. Today EDI is the workhorse of the supply chain. Messages are transmitted
and opened safely and securely. However, it is batch, and there is latency as the message is
opened. In addition, the passage and receipt of EDI requires sophisticated IT groups. As a result, it
is costlier. (This is not always a reality for small companies in emerging economies.) Could
blockchain replace EDI? This is a stretch objective, but I think it’s possible.

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3. Lineage. Track and Trace. Track and trace across multiple parties is cumbersome and lacks
reliability. Blockchain offers the ability to embed the origin and transfer points in the chain to encode
lot codes, origin points and destinations. This could help in food track and trace, the management of
gray market goods to eliminate counterfeit items, and the assurance of origin for tracking social
responsibility goals for fair labor, Congo metals, etc. This will enable compliance and could
streamline recalls.
4. Safe and Secure Supply Chains. As goods pass through the supply chain, multiple parties handle
the goods. Blockchain technologies enable a chain of custody. In the process, the requirements of
each product for handling are communicated on receipt. Why is this an issue? We discard 1/3 of
perishable items. What if the use of blockchain, along with temperature sensing, could redefine
code dates, real-time, based on handling for perishable items like fruits/vegetables and chicken?
And, in extreme cases of temperature or handling abuse, indicate disposal? (This is extremely
important in pharmaceutical cold chains.) Or, what if the conditions for handling could be read on
receipt to enable safe put-away for flammables?
5. Improving Social Responsibility Goals. Tracking carbon and point of origin for compliance is
difficult. One thing is clear: audits do not work. As we tackle issues like fair labor, clean water,
Congo metals, and carbon consumption, blockchain can track the chain of custody and help us to
better understand and measure energy consumption, carbon emissions and other social
responsibility goal tracking
6. Supply Chain Finance. The origin of blockchain is safe and secure payment. Could we
disintermediate banks as we know them? Each time a supply chain transaction passes through a
bank, there are charges. Could we drive a massive restructuring of world banking to reduce bank
charges for credit cards, wire transfers, and EFT/ACH payments?
7. Document Sharing. I think the world has too many lawyers. In supply chain, we spend hours upon
hours negotiating terms and conditions of contracts. After completion, the filed contracts are never
used again. We do not connect the contracts to supply chain execution. So, what if contracts could
accompany a PO, and as conditions change, rules change the cost based on delivery conditions?
Or delivery conditions, based on availability (dynamic dock scheduling) and weather? This is all
possible, but must be tested and proven.

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Related Research
Supply Chain Insights regularly publishes reports. Unlike other industry analyst groups—who keep
research behind a paywall—we share research openly to help all global supply chain leaders. All of
the research is archived in our community on the Supply Chain Insights website, on SlideShare, and
on Beet Fusion for social sharing. To gain an understanding of supply chain excellence, check out
this related research:
In Search of Supply Chain Excellence
Driving Improvement Through Supply Chain Centers of Excellence
Improving Supplier Reliability
Supply Chain Visibility in Business Networks
Supply Chains to Admire 2015
Supply Chains to Admire 2016
Supply Chain Talent Evolving Across the Generations
The Global Supply Chain Ups the Ante for Risk Management

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About Supply Chain Insights LLC
Founded in February 2012 by Lora Cecere, Supply Chain Insights LLC is beginning 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 September 2015, and the fifth book, The Shaman’s Journal
2016, published in September 2016. A sixth book will publish in September 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.