Driving Digital Supply Chain Transformation

A Handbook for Action

05/21/2017

By Lora Cecere

Founder and CEO Supply Chain Insights LLC

Page 2

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

3

4

4

7

8

10

11

13

15

16

16

17

19

20

20

Page 3

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

.

Page 4

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.

Page 5

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

Page 6

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.

Page 7

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

Page 8

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

Page 9

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.

Page 10

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.

Page 11

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.

Page 12

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

Page 13

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.

Page 14

Page 15

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.

Page 16

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.

Page 17

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.

Page 18

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.

Page 19

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

Page 20

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.