The Role of Analytics In Defining The Art Of The Possible

EMBRACING
THE ART OF THE
POSSIBLE
Unleashing New Levels of
Value With Analytics
Lora Cecere
Founder and CEO
Supply Chain Insights LLC
January 2022

2 ANALYTICS: RESEARCH INSIGHTS ON EMBRACING THE ART OF THE POSSIBLE // 2021

3
ANALYTICS: RESEARCH INSIGHTS ON EMBRACING THE ART OF THE POSSIBLE // 2021
TABLE OF CONTENTS
Research Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Disclosure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
Executive Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Why Analytics Projects Struggle And Fail . . . . . . . . . . . . . . . . . . . . . . . 6
Current Investment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
The Role of Analytics in Digital Transformation . . . . . . . . . . . . . . . . . . . 9
Working in the World of Gray . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
Building Talent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Analytics Primer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Thinking Out of the Box Starts by Defining the Box. . . . . . . . . . . . . . . . . 16
Using Analytics In the World of Gray . . . . . . . . . . . . . . . . . . . . . . . . 19
Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Improving Supply Chain Visibility . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Other Reports in This Series: . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
About Supply Chain Insights LLC . . . . . . . . . . . . . . . . . . . . . . . . . . 24
About Lora Cecere . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Open
Content
Research
This report is shared using the principles of Open Content
research. The goal is to share research widely to improve
business value. We welcome sharing this data freely within your
company and across your industry. All we ask for in return is
attribution. Supply Chain Insights publishes using the Creative
Commons License Attribution-Noncommercial-Share Alike 3.0
United States, and you will find our citation policy here.
Research
Methodology
This research is based on the response of ninety-four supply
chain leaders in September 2021 to a quantitative survey. We
supplement the insights with insights from our ongoing work
with business teams.
The primary source of respondents was LinkedIn. We share the
details on the respondents in the demographic section of this
report.
Disclosure
Your trust is important to us. We are open and transparent
about our work’s financial relationships and research
processes. We never share respondents’ names or give
attribution to the available comments collected in the research.
This research was 100% funded by the Supply Chain Insights
team.

5
Today, only one in two business leaders are satisfied with their
ability to use analytics. Getting the correct data in a usable form
to make decisions at the speed of business is a barrier for 76%
of respondents surveyed. Available data and insights are critical
to making the proper supply chain decisions.
Over the decade, despite a preponderance of new solutions
and the presence of an analytics Center of Excellence in one
out of two companies, few companies are testing new forms of
analytics. In this study, only 4% of manufacturers are “the first to
try.” Most are conservative. Over sixty percent of manufacturers
consider themselves laggards. As will be seen in this report,
talent development and structuring test and
learn pilots are opportunities.
______________________________
Definition: Analytics is the systematic
computational analysis of data or statistics.
It is used to discover, interpret, and
communicate meaningful patterns in data.
Source: Wikipedia
______________________________
We find most leaders intuitively understand that there must
be value in new forms of analytics but struggle internally to
test and learn to drive new levels of value. The reasons are
many, but an overarching issue is the inability to build a guiding
coalition to actualize the Art of the Possible. The barriers are IT
standardization, organizational knowledge, and the requirement
for a fixed ROI. To capture the promise of new forms of
analytics, teams must abandon the conventional project-based
implementation mindset.
KEY RESEARCH FINDINGS INCLUDE:
1. Companies with data-driven processes and more advanced
descriptive analytics capabilities fared significantly better
during the first year of the pandemic.
2. While organizations tout digital transformation, the current
focus is on digitization (making traditional processes faster
and touchless). Companies with digital transformation
reporting to the Information Technology (IT) department
versus line of business are less likely to embrace newer
forms of analytics.
3. Organizations struggle to actualize
the promise of supply chain planning.
4. Progress on visibility stalled during
the decade—success in visibility projects
that requires a holistic understanding and
embracing disparate data.
Less than 5% of manufacturers are attempting
to drive value through new forms of analytics—
NoSQL, cognitive computing, semantic reasoning, unified data
models (to combine and use disparate data), and unstructured
data mining. Most organizations are stuck in a relational data
model mindset with a narrow view of analytics.
Analytics means different things to different teams. The first
step is to align organizationally. This report is designed to
educate and inform. The goal is to help business leaders drive
new levels of value in this highly variable world of demand and
supply.
Executive Overview
______________________________
Definition: Art of the Possible makes things forever considered impossible into provably and
usefully possible. Teams use analytics to unleash the art of the potential understanding of what is
possible and work to actualize answers to problems that seem impossible at the time.

Supply chains are complex and highly variable. In this study, the
average respondent reported an average of 50 manufacturing
sites and 20 distribution centers. Flows of products and
services are constantly changing; yet, IT systems are fixed
and inflexible. Success requires embracing the supply chain
as a complex non-linear system with constraints. From our
discussions with business leaders, we find five significant
issues.
• Limitations of Current Approaches. Historic investments
are insufficient to understand the impact of variability
on reliability. Over the last decade, 93% of organizations
embarked on an “enterprise-
centric” roadmap focused on
Enterprise Resource Planning
(ERP) using relational database
technology. Despite the
deployment of supply chain
planning, companies base 93%
of decisions on Excel-based
spreadsheet analysis. Decisions
based on spreadsheet analysis
fail in showing the impact of
variability and constraints.
• Need for Sensing. In the
traditional approach, supply
chains respond but do not
sense. Policies and rules are
not aligned to the output of
optimization engines. As a result, organizations cannot
adapt..
• Functional Automation Is Limiting. Current approaches
are functional, assuming the availability of well-defined and
pristine data. In this changing world, this is not reality.
• Inflexibility to Use Market Data. The traditional approach
of relational database technologies focusing on process
integration is too limiting.
• Focus on Integration. To build adaptive systems to
overcome these issues, the mindset needs to shift from
integration to interoperability. The
move from integration (moving
known data through fixed formats)
to interoperability (embracing data
flows across disparate systems) is a
paradigm shift.
______________________________
Definition: Interoperability enables
the effective exchange of data from
different software and applications
to improve usability. Interoperability
is a lynchpin of supply chain agility.
There are three forms technical,
semantic reasoning, and process
logic.
Why Analytics Projects
Struggle And Fail

7
Business user deployments of analytics are conservative,
focused less on process innovation, and driven primarily by
the need for a definitive Return on Investment (ROI). As shown
in Figure 2, the top three investment areas are descriptive
analytics, data lake deployment, and machine learning.
As shown in Figure 2, less than 1% are testing NoSQL
approaches, and fewer than 13% are actively deploying newer
analytics approaches for open-source analytics, semantic
reasoning, cognitive computing, and sentiment analysis.
During the last decade, blockchain was overhyped. While over
50% evaluate blockchain pilots, the deployments are less
than 4%. The only effective public supply chain blockchain in
the market today is the TradeLens product (a joint venture by
Maersk and IBM).
As shown in Figure 3, today, only 25% of business leaders
believe that they are investing the right investment resources to
evaluate analytics strategies properly; however, they struggle to
drive change in IT investments due to issues on organizational
alignment.
While machine learning and Internet of Things technologies are
considered the most disruptive technologies today, Cognitive
computing will rise in importance to help business leaders to
solve problems within five years.
Quotes from Discussions with Clients on Blockchain
Deployments:
“Smart contracts are neither ‘smart’ nor are they ‘contract’.
Instead, they are a way of automating supply chain rules.”
“The deployment of blockchain is a team sport. The
problem is that in the supply chain, there is no good
definition of a team.”
“Blockchain projects will only be successful in value chain
deployments when there is a power broker that can drive a
guiding coalition of players with a common purpose.”
Current Investment
63% 97%
53% 72%
43% 77%
37% 71%
34% 76%
29% 71%
21% 53%
13% 58%
13% 71%
11% 50%
10% 40%
4% 50%
25%
15%
11%
11%
9%
4%
4%
4%
4%
4% 23% 23% 42% 9%
6% 13% 17% 51% 9%
9% 32% 26% 17% 11%
2%
2% 9% 26% 13% 28% 21%
11% 19% 26% 26% 15%
17% 9% 23% 28% 19%
25% 17% 25% 19% 11%
25% 23% 19% 19% 6%
26% 21% 13% 13%
15%
32% 21% 13% 17% 6%
38% 11% 8% 15% 13%
38% 25% 9% 4%
Data visualization
Data lakes for data mining
Machine Learning
Software robots
Internet of Things
Pattern Recognition
Hadoop/open source analytics
Cognitive computing
Unstructured data mining
Sentiment Analysis
Drones
Blockchain
Deploy
ment+
Evalu-
ating+
Mainstream Adoption Live Deployments Experimentation / Pilot Program
Evaluating No Interest Don’t Know
Source: Supply Chain Insights LLC, Analytics Digital Transformation Study
Base: Users (n=53)
Q27. What is your typical company’s current level of investment in the following analytics strategies?
Figure 2. Current Levels of Investment and Deployment
TOP 3

Q28. In your personal opinion, how would you rate the LEVEL OF INVESTMENT that your company is
putting into analytics strategies today?
Significantly Higher than TOTAL at 80% Confidence Level
Base: Total (n=97)
Q30. Of these same analytics strategies, what three do you believe are the most disruptive to supply
chains TODAY/IN FIVE YEARS?
Figure 3. Evaluation of Current Levels of Spending on Analytics Strategies
Figure 4. Most Disruptive Technologies
Personal Opinion on Current Level of Investment in Analytics Strategies:
Users vs Vendors
Way too little
investment
Somewhat too little
investment
Just the right amount
of investment
Somewhat too much
investment
Way too much
investment
Users Vendors
18%
58%
13%
7%
4%
28%
43%
25%
4%
Machine Learning 66% Machine Learning 56%
51%
Internet of Things 45%
Cognitive Computing
43%
Data visualization 38%
Internet of Things
34%
Data lakes for data mining 34%
Blockchain
25%
Pattern Recognition 25%
Unstructured data mining
21%
Cognitive Computing 21%
Pattern Recognition
21%
Software robots 21%
Software robots
21%
Unstructured data mining 21%
Data visualization
14%
Blockchain 14%
Hadoop and Open-source
8%
Drones 8%
Sentiment analysis
7%
Hadoop and open-source anaytics 7%
Hadoop and open-source anaytics
7%
Sentiment analysis 7%
Data lakes for data mining
TODAY
MOST
DISRUPTIVE
IN FIVE YEARS

9
Digital supply chain transformation strategies come in many
forms. The concepts are top of mind for business leaders,
but the industry lacks a standard definition, and the process
is over-hyped by most consultants. In this study, 35% of
respondents had a digital strategy, and 43% reported evolving
their systems to be more digital. If there was a digital strategy,
as shown in Table 1, the company had an 80% probability of
rating their supply chain as “doing better” during the pandemic.
The top investments of the digital strategy—better supply chain
planning, improved visibility, and better descriptive analytics—
helped companies to adapt quickly and make better decisions
during 2021.
The concept of a digital supply chain transformation is not new.
In this study, 49% have had a digital strategy for more than three
years—45% of the time; the digital transformation program is
owned within the organization by IT. When owned by IT, the
organization rates their satisfaction with new forms of analytics
significantly lower at an 80% confidence level.
Most of the investments of digital transformation initiatives
are in supply chain planning, visibility, and descriptive analytics,
but the most significant value through the pandemic was
descriptive analytics. As will be seen in the data collected
for this report, companies are struggling to implement the
traditional planning and visibility solutions and drive value in the
face of heightened demand and supply variability. The focus
is on digitization for 50% of companies surveyed—speeding
The Role of Analytics in
Digital Transformation
TOTAL
During the
pandemic, we
managed very
well. No issues.
During the
pandemic, we
managed well
with some
issues.
We managed
the business by
brute force. We
did it, but it was
tough.
The business
struggled
during the
pandemic but is
continuing
The business
was not equal
to the pandemic
challenge and is
contracting.
Base 97 5 49 23 17 3
Yes - There is a digital
transformation strategy.
35% 80% 31% 48% 18% 33%
No - There is not a digital
transformation strategy.
22% - 22% 17% 29% 33%
Evolving - The digital
transformation strategy is
evolving.
43% 20% 47% 35% 53% 33%
Base: Total (n=97)
Q10. During the pandemic, what was the impact of the pandemic market changes on aggregate demand
(volume)?
Table 1. Impact of Digital Transformation and Analytics on Pandemic Performance

up and reducing manual inputs for existing processes. If the starting point for investment is acceptance of today’s process definitions,
companies will never push to explore and unleash value from new forms of analytics.
Definition: Digital Transformation. We define digital transformation as redefining the atoms and electrons through the deployment of new
forms of technology to improve value. This is opposition to Digitization that focuses on the deployment of analytics to improve the speed
and reduce the labor dependencies of existing processes.
Figure 5. Digital Transformation Investments
Improvements in supply chain planning 75%
74%
71%
50%
46%
41%
34%
33%
30%
28%
25%
16%
14%
7%
7%
5%
Driving better supply chain visibility
Imrpoved analytics for decision making
Speeding up processes
Sensing market conditions to imrpove the demand signal
Improving order-to-cash processes
Improving procure-to-pay processes
Automation of transportation decisions
Automation of factories
Warehouse automation
The elimination of paper
Accelerating time to market through changes in manufacturing
Sensing product quality in transport
Alternative energy
3-D printing
Other
Base: Total (n=76)
Q13. (BUSINESS USER) If yes, what was the focus of the digital transformation strategy? / (VENDORS/
OTHERS) If yes, state your focus for your typical company?

11
The highest level of supply chain automation is in the area of
transactional efficiency. Over the last two decades, over 90% of
companies focused on improving order-to-cash and procure-
to-pay transactions. In this area of supply chain improvement,
the processes are well defined, and the methods are very black
and white. During the pandemic, little changed that affected
transactional automation.
In contrast, the processes are very gray in decision support
applications—network design, supply chain planning, revenue
optimization, transportation planning, etc. Most companies
struggle with process latency—the time to make a decision—and
data latency—clean and timely data to make a decision.
As demand and supply variability increased during the
pandemic, the world of decision support analytics became
much grayer with increased uncertainty. Despite over 90% of
organizations owning supply chain planning in this study, the
planners used Excel to drive decisions. The problem? There
are many. Excel spreadsheet modeling lacks the capabilities
to analyze constraints, conduct what-if analysis, or show
the impact of variability. Supply chain planners like Excel for
simplistic modeling to easily manage and understand results
from their desktop tools. In general, the deployment of decision
support as a bolt-in technology into a fixed IT architecture batch
planning cycle does not fit the work requirements. As a result,
in our interviews, we found that as the supply chain increased
in variability-- with more and more shades of gray—companies
turned off their planning systems and turned to descriptive
analytics and desktop modeling.
Quotes from Discussions with Clients on Planning
Deployments:
“We didn’t use our planning systems during the pandemic.
The solutions were slow and too much of a black box.”
“When shipments no longer reflected market demand,
traditional demand planning solutions were inadequate.”
“Ownership and knowledge got lost during the pandemic.
The solution couldn’t be maintained, and the error reduction
level deteriorated due to network changes and order
pattern changes requiring base-level remodeling. We just
did not have the time to redo the models.”
“Coming into the pandemic, we struggled with the
maintenance of the planning system. The issues grew
worse in the pandemic. The Supply Chain Executives were
not interested in the project: and as a result, did not see the
value. We turned the system off. “
Working in the World of
Gray

Over the last decade, the focus of IT investments has been to
improve enterprise efficiency. The investment in value chain
analytics is underserved despite the growing number and
relative importance of network relationships. In this study,
outsourced manufacturing averaged 27%, while 34% of the
distribution of products is
outsourced to a 3PL on a volume
basis. Within the network, data
moves slowly, often lacking
synchronization. Data latency,
cleanliness, and availability are
ongoing challenges. Despite the
growth and availability of new
streaming data sources, this is
the case.
The term visibility is used often
and rarely clearly defined. Within
the supply chain, the meaning
varies by role and use case. With
the increase in variability in 2021,
over 74% of companies larger
than 5B in annual revenues
attempted to improve supply
chain visibility through analytics.
Yet, little progress was made. As
shown in Figure 6, organizations
rate the lowest capability in
supplier visibility and the highest in logistics. While 64% of
companies believe it is essential to transmit product information
digitally to suppliers to improve manufacturing processes, 36%
believe they do it well.
In summary, there are significant gaps in each of the four areas
of visibility studied with no easy answers. Blockchain is not a
panacea. Current value chain supply chain operating networks
augmented with streaming data helps. What is clear is that the
answer to supply chain visibility does not lie in the deployment
of more enterprise-based data models. Instead, supply chain
visibility requires the automation of the network outside-in.
In short, the industry has
many visibility projects, but
companies are making little to
no progress. Despite significant
investments, the gaps are
significant. We are making slow
progress on transportation
visibility but struggling with
supplier visibility.
The problem with supplier
visibility is bookended into
procurement processes
that have gone back, not
forward, over the last decade.
Procurement processes-
-encumbered by a focus
on paperless processing,
RFPs/RFQs, and efficient
procurement--do not
embrace the capabilities and
requirements of direct material
sourcing. The secondary
problem is the lack of definition of process requirements and a
buying team that cannot see past simple MRP/MRP II/DDMRP
requirements. There are no value network solutions to enable
plan/source/make and deliver visibility holistically. All are self-
serving, operating on an island lacking interoperability with other
network solutions.
The Role of Analytics in
Improving Supply Chain
Visibility
Figure 6. Current State of Supply Chain Visibility
Base: Total (n=76)
How important would you rate visibility capabilities for your company? (scale 1-5)
How effective would you rate your visibility capabilities for your company? (scale 1-5)

13
So, if one out of two manufacturing companies have an
analytics center of excellence, why can we not make more
progress in using new forms of analytics, the intelligent reader
might ask? The answer to
this question is the genesis
of this research.
As shown in Figure
7, the answer is
threefold—organizational
alignment, employee
skill and understanding,
and leadership. While
consultants strongly
believe that the failure to
use new forms of analytics
is a leadership gap, and
business leaders feel
that it is an employee
skill deficiency, the
more significant issue is
alignment.
Our take? We talk
analytics but struggle
to embrace Art of the
Possible capabilities. The
struggle is twofold: helping
people imagine the future
using new analytics and
technology to improve the
meaning of work. Start by
aligning business and IT:
Role of IT
Standardization.
The focus of IT on
technology standardization misaligns with the business
struggling to use technologies deployed. Over the
past two decades, there have been ten failures of co-
development with existing technology providers for each
success.
Steps to Take: Create a cross-functional group to
evaluate analytics projects. Think past the –one
throat to choke paradigm (to minimize costs) —that
proliferates in most
organizations. Instead,
challenge the analytics
center of excellence to
experiment with new
forms of analytics to solve
the unsolved business
problems. Fund this group
with a separate budget for
process innovation.
Lack of Executive
Support. Leadership
support is an ongoing
challenge. Many
executives--blinded by
the goals of transactional
efficiency--lack the holistic
understanding of how
to manage a market-
driven value chain. The
traditional endorsement
of a marketing-driven
response versus a market-
driven response paralyzes
a company in times of
growth and high variability.
In parallel, the myopic
focus on cost versus
margin reduces response
reliability.
Steps to Take: Build digital
twin simulation capability
to help business leaders understand the impact of
demand shifting versus effective shaping, product
portfolio management, and platform rationalization.
Talent. Across the organization, employees struggle
to understand the potential of new technologies
Building Talent
Alignment between
business & IT
43%
41%
35%
34%
24%
11%
9%
1%
Employee skill levels
Leadership support
Funding
Management of the
rate of change
Employee
knowledge
Other
(please specify)
Don’t know
Base: Total (n=97)
Q35: What are the top TWO challenges that your company/the typical company is facing today
when it comes to analytics strategies?
Top 2 Challenges Companies Face with Analytics Strategies
Figure 7. Barriers in Adopting New Forms of Analytics

abounds. There is no clear-cut route to test and learn
to drive process innovation. Invest in
training, lunch and learn sessions, and
structured learning from innovative
technology providers. Focus on moving
employees cross-functionally to ensure
a holistic understanding of business
problems.
Steps to Take: Share the success and
failure of testing new forms of analytics
with the larger organization in regular
sharing sessions. (We highlight failure because we find
organizations reluctant to share insights on projects that
did not work. Do not mistake this effort as an employee
hoopla event.)
The knowledge gap across the industry cannot be
underestimated. As shown in Figure 8, familiarity with the base
terms of new forms of analytics is low for both business leaders
and technologists.
Push the organization to be more data-driven
and challenge the status quo. Business
users need to use caution that technologists
approach the topic of data collection with
rose-colored glasses but struggle to drive
test and learn process innovation due to the
breakdown in current approaches to analyze
new forms of analytics.
______________________________
Definition: Test and Learn is the evaluation of new forms of
analytics to solve problems that lack a good solution to drive
value. This is starkly different from the typical project-based
approach focusing on implementation.
-9% -11% -13% -14% -19% -21% -22% -25% -28% -35%
47%
Blockchain Ontology Hadoop Apache Spark Fuzzy Logic Python R Deep Learning
Probabilistic
Forecasting
Sentiment
Analysis
13%
24% 23%
36%
13%
27%
17%
36%
43%
64%
25%
47%
53%
78%
34%
62%
23%
58%
56%
Figure 8. Familiarity with Meaning of Analytics Strategy Terminology: Users vs. Vendor
Users
Vendors
Gap (Users - Vendors)
Greatest Gaps Between
Users and Vendors
Base: Total (n=97)
Q38. How familiar are you personally with the meaning of the following terms as they apply to analytics
strategies?

15
Total Users Vendors
Base 97 53 45
Getting access to the data
needed to make decisions
41% 38% 44%
Driving data-driven
decisions
38% 40% 36%
Testing new analytical
concepts
29% 36% 20%
Maximizing the value of
analytics technologies
23% 30% 13%
Table 2. Organizational Effectiveness: Contrast of Business User and Technologist Perspective
Base: Total (n=97)
Q40. Overall, how effective is your company (BUSINESS OWNER) the typical company (VENDORS/OTHERS)
at doing each of the following?

Success happens when the right question aligns with the correct analytical technique. Asking the right question and assessing how
companies make decisions is the starting point.
New levels of value come from clarity on how to use analytics to make a better decision. In your journey, start by asking yourself, “What
defines a good decision for my organization?”
Analytics Primer
Thinking Out of the Box Starts by
Defining the Box.
A barrier to “out-of-
the-box” thinking is
definitional clarity
to align analytical
capabilities to solve
real-world problems.
Let’s start by aligning
on the definitions used
in this research:
• Artificial Intelligence (AI), founded as an academic
discipline in 1955, is evolving rapidly. In 2015, there was
a step-change in using machine learning by innovators in
supply chain planning.
• Machine Learning and Semantic Reasoning are subsets
within AI, while Machine Learning uses data to train and
find accurate results, semantic reasoning infers logical
consequences from a set of facts. An ontological language
usually drives the inference logic. (An ontology is a set of
truths to guide the reasoning.) Pattern recognition, Natural
Language Processing (NLP), and deep learning are subsets
of Machine Learning.
• Pattern Recognition detects patterns and regularities
in data, while natural language processing translates
unstructured data into a structured form to enable learning.
• Deep Learning uses many algorithms to drive insights
through neural networks. Deep learning processes
encompass unstructured data, whereas pattern recognition
is limited to structured data. Today, when a company in
the decision support technology market speaks of AI, it
is usually pattern recognition. (Most companies are just
dipping their toes in deep water.)
A barrier is the structure of today’s planning systems. How
so? The use of relational database structures hardcode data
into formal and inflexible tables. Schemas store tables, and
inside each table, there are pre-defined columns and rows. In
contrast, as shown in Figure 9, a graph-based database is a
mathematical representation of objects, entities, or nodes and
their relationships.

17
SALES
Customer Item Time
0001 1A 20:34
0001 1A 21:15
0002 2A 21:16
0002 1A 21:16
0002 5C
INVENTORY
Description SKU
Pepsi 1A
Club Soda 2A
. .
. .
Diet Coke 5C
CUSTOMER
Name CustID
John 0001
Jack 0002
Ted 0003
Ken 0004
Valerie 0005
Traditional databases store data to efficiently store facts, but relationships
must be rebuilt with JOINs and other inexact techniques
Person:
John
Person:
Jack
Item:
Pepsi
Item:
Club Soda
Person:
Valerie
B
u
y
s
(
x
2
)
B
u
y
s
Buys
Buys
Graph databases store both facts and the relationships between the facts,
making certain types of analysis more intuitive.
Figure 9. Contrast Between Relational Tables and Graph Databases
Attribution: Cambridge Semantics
So what does this mean? When technology providers use AI, the application is for simple pattern recognition. We are early in the use of
graph technologies and even earlier in applying cognitive or semantic reasoning. We show this relationship in Figure 10.
Semantic Reasoning
Machine Learning: Deep Learning
Machine Learning: Natural Language Processing
Simulation: Monte-Carlo Analysis
Predictive Analytics: Statistical Inference
Machine Learning: Instance-Based
Learning / Pattern Recognition
Machine Learning: Reinforcement Learning
Machine Learning: Ensemble Learning
Machine Learning: Decision-Tree Learning
Complexity of Technique to Drive Insights
Likelihood
to
be
used
in
AI
Applications
Figure 10. Application of Analytic Techniques

For business leaders trying to figure out how to drive value, we provide insights in Table 3.
Table 3. Application of Analytic Techniques
Analytics Technique Potential Value Proposition(s)
Pattern Recognition Mapping dirty data like master data. Visualization of
patterns in data.
Natural Language Processing Mining unstructured data. Visibility of customer
sentiment from email and comments. Listening posts for
sentiment, warranty or quality data.
Deep Learning Demand insights generation from ever-changing markets.
Semantic Reasoning Rule automation: connection of customer-centric
segmentation to Available-to-Promise and Allocation
Strategies.
Sometimes there is confusion between an ontology and a knowledge graph. An ontology is metadata/schema, whereas the knowledge
graph is the data itself. Think about generating a domain ontology and populating it with dynamic facts using a knowledge graph to create
side-by-side collaborative work: machine learning feeding semantic reasoning for ongoing education.
Use Of Analytics to Transform
In making decisions in the journey to unleash the Art of the Possible, ask your team to connect graph-based databases across sales,
marketing, finance, and supply chain groups to enable demand visibility across multiple models to drive new role-based insights for all
participants. For the innovator (roughly 4% of the population), explore semantic reasoning to tie baseline market sensing to program
definition of trade and price policies and connect customer segmentation to rule enablement for order management through Available to
Promise (ATP) and allocation strategies.

19
Each phase of the pandemic is new and unprecedented.
Leaders must drive forward in the face of increased variability.
The correct answer is seldom black and white. Instead, it
is a sensitivity analysis, simulation, and game theory world.
The supply chain investments of the last decade focused on
improving black & white processes—cleaning-up transactions.
Transactional automation (ERP) investments improved order-to-
cash and procure-to-pay. However, ERP automation did not help
as the world became grayer with increased demand and supply
variability.
Forward progress also
means learning a new
language. As the world
becomes grayer and
grayer--with increases
in variability of all types-
-analytics offer promise. However, the benefits cannot be
achieved without education, exploration, and leadership.
Use Cases
We cannot change things overnight, but there are some steps
that we can take through the use of advanced analytics.
Use Case #1. Manage Make, Source, and Deliver Together.
At the enterprise level, manufacturers and retailers focus on
corporate efficiency. Leaders are blind that the most efficient
supply chain (lowest cost) is ineffective.
Market sensing takes months (shifts in the market), and
process latency requires weeks (organizational agreement on
what to do). As a result, companies make the wrong decisions.
Factories were shut down in the face of rising demand during
the early days of COVID-19. Fourteen of twenty-eight industries
have rising inventories. Supply is more significant than demand.
In contrast, the balance of the industries lacks supply. All are out
of balance.
In Table 4, we share what this looks like for a manufacturing
company of white goods.
Traditional systems
distort market signals.
The assumption is that
the order is a good
representation of demand.
It is not. The translation of
signals with conventional
approaches--ERP, CRM, SRM, and APS--further distort the signal
and add noise. As a result, organizations are more reactive in
this time of increased variability.
What to do? Invest in analytics to sense and translate demand.
Place investment in legacy systems -- ERP, CRM, and SRM--on
hold. Change internal metrics to a balanced scorecard and force
the functions to align on common goals.
Use Case #2. Focus Outside-In. Today’s supply chains
respond, but they do not sense. There is no place to put market
data--weather, telematics, point of sale/consumption, and
unstructured data in today’s infrastructure. We are very early
Using Analytics In the
World of Gray
Table 4. Current State of a Global Manufacturer of White Goods
Demand Distortion
to Manufacturing
Latency from Market
Signals
Demand Latency
of Point of Sale to
Orders
Process Latency
(Time to Make a
Decision)
49% Seven months Two weeks Three-four weeks

in the definition of outside-in processes. The most significant
barrier is unlearning--decoupling ourselves from believing that
historical processes are best practices. While new analytics
possibilities exist, business leaders’ understanding of using
them is limited. Invest time in understanding what is possible.
What to do? After alignment, use NoSQL techniques and data
scientists to build outside-in processes.
3. Design for Resilience. There is a level of unprecedented
inefficiency in today’s supply chain. Labor turnover, supply
reliability, and transportation variation are here to stay. As a
result, companies need to redesign supply chains based on
the inefficiencies of today. The design, and redesign, of supply
chains, need to be continuous based on market data.
What to do? Invest in network design technologies. Build an
internal team to continually design and redesign flows holistically
using the factors of today’s inefficiencies. Redesign flows and
sets buffers monthly.
4. A Sole Focus on Volume Is a Mistake. In 2021, gasoline
prices increased 42.1% year-over-year and climbed 1.3% over
the prior month in December. Used Cars and Trucks prices rose
24.4% year-over-year while the food increased 4.6% over year-
ago prices. Traditional planning systems recommend decisions
based on the volume and eliminate manufacturing bottlenecks.
This is not sufficient.
What to do? Build planning models to manage the trade-offs of
price and volume together. This is a good use case for a graph-
based deployment.

21
Recommendations
The great promise lies ahead for business leaders with the courage to be innovators. To move forward, embrace new analytics forms, and
test/learn through active pilots and the redesign of today’s processes. Build a coalition to drive change and have the courage to go a more
data-driven approach to solving business problems.
Conclusion
1. Question the Status Quo. Traditional supply chain
processes evolved despite the limitations of analytics in
the 1990s. Despite the preponderance of new analytics
capabilities, too few companies question the basis of the
analytics enabling historical processes.
2. Build Talent and Test and Learn with New Technologies.
The most significant barriers to driving the levels of value
are organizational. Train employees, push-to-go classes of
value, and implement programs to test and learn.
3. Close the Gaps on Visibility. Stop deploying “visibility
solutions” for “visibility’s sake.” Instead, forge a cross-
functional team to build a comprehensive program
including:
• The Adoption of Authoritative Identifiers. Cars
have VINs, and a candy bar has a UPC. Your wallet
has social security or a passport number. These
are authoritative identifiers. Today, there are no
authoritative identifiers to facilitate the tracking and
tracing for containers, warehouse locations, trucks,
or manufacturing plants. Actively work to close this
gap by adopting the GS-1 and ISO-8000 standards.
Move aggressively on building authoritative
identifiers in visibility programs.
• Maximization of the Use of Existing Trading
Partner Solutions. Survey existing suppliers, third-
party logistics providers, and customers. Build
maps of required interoperability requirements. To
move forward, educate the team on the differences
between integration and interoperability. Using a
rules-based ontology, use NoSQL to build a unified
data model across disparate data systems.
• Embracing Disparate Data. Map performance
gaps and identify all forms of potential data to drive
improvements. Unleash the Art of the Possible by
using unstructured, streaming, and image data.
Combine these new forms of data with transactional
capabilities to drive new outcomes.
• Building a Digital Twin Using Planning Master Data.
Drive plans based on actual lead times, conversion
rates, and cycle times. Actively design the network
and measure performance to develop capabilities.
Constantly tweak and change the design based on
network shifts.
• Designing an Over-Arching Strategy and Building
Blocks. Don’t mistake that you know the definition
and requirements of visibility. Build a multi-year
visibility strategy. Work with a cross-functional team
and use the activity to educate your organization.

In this section, we share the demographic information of survey
respondents and additional charts referenced in the report to
substantiate the findings.
The participants in this research answered the surveys of their
own free will. There was no exchange of currency to drive an
improved response rate. The primary incentive to stimulate the
response was an offer to share and discuss the survey results
through Open Content research at the end of the study.
The names of individual respondents and companies
participating are held in confidence.
In this study, the average respondent posted revenues greater
than 10$ billion in 2020, with an average of fifty manufacturing
locations with 27% of volume outsourced to a third-party
manufacturer. The respondents crossed industries with 58%
in process industries and 36% in discrete (make-to-order or
configure-to-order) businesses. By role, 42% of the respondents
were a director or senior director, 23% VP, SVP, or COO. The
intent was to gain insights on the global response with
respondents from 48% North America, 28% from Europe, and
11% from Asia.
No two companies define supply chains alike. In this report,
the typical definition of reporting relationships in the supply
chain is shown in Figure A. There was no correlation between
organizational design and other questions in the study.
56% of manufacturers did well or very well during the pandemic,
while 21% struggled. Companies performing the best had a
data-driven culture and a higher and more effective level of
descriptive analytics.
The respondents had a skewed distribution. Only 4% evaluated
themselves as the first to try.
Appendix
Figure A. Organizational Definition of
Supply Chain Management
Base: Total (n=97), User (53), Vendor (45), Other (2)
Q7. When you think of the term “supply chain” in your organizations/your typical client which
functions report to supply chain?
Transportation
Management
86%
74%
65%
60%
55%
33%
33%
13%
Order
Management
Procurement
Manufacturing
Contract
Manufacturing
Supply Chain
Finance
Risk
Management
Corporate Social
Responsibility
TOTAL

23
Figure B. Pandemic Self-Assessment During 2021
Group 1 & 2 56%
5%
51%
24%
21%
18%
3%
During the pandemic, we managed
very well. No issues.
During the pandemic, we managed
well with some issues.
We managed the business by brute force.
We did it, but it was tough.
Group 4 & 5
The business struggled during the
pandemic but is continuing.
The business was not equal to the pandemic
challenge and is contracting.
TOTAL
Base: Total (n=97)
Q8. When you think of the supply chain’s response during the pandemic, how would you classify your
company’s business performance?
50%
First to Try
4%
17%
27%
2%
Among the First In the Middle Among the Last Last to Try
Figure C. Self-Assessment on Innovation
Company’s Approach to Investing in New Analytics Strategies - Users Only
Base: Total (n=92)
Q26. Which of the following best describes your company’s approach to investing in new analytics
strategies, in general?

25
About Supply Chain Insights LLC
About Lora Cecere
Other Reports in This Series
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. We want you to turn to us if you need to know which practices and
technologies make the most significant difference to corporate performance. We are a company dedicated to this research. Our goal is to
help leaders understand supply chain trends, evolving technologies, and which metrics matter.
Lora Cecere (Twitter ID @lcecere) is the Founder of Supply Chain Insights LLC and the famous enterprise
software blog Supply Chain Shaman, currently read by 15,000 supply chain professionals. She also writes
as a Linkedin Influencer and is a contributor for Forbes. She has written five books. Bricks Matter’s first book
(co-authored with Charlie Chase) was 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 a first-mover advantage
Readers may gain added value by accessing complementary and relevant reports on the Supply Chain Insights website:
Big Data and Analytics
Navigating the Supply Chain Through the Pandemic
Managing Supply Chain Talent Through the Pandemic

7 Dart Manor Court
Hanover, PA 17331
617-816-9137
www.supplychaininsights.com

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