per has data on everything a truck was doing before it
was involved in an incident that resulted in damaged,
overheated, overcooled, or missing goods, it can better
determine what led to the incident and use that information to reduce future losses. 2 The analysis and use
of a combination of data sources—on the vehicle, in
the container, on the package, and even biometrics
from the vehicle’s operator—can be fed into a predictive model that can then be used to drive change in
the supply chain. AI could automatically change routes
or shipping methods, for example, or DA could drive
changes in packaging or in vendor training. 3 These
type of actions can lead to both cost savings and the
development of premium-priced services.
Data analytics is used to make
predictions with reduced errors
about future events, such as when
a product will be purchased, a
machine will break down, or a
valued employee will quit, to
name a few examples. DA often
uses accessible, easy-to-under-stand reporting mechanisms, such
as “heat mapping” (a graphical
representation of data where the
individual values contained in a
matrix are represented as colors) and multidimensional
plotting, to communicate findings.
One relevant goal of data analytics is improved supply chain visibility, which results in reduced risk and
shorter lead times as well as the ability to quickly identify shortages and detect quality problems at the source.
This increased visibility has both positive and negative
implications. On the positive side, companies use real-time visibility to monitor supply chain operations,
especially for high-value assets such as pharmaceuticals
that have crucial delivery timing and are subject to gov-ernment-imposed regulatory and compliance requirements. With real-time visibility, organizations have
near 100-percent knowledge accuracy for assets stored
in and across containers, pallets, and shipping crates
in their supply chains. Real-time visibility reduces personnel costs, improves response times, and decreases
asset spoilage. On the negative side, when processes go
wrong, errors are made, or technology fails, everything
is exposed to suppliers and customers—also in real
time—and there is little chance to manage perceptions.
A second, related topic is the use of algorithms. An
algorithm is a clearly structured set of rules and proce-
dures that are applied to data in order to solve prob-
lems. An extension of a rich set of algorithms drives
artificial intelligence, defined as when computer sys-
tems have the ability to mimic human cognitive activ-
ity, through sensing (visual, auditory, determining hot/
cold/wet, and so forth), recognition/categorization, and
decision-making activity. This is followed by auto-im-
provement based on feedback, enabling the artificial
intelligence system to learn over time. Amazon is one
of many companies making significant investments in
the development of AI, with both consumer-oriented
and supply chain implications. 4
Data analytics and artificial intelligence are just
two ways that data-driven disruptive technologies are
relevant and imminent. Both take streams of data as
input and apply computing power, generating highly
valuable, actionable information.
The third step in the disruptive technologies narrative for supply chain managers is to understand how
data are transformed into actual applications in the
physical world. In other words,
once we process data, what does
it mean inside the warehouse, in
retail stores, or on the roads and
in the air around us?
3. How we transform data
into real-life applications. The
transformation of data into disruptive technologies represents the
highly visible outcomes of new
data sources and new data-analysis
tools. We break this down into
two broad categories: changing how we move goods,
and changing how we manufacture.
Changing how we move goods. The rise of unmanned
autonomous vehicles (UAVs) has long been predicted
as imminent. Now we are seeing UAVs in actual use
inside closed spaces like manufacturing plants and distribution centers. Advanced UAVs also are starting to
move onto public roads and into the skies.
Self-driving vehicles and aerial drones use advanced
sensors, satellite data, and peer-to-peer communications to move from point to point with limited or no
human intervention. These capabilities could not only
reduce costs but could also make it possible to provide
premium services, such as delivering products or services in unserved areas, that competitors cannot offer.
On the inbound logistics side, autonomous delivery of materials and components by trucks and rail
could reduce errors, increase delivery speed (no driver
breaks required), and drive down costs. In the warehouse, robots are already reducing errors and cost.
And on the outbound side, autonomous delivery is
expected to change when and how customers can
What does this mean for supply chain managers?
Drone-based delivery may eventually set the new
standard for fast delivery. Self-driving forklifts (
autonomous vehicles combined with advanced robotics) could
easily be the next evolution of autonomous vehicles in
distribution centers. Taking human labor out of supply
[DISRUPTIVE TECHNOLOGIES: SHOULD YOU GIVE THEM THE GREEN LIGHT?]
Viewing and implementing
strategically will allow you
to be the disruptor, rather
than the disrupted.