Special
Delivery
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-understand 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 government-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 procedures that
are applied to data in order to solve problems. An extension of a rich set of algorithms drives artificial intelligence,
defined as when computer systems have the ability to mimic
human cognitive activity, through sensing (visual, auditory,
determining hot/cold/wet, and so forth), recognition/cate-gorization, and decision-making activity. This is followed
by auto-improvement 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 receive goods.
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 chain activities can save
time and cost while improving accuracy. However, the risks
of technology failure are exacerbated in today’s lean environment. For example, a single system failure could result
in the shutdown of an entire distribution center. In addition, Web-enabled tools are at risk of data-security breaches
that can have similar negative effects.
Given these uncertainties, how should supply chain managers proceed in regard to implementing autonomous vehicles? We believe it is imperative that supply chain managers
strategically determine customer needs and partner capabilities, and align appropriate technologies with each appli-
