are emerging as a game-changing disruption that is already
transforming and will continue to transform supply chains.
The overarching contextual theme that supply chain
managers can use to turn disruptive technology into a strategic tool is data. Some of these data are coming from radically different sources than just a few years ago. How is the
next wave of data-saturated supply chains impacting the
already challenging jobs of supply chain managers? We will
focus on three elements that directly impact supply chains:
1. Where we get data. We will discuss both internal and
external sources of data.
2. How we analyze data. We will explore the impact of
“big data”/data analytics on how we process and transform data into useful knowledge, and we will discuss
artificial intelligence—how algorithms can be used to help
machines make decisions with minimal human input.
3. How we transform data into action in the physical
world. We will explore two specific and relevant examples,
three-dimensional (3-D) printing and autonomous vehicles, in both private and public spaces.
At first glance, these rapidly changing areas may appear
marginally related to the main activities of supply chain
managers. However, when they are viewed through the lens
of disruptive technologies, closer consideration suggests
disruptive forces that will directly impact supply chains are
already in motion. Thus, all supply chain managers should
consider these elements and, depending on the organizational and competitive context in which they operate, take
some degree of action in the near future.
1. Where we get data. There are many sources of business
data. For example, data generated by enterprise resource
planning (ERP) systems, including operational, financial,
and human resources information, flow into most orga-
nizations. Many companies use business intelligence (BI)
tools to parse and transform data into a format that assists
decision making. However, there are several other data-re-
lated trends that bear consideration. For example, some
companies are:
x harvesting and analyzing customer comments and
complaints from social media sources such as Twitter,
Facebook, LinkedIn, and Snapchat;
x tapping into Web-based data sources, including the
U.S. government (Bureau of the Census, Department of
Labor, and others), or international organizations, such
as the European Union or the United Nations, to identify
macroeconomic and demographic trends;
x using cameras, Web logins, and in-store tracking to
capture customers’ behaviors or preferences;
x using survey data and loyalty cards to capture custom-
ers’ buying habits; and/or
x using geographic mapping when considering new loca-
tions or advertising initiatives.
A swiftly growing source of data is the Internet of Things
(IoT). The IoT consists of numerous Internet-connected
sensors and switches that collect, send, and receive data
that can be used to monitor and control devices and
equipment, as well as predict events with minimal human
intervention. In the logistics arena, IoT is a placeholder
term that describes the process of taking all the disparate
systems and equipment in, for example, a distribution
center (conveyors; robots; automated storage and retrieval
systems; automated guided vehicles; forklifts; and lighting
and heating, ventilation, and air conditioning systems)
and tightly coupling them to warehouse-control and labor,
transportation, order, and customer management systems.
Such a connected warehouse would allow supply chain and
warehouse managers to reach new levels of operational
efficiency and predictability while providing real-time visi-
bility into operations.
These sources are disruptively changing what we know,
how we know it, and what we can do with our knowledge.
Yet data by itself is not disruptive. Instead, relevant, clean,
timely pieces of data, organized for analysis, are the foun-
dation upon which disruptive technologies are built. The
next step in turning disruptive technologies into a strategic
advantage is to understand changes in data analysis, namely
data analytics and artificial intelligence.
2. How we analyze data. We will explore two ways data
are analyzed. One—data analytics (DA)—is human-driven,
and the other—artificial intelligence (AI)—is human-de-signed but utilizes technology to learn and adapt. Both DA
and AI involve the application of mathematical techniques
to large data sets to identify patterns and relationships
that may previously have been unnoticed. For example, if
a shipper 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
