[BY MICHAEL E. WASSERMAN AND FARZAD MAHMOODI]
MICHAEL E. WASSERMAN ( MWASSERM@CLARKSON.EDU) IS ASSOCIATE PROFESSOR OF ORGANIZATIONAL STUDIES AND FARZAD MAHMOODI (MAHMOODI@
CLARKSON.EDU) IS JOEL GOLDSCHEIN ’ 57 CHAIR AND PROFESSOR OF SUPPLY CHAIN MANAGEMENT AT CLARKSON UNIVERSITY, POTSDAM, NEW YORK.
FINANCE GLOBAL LOGISTICS MANUFACTURING PROCUREMENT STRATEGY [TECHNOLOGY]
being crowded out by them. 1 Examples include Kmart being
elbowed out of the discount retail market by Wal-Mart’s
innovative distribution practices and technologies in the
1980s and 1990s, or Dell’s desktop computers being supplanted by new processor, battery, and display technologies
that accelerated the rise of laptops and tablets offered by
Samsung, Acer, and Apple in the 2010s. Today data-driven
technologies are emerging as a game-changing disruption
that is already transforming and will continue to transform
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
b harvesting and analyzing customer comments and
complaints from social media sources such as Twitter,
Facebook, LinkedIn, and Snapchat;
b 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;
b using cameras, Web logins, and in-store tracking to
capture customers’ behaviors or preferences;
b using survey data and loyalty cards to capture custom-
ers’ buying habits; and/or
b 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, 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 visibility 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 foundation 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 ship-