DC Velocity - IdeaBook 2016

machine. It’s about the same size as traditional vending machines, but it can dispense 126 different flavors, offering consumers a huge number of combinations. The machine uses cartridges that store concentrated syrups and are equipped with radio frequency identification (RFID) chips. The RFID chips detect how much of each syrup the machine has and what combinations are being used; when it detects that it needs supplies, it transmits information to both Coca-Cola and the store owner, including what has been sold, a record of when sales occurred, troubleshooting information, and service data. As a result, both product sales and customer satisfaction increase.

Predictive maintenance. Predictive maintenance pro-
grams for machines and other equipment can utilize sen-
sors and connected devices to monitor and react to prob-
lems in applications as diverse as large-scale manufacturing
and diagnostics on the family minivan. This self-diagnosis
capability can detect a potential problem before failure,
order a replacement part, and even schedule maintenance
to avoid costly downtime. Further, the use of the IoT to
stay ahead of maintenance issues has implications on an
industrywide scale. With continuous retrieval of data from
factory equipment, for instance, manufacturers can better
see problematic trends that could affect future production,
and parts depots can better forecast inventories and ensure
consistent safety-stock levels.

In relation to home automation, predictive maintenance will become integrated into our everyday lives. Appliances will become smarter, more efficient, and easier to monitor. Internet-connected sensors will be embedded into everything from appliances like washing machines and dryers to heating, ventilation, and air conditioning (HVAC) systems. These connected appliances will perform self-diagnosis, determine the most cost-efficient time to operate, and even

The basic functionalities of a warehouse management system (WMS) include monitoring what is moving through a warehouse and keeping track of inventory and transactions. A labor management system (LMS) monitors worker productivity. Both the WMS and LMS rely on data from various sources and produce data that warehouse operators and managers use to make decisions.

WMS and LMS software has become very efficient at documenting what happened; for example, that lift truck A traveled X distance and performed Y activities in a given time. This information allows for a basic comparison between lift trucks—lift truck A completed 25 percent more work within a specified time than lift truck B did, for instance. However, the data gathered is often one-dimensional and does not provide information about where the truck traveled, how efficiently the work was performed, or what events preceded or followed the work. Moreover, because such data is often collected manually, the information cannot be viewed in real time, and processing data from different operations may require separate systems with incompatible reports. In short, there are information gaps, and decisions are therefore based on historical rather than real-time data.

The emerging Internet of Things can help WMS and LMS software move beyond basic transactional information and transform warehouse operations. The massive amounts of data that connected devices provide to these programs in real time can be used to better manage personnel, control and manage inventory, make purchasing decisions, and manage shipping.

Precisely tracking inventory from receiving to the shipping dock
while being able to locate exactly where a particular item is in
a warehouse at any moment, and making sure that it is moved
at exactly the right time in the most efficient manner possible,
creates transparency and visibility—removing information
“black holes” that have long hampered decision making.

For example, vehicle-mounted sensors providing data to an existing WMS can allow for tracking lift trucks and other vehicles and inventory to the inch, in three dimensions, anywhere in the building. Matching this data to the planned work permits accurate monitoring of productivity in real time. Decisions around staffing, slotting, and purchasing can be reviewed continuously and can be adapted as a business environment changes.

Additionally, the Io T opens up new data streams and allows for new reporting within the WMS. The new data streams provide the ability to cross-reference a particular vehicle’s position, load status, or condition (for instance, if it has been in an accident or is in need of maintenance) with the key metrics in the WMS to find efficiencies in staffing levels, maintenance plans, traffic patterns, building layout, and inventory management.

Moreover, real-time accuracy down to the inch allows for sophisticated, heuristic route planning as vehicles become more “aware” of each other. For example, this data could: allow software to assign tasks to drivers in a way that makes sure they take the most efficient route; monitor trucks’ location to reduce congestion in the warehouse; use lulls in the workday to actively re-slot the warehouse to make future jobs more efficient; and prevent violation of safety rules, such as speed limits.