IEEE Systems, Man, and Cybernetics Magazine - January 2018 - 9
From the organizational aspect, we consider that the
manufacturing of the IoT holistically consists of five levels (as shown in Figure 1): sensor-actor-machine, shop
floor, factory, enterprise, and supply chain. The IoT can
greatly enhance efficient information flow (or even accelerate logistics) downward and upward between any two
levels (e.g., cross-layer interaction), leading to a trend of
increasingly flatter organizational structures. For example, the geographically distributed IoT-enabled factories
can now be efficiently managed and scheduled directly
by a single management system, while the need to deploy
a great amount of hierarchical and fully functional local
management systems is lowered as the IoT pushes more
work to be automated and fewer people are involved.
Furthermore, such an efficient organization of resources with the help of the IoT can better support the full life
cycle of products (research and development and design,
production, marketing and sales, after-sales service). With
a powerful IoT infrastructure, any two stages can interact
to gain useful feedback rather than having traditional
interaction just between two consecutive stages. We are
currently experiencing the transition from the seller's
Flatter Horizontal
Structure and Full
Interconnection
market to the buyer's market. Thus, such cross-stage
feedback can potentially benefit all value-chain parties.
For example, the designers will receive useful user reviews
by directly posting their conceptual design to the social
network; customers may recommend a customized product that suits their personality.
Another new IoT-enabled paradigm is intelligent products [28] (shown as "in-use product" in Figure 1) that can
enforce the online monitoring of product conditions and
perform remote diagnosis. This is not covered by the traditional supply chain, which involves the transformation of
materials to finished products, even though some used
products may reenter the chain again. Based on the
acquired online usage data, manufacturers can conduct
proactive maintenance or use such information to improve
their designs or manufacturing processes. In such a scenario, when the prediction based on real-time data indicates that certain parts should be replaced after some
time, orders can automatically be placed so that just-intime production and the replacement of parts can be
enabled to facilitate lean production and reduce costly
downtime (improving user experience greatly).
* Social Network
* Cognitive Thinking
R&D and
Design
Cross-Stage
Interaction Across
the Product Life Cycle
Production
Supply Chain
In-Use Product
Enterprise
* ServiceOriented
Architecture
* Virtualization
Technology
Cloud-Based
Big Data
Factory
* Acquisition
* Storage
* Processing
Shop Floor
Sensor, Actor, Machine
Marketing
and Sales
After-Sales
Service
* Advanced
Connectivity
* Real-Time ClosedLoop Control
Figure 1. The impact of the IoT on the manufacturing industry. R&D: research and development.
Ja nua r y 2 01 8
IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE
9
Table of Contents for the Digital Edition of IEEE Systems, Man, and Cybernetics Magazine - January 2018
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