The supply chain in the digital economy has been moving slow. Traditional tools still deployed in many organizations, are simply not built for supply chain planning and execution in the digital age. Often business processes are built around sequential planning steps and batch based processes that negate the benefits of the tools themselves.
Most organizations have worked hard to get where they are today using the technologies that have been available over the last 10 to 20 years. The challenge is how to take advantage of digital driven supply chain, remove latency, improve collaboration with supply chain networks, and better integrate planning and execution.
Collapsing Silos in Supply Chain
Product Lifecycle Management has traditionally followed a linear path from design to production. Design, manufacturing, asset management, and the supply chain operated mostly in siloed domains that consisted of largely disconnected processes, a framework that was entirely sufficient when feedback loops linking a finished product to design were measured by months or years.
Sensor data obtained through Internet of Things (IoT) connected devices, combined with new technologies such as 3D printing, make the previously linear path unjustifiable. In just the past few years the playing field has changed so dramatically as to be almost unrecognizable, requiring companies to turn to new tools to meet demands for immediacy and individualization in the production and delivery of goods and services.
In a digital enterprise, design, manufacturing, asset management and finished products all come together in an uninterrupted, real time loop. In this environment, manufacturers capture individual customer requirements and automate and deliver on those requirements, despite having a shrinking window to fulfill the demand for immediacy.
As the traditional build to stock model diminishes, the importance of leveraging IoT technology as a tool to help manufacturing meet this challenge builds. How?
- First, using sensor data on machines on the manufacturing floor in a -lot size of one- production model is needed to fulfill individual orders.
- Second, analyzing sensor data from a product in the field.
- Third, improvements based on real time performance data are then reflected for the next set of customers configuring a product.
Underlying this evolving design and production model is a need to ensure that the assets themselves are running optimally. That is why sensor data becomes critically important to ensure that a machine on a production line, or a machine in the field with output that is sold as a service, does not experience unintentional downtime.
Digital Transformation, Internet of Things (IoT) and Service
The product life cycle that has traditionally started with design and ended with production now extended to analyzing the real-time performance of a finished product in the field. With non-static products, sensor data is also important from service and maintenance perspective. And here we see how IoT fundamentally changes longstanding, usage based maintenance strategy.
In the Industrial IoT or Industry 4.0, sensor data is being used to predict asset failure, including manufacturing equipment, connected fleets, and other assets in the field. With real time insight into an asset’s performance, organizations can make accurate and insightful decisions that minimize risk and cost while maintaining peak performance in ways that were never before possible.
Further applications of IoT data can be seen in the use of digital twins, where sensor data and real-time operational insights integrate with business data. This digital twin is important not just for the after-market side of manufacturing, but also for the management of manufacturing processes themselves.
In today’s production environment, it is critically important for plant level engineers and operators to have full visibility and awareness of production assets and to likewise use sensor data to drive new processes.
Today, by levering innovative, easy to adopt supply chain planning and execution technologies, supply chain professionals are making decisions based on live data in real time. If the data shows that demand is changing, a fully digitized supply chain with built-in machine learning capabilities can respond with a high degree of automation and speed.
Using more data means better decisions, reducing cost to serve, and using live data means cycle times can drop from months and weeks to days and hours, allowing for more agility.
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