Manufacturing today entails immediate yet informed decision making. However, with increasing levels of sophistication and production, senior leadership often has limited time to make optimum decisions pertaining to the number of unanticipated issues surfacing from time to time. These issues—if not managed properly and timely—can lead to defects and wastes.
Top global enterprises are utilizing innovation and creative ways to enable prompt decision making. Specifically, they are using Internet of Things (IoT) to effectively handle critical aspects of manufacturing. Successful implementation of a Manufacturing IoT system facilitates in automating key tasks, decisions and processes; curtailing scrap and rework; and enhancing productivity.
People often object to implementing an IoT system by citing other important projects that they are already undertaking and the resource and time constraints as pressing hurdles. To work around these limitations, manufacturers can engage 3rd party consultants having proven expertise in end-to-end successful IoT, Asset Tracking, and manufacturing systems deployment.
Implementing a Manufacturing IoT System leverages immense benefits, including:
- Enhancing the ROI of other programs under way.
- Streamlined and Process Improvement and Robotic Process Automation help prompt informed decisions.
- Managing materials efficiently.
- Adjusting to customer requirements.
- Avoiding costly mistakes and rework.
However, harnessing IoT necessitates careful deliberation and planning. The core requirements to effectively implement a Manufacturing IoT system can be segregated into 2 broad categories:
- Functional Requirements
- System Requirements
Functional Requirements (FR) describe the system or its components. FR provide a description of services that the Manufacturing IoT system must offer. FR for Manufacturing IoT may include:
- Control Assets and Administer Asset Properties
- Track Assets Movement
- Setup Locations
- Maintain Equipment Duty Cycles for Maintenance
- Record Raw Material Shelf Life
- Maintain Asset Family and Digital Thread
- Extend Material Shelf Life
- Enable Cutting and Kitting
- Asset Search and Filter
- Maintain Assets Events
- Record History of Events
- Generate New Assets
- Record Cured Kits
- Document Assets
- Allow Synchronization with Current Systems
- Generate Real-time Production Maps
- Enable Integration with Cut Planning Optimization Systems
- Allow Production of Passive RFID Tags Internally
- Create Customized Alerts
All systems require availability of certain software resources, functionalities, or other hardware components. These prerequisites have to be met in the design of a system. Typical System Requirements for manufacturing IoT may include:
- User Authorization
- Quick installation
- Integration to Next-generation IoT Platforms
- Radio-frequency Identification (RFID) Ability
Let’s delve deeper into some of the Functional Requirements for now.
Control Assets and Administer Asset Properties
The system should be able to manage multiple assets and add new assets. It should be capable of:
- Creating asset properties, e.g., name, ID and shipment date.
- Editing property labels and show / hide asset properties.
- Automatically adding materials’ expiry date, “remaining exposure time,” “tool autoclave cycles left,” and “tool usage time left.”
Trace Assets Movement
The IoT system should be able to:
- Follow assets location from one site to another during the manufacturing process.
- Allow integration of MAT with RFID and other floor sensors to gather real-time assets’ location and condition data.
- Enable asset location reporting manually, through barcode, or hybrid (barcode and RFID).
The system should maintain:
- Asset data from multiple sites (locations).
- Assets movement to and fro various sites, reported using RFID or other sensors.
Maintain Equipment Duty Cycles for Maintenance
The IoT manufacturing system should record all maintenance needs and maintenance activity performed on an asset. Specifically it should:
- Keep data on all tools available at various sites with their duty cycles for preventive maintenance.
- Maintain record and generate reports on maintenance activity preformed on a specific tool.
Record Raw Material Shelf Life
The IoT manufacturing system should:
- Automatically calculate raw material and work in process exposure time and date of expiration.
- Maintain assets’ shelf life and generate automated screen notifications, alerts, emails, or SMS.
Interested in learning more about the details of other Functional and System Requirements of a Manufacturing IoT system? You can download an editable PowerPoint on Manufacturing: Internet of Things Implementation here on the Flevy documents marketplace.
Are you a Management Consultant?
Survival of a business in this digital age largely depends on its ability to timely embrace Digital Transformation. Digital Transformation entails using Digital Technologies to streamline business processes, culture, and customer experiences.
In order to compete today—and in future—and to enable Digital Transformation, organizations should work towards fostering a culture of continuous learning, since Digital Transformation depends on learning and innovation. The organizations that holistically embrace this culture are called “Next-Generation Learning Organizations.”
The next generation of Learning Organizations capitalize on the following key variables; Humans, Machines, Timescales, and Scope. These organizations incorporate technology in enabling dynamic learning. Creating Next-Generation Learning Organizations demands reorganizing the entire enterprise to accomplish the following key functions to win in future:
- Learning on Multiple Timescales
- Man and Machine Integration
- Expanding the Ecosystem
- Continuous Learning
Learning on Multiple Timescales
Next-Generation Learning Organizations make the best use of their time. They appreciate the objectives that can be realized in the short term and those that take long term to accomplish. Learning quickly and in the short term is what many organizations are already doing, e.g., by using Artificial Intelligence, algorithms, or dynamic pricing. Other learning variables that effect an organization gradually are also critical, e.g., changing social attitudes.
Man and Machine Integration
Rather than having people to design and control processes, Next-generation Learning Organizations employ intelligent machines that learn and adjust accordingly. The role of people in such organizations keeps evolving to supplement intelligent machines.
Expanding the Ecosystem
The Next-generation Learning Organizations incorporate economic activities beyond their boundaries. These organizations act like platform businesses that facilitate exchanges between consumers and producers by harnessing and creating large networks of users and resources available on demand. These ecosystems are a valuable source for enhanced learning opportunities, rapid experimentation, access to larger data pools, and a wide network of suppliers.
Next-generation Learning Organizations make learning part and parcel of every function and process in their enterprise. They adapt their vision and strategies based on the changing external environments, competition, and market; and extend learning to everything they do.
With the constantly-evolving technology landscape, organizations will require different capabilities and structures to sustain in future. A majority of the organizations today are able to operate only in steady business settings. Transforming these organizations into the Next-Generation Learning Organizations—that are able to effectively traverse the volatile economic environment, competitive landscapes, and unpredictable future—necessitates them to implement these 5 pillars of learning:
- Digital Transformation
- Human Cognition Improvement
- Man and Machine Relationship
- Expanded Ecosystems
- Management Innovation
1. Digital Transformation
Traditional organizations—that are dependent on structures and human involvement in decision making—use technology to simply execute a predesigned process repeatedly or to gain incremental improvements in their existing processes. The Next-generation Learning Organizations (NLOs), in contrast, are governed by their aspiration to continuously seek knowledge by leveraging technology. NLOs implement automation and autonomous decision-making across their businesses to learn at faster timescales. They design autonomous systems by integrating multiple technologies and learning loops.
2. Human Cognition Improvement
NLOs understand AI’s edge at quickly analyzing correlations in complex data sets and are aware of the inadequacies that AI and machines have in terms of reasoning abilities. They focus on the unique strengths of human cognition and assign people roles that add value—e.g., understanding causal relationships, drawing causal inference, counterfactual thinking, and creativity. Design is the center of attention of these organizations and they utilize human imagination and creativity to generate new ideas and produce novel products.
3. Man and Machine Relationship
Next-generation Learning Organizations (NLOs) make the best use of humans and machines combined. They utilize machines to recognize patterns in complex data and deploy people to decipher causal relationships and spark innovative thinking. NLOs make humans and machines cooperate in innovative ways, and constantly revisit the deployment of resources, people, and technology on tasks based on their viability.
Interested in learning more about the other pillars of Learning? You can download an editable PowerPoint on Digital Transformation: Next-generation Learning Organization here on the Flevy documents marketplace.