Enterprises that seek to become proficient in advanced manufacturing must incorporate manufacturing management tools and integrate data throughout the supply chain to be successful. This course will make students aware of what a digitally connected enterprise is, as they learn about the operational complexity of enterprises, business process optimization and the concept of an integrated product-process-value chain. Students will become acquainted with the available tools, technologies and techniques for aggregation and integration of data throughout the manufacturing supply chain and entire product life-cycle. They will receive foundational knowledge to assist in efforts to facilitate design, planning, and production scheduling of goods and services by applying product life cycle data. Main concepts of this course will be delivered through lectures, readings, discussions and various videos. This is the sixth course in the Digital Manufacturing & Design Technology specialization that explores the many facets of manufacturing’s “Fourth Revolution,” aka Industry 4.0, and features a culminating project involving creation of a roadmap to achieve a self-established DMD-related professional goal. To learn more about the Digital Manufacturing and Design Technology specialization, please watch the overview video by copying and pasting the following link into your web browser: https://youtu.be/wETK1O9c-CA
The Transparency of Product Life Cycle Data
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來自 University at Buffalo 的課程
Advanced Manufacturing Enterprise
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從本節課中
The Concept of a Connected and Collaborative Enterprise
The purpose of this module is to educate students on why a holistic approach is necessary for analyzing the impact of advanced manufacturing on the success of an enterprise.
與講師見面
Sara BehdadAssistant Professor
Mechanical and Aerospace Engineering, Industrial and Systems Engineering
The topic of this lesson is on understanding the need for change.
In this lesson, we describe the need for greater transparency
and the desire to collect and use product lifecycle data.
Before we understand the need for a change,
let's describe the concept of product lifecycle.
Product lifecycle refers to all the stages of a product, from the earliest stage
of concept design to disposal and recovery.
Product lifecycle can be viewed from different perspectives.
In general, product lifecycle covers three main phases:
beginning of life, including design and manufacturing;
middle of life, including use, service, and maintenance;
and finally end of life, there products are recollected,
sorted, disassembled, remanufactured, recycled, reused, or disposed.
Now let's discuss why organizations need to collect product lifecycle data.
There can be different motivations to collect product lifecycle data.
A few of them are transparency, business evaluation, improvement, and forecasting.
Full transparency enables both the consumers
and manufacturers to make better well-informed decisions.
Examples of transparency include the release of the nutrition information on food products
and the release of environmental impacts information on products to consumers.
In order to have transparency, we need to collect
and share data of different aspects of a product or enterprise.
Another motivation to collect product lifecycle data is to use data
for business evaluation and improvement.
Continuous improvement requires continuous evaluation
of the business process from different aspects.
Continuous evaluations requires continuous collection of data to evaluate.
For example, collecting and analyzing costumer's online reviews
about product will help manufacturers
and product designers to improve the next generation of product design.
Finally, by collecting data,
the enterprise management is able to forecast valuable information.
An example of this is when an enterprise collects the market trends
to predict future market demands.
Another is when an enterprise collects the energy usage
of an industrial machine to predict its failure.
As we just learned, the product life-cycle covers three main phases:
beginning of life, middle of life, and end of life.
The information flow during the beginning of life is quite complete
and is collected using different information management systems,
such as computer-aided design, or CAD models,
computer-aided manufacturing, or CAM systems, and product data management or PDM systems.
The information flow becomes less complete after the beginning of life cycle.
In fact, the information flow is stopped after product is delivered to a consumer
for the majority of today's consumer products such as household appliances and vehicles.
This is because after products are delivered to consumers,
the ownership of product is no longer with companies and is transferred to customers.
Therefore it is not easy for companies to track product usage data generated by consumers.
As a result, decision makers involved in each phase
of a product lifecycle make decision based on an incomplete
and inaccurate product lifecycle data from other phases,
which in many cases result into inefficient decisions.
Product lifecycle data can be categorized under static data and dynamic data.
Static data is mainly generated at the beginning of life phase
and rarely changes during the lifetime of the product.
Examples of static data are bill of materials, material content, take back information,
disassembly instruction, return policies, and recycling information.
Static data is fairly complete
and can be collected through existing data management systems.
The dynamic data includes mainly the data generated during the usage phase.
Examples of dynamics data are use patterns,
environmental conditions, and servicing actions.
Dynamic data is often lost and is difficult to obtain during the product lifecycle.
Product lifecycle have many applications.
Suppose that companies are able to track
and trace product usage data generated by consumers.
Companies could gain substantial business advantages
if they use product usage data to improve their products and optimize relevant operations.
For example, lifecycle acquisition can be used to enable product-related services.
Product lifecycle data is valuable
and can be used to facilitate the service delivery such as maintenance,
repair, and after-sales services.
It is particularly important in the aviation industry.
In aviation industry, capital equipment,
and products have long service lives and complex configurations.
The profitability of the industry
is not only from the sales of capital equipment and aircrafts,
but from maintaining them for an anticipated 30 plus year lifespan.
Therefore, maintenance and repair companies aim to minimize maintenance cost
and turnaround time to maximize revenue.
Due to the complexity of the system, automated information retrieval
and product structure information
can help companies easily detect their potential failures
and obtain the information necessarily for component repair and tooling design.
Collecting product lifecycle data is a backbone for an integrated enterprise.
