A Perspective of the Manufacturing Future: The Value

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A Perspective of the Manufacturing Future: The Value

In this series-concluding article, get an overview of the value of IIoT and witness the impact on manufacturing that AI, automation, and more are having.

· IoT Zone ·
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This is the fifth post in a series about my perspective of what the future has in store for various aspects of manufacturing. To recap, I have been an advanced manufacturing engineer implementing manufacturing technology in GE shops for five years. During this time, I generated a roadmap for implementing technology with respect to a set of common manufacturing challenges.

I speculated about what the future has in store given what we know to be possible today. In my previous posts, we've covered the future for cutting tools for machiningproduction managementquality control, and inventory control

The value of these concepts relate to the following:

  • Reducing production costs through
    • Labor,
    • Tooling and supplies,
    • Equipment depreciation,
    • Equipment maintenance,
    • Cost of quality, and
    • Energy
  • And enabling new sales and growth by
    • Reducing New Product Introductions (NPI) costs and process development time,
    • Improving a manufacturer’s demand response and ability to meet a customer’s schedule, and
    • Improving competitiveness

With regards to production costs, labor costs are frequently a top driver for manufacturing operations, especially in Heavy Industry companies. Tools and supplies, or consumables, will often comprise a significant portion of the operations budget, especially in machining operations. It’s not uncommon for consumables to be as much as 40% or more of the costs. Equipment depreciation, equipment maintenance, cost of quality, and energy costs are also significant but, in many cases, play lesser roles.

Traditionally, manufacturing shops have focused most of their effort on reducing production costs. But, they also have significant ability to enable new sales and growth for their respective businesses. By improving cycle times, reducing production costs, and leveraging best practices to reduce development time, NPI cycles can be greatly reduced which allows the business to develop and deploy products to the market faster or at the pace that their customers are demanding.

Greater visibility to the status of material and processes within the supply chain, coupled with being able to reduce cycle times as needed, will enable manufacturers to be more nimble and better able to respond to ebbs and flows in the market. All of the cost reductions and demand responsiveness will ultimately lead to a more competitive business.

Cutting Tools

In the first post, I discussed how automation, data, and AI were going to manifest in the way cutting tools are handled. These themes can drive an enormous amount of value.

Automation will have an impact on costs associated with safety due to handling sharp tools and repetitive motion, costs associated with quality by making the tool setting more repeatable and data transfer automatic (no mistyping), and costs associated with labor because the people will be able to focus on other value-adding tasks and the operation will become more productive.

Data will have an impact on costs associated with labor in a similar way as automation because data will enable greater productivity thereby lower labor costs. Data will impact the costs associated with tools and supplies, and quality because it will provide unprecedented visibility into the real trade-off between the operation time of the machine and cutting tool life and cutting tool-life will be monitored and controlled to maximize the cutting tool’s usage.

AI will impact each cost by augmenting decisions and enabling them to happen faster or, in some cases, automatically. AI will also enable faster root-cause analysis when issues arise because it is identifying anomalies and/or data correlations.

Production Scheduling

Imagine a future where data does not exist in pockets and the data is analyzed for short-term glimpses into the future and optimization of the production schedule. From the data perspective, the contiguous digital thread from sales to customer monitoring and back again will ensure that every point along the thread will operate with the most up-to-date information available. Meaning that changes will not take days or weeks to propagate from one person/organization to the next and manufacturers will be able to respond faster.

The short-term glimpses into the future will prevent unexpected downtime costs because machine health will be monitored, maintenance events will be predicted, and downtime will be scheduled and planned for. Similarly, costly downtime due to labor/skill absence will be avoided. Other outside influences will also be coupled with those predictive analytics to optimize the macro production system from the aspect of minimizing risks to the schedule and production costs.


Envision a future where shared quality data will provide increases process yield, flexibility, and capability. The manufacturing quality data will also provide insights into the manufacturability of a design before making the first part. The manufacturing quality data will be used to identify correlations between product performance for the customer and manufacturing influences. The obvious value of collecting, analyzing, and using the quality data is for reducing the cost of quality by increasing first-time-yield.

However, there is also value in coupling the manufacturing quality data with product performance data to identify and drive out failure mechanisms that originate as part of the manufacturing process. Probably the greatest value would come from using the manufacturing quality data within the context of manufacturability of products.

I’ve heard it said in conversations that 80% of production costs are dictated by the product’s design. I do not have the research to reinforce this statement but intuitively it makes sense. The materials used in the design impacts the cost of raw materials but it also drives costs of manufacturing consumables, manufacturing cycle times, and the equipment used to produce the parts. The complexity of the geometry coupled with the dimensions and tolerances of the design dictate the type of manufacturing process to be used, the equipment used, and even the expertise level of the manufacturing associate who runs the process. Each of those factors has significant impact on the costs incurred during manufacturing.

There are many factors influencing manufacturability and quality data may not convey the entire narrative around manufacturability; however, the quality data will show the capability of existing processes to meet specifications. This coupled with current manufacturing costs and the costs to upgrade or improve manufacturing capability to meet design requirements should be evaluated in conjunction with the expected customer value during the design phase to ensure that the design is providing the customer value in an efficient way for the business. The flow of quality data/analysis and presentation in an insightful way will enable this evaluation.


In the fourth post, on inventory management, I discussed a future where data analysis coupled with process analysis and process modeling/simulation will be used to drive down inventories that manufacturers keep in the supply chain. In large heavy industry enterprises, components can be very expensive and have long lead times, which leads to a lot of capital being tied up in inventory. The amount of capital that is constrained varies based on many influences but it could easily be into millions of dollars. Risk associated with missing deadlines due to disruptions in the supply chain is a major contributor to carrying a lot of inventory.

In addition to constrained capital, there are also operational costs associated with managing the inventories and this does not provide value to the component itself. Therefore, the value of pursuing efforts around inventory management is in the reduction of capital that is tied-up in product, insight into the supply chain for the avoidance of costly disruptions, and the reduction of carrying costs.

In this post, I’ve wrapped up a series on my perspective of the manufacturing future, which has been based on my experience working with factories. I'm confident the pursuit of these endeavors will unlock value for manufacturers. In my next set of blog posts, I will discuss getting started down these various digital paths. 

ai, automation, iot, manufacturing

Published at DZone with permission of Andy Henderson . See the original article here.

Opinions expressed by DZone contributors are their own.

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