When it comes to optimizing supply chain performance, manufacturing supply chains that link toolmakers, OEMs and suppliers are frequently overlooked. Often problems that arise can be attributed to the tooling — such as molds and dies — used to manufacture products, or when an outsourced supplier fails to use that tooling to its full potential. Though these problems are easily pinpointed with the right data, that data is rarely available.
A combination of sensors and software can identify production issues associated with poor tooling and their improper use. When analyzed, sensor data can identify manufacturing issues and predict delivery problems, strengthening the performance of manufacturing supply chains and the relationships among their stakeholders.
Suboptimal quality management. Tooling, quite literally, shapes the output of manufacturing processes. These expensive assets, ranging in cost from $20,000 to $500,000 each, are the intellectual property of original equipment manufacturers (OEMs).
OEMs often hand over their tooling to outsourced manufacturing suppliers, at which point they lose visibility into the tooling’s performance, and relinquish control over how it’s used.
Leaving suppliers to their own devices can lead to quality problems, both for the tooling and its output. For example, suppliers may be tempted to decrease a tooling’s cycle time beyond an OEM’s specifications, in order to increase margin or stay on schedule. But a variance of even a few seconds in manufacturing speed can reduce the tooling’s lifespan and produce suboptimal parts.
Suppliers may also tolerate erratic production patterns, such as failing to maintain the proper temperature or pressure during the manufacturing process, or may assign an inexperienced operator to a machine. All of these actions and inactions will impact the quality of the parts produced.
Fortunately, technologies exist that can address these issues and maximize manufacturing efficiency. Referred to as “tooling digitalization,” these include internet-of-things (IoT) sensors that are integrated with toolings to collect relevant data on manufacturing processes, and artificial intelligence software that analyzes the data to flag potential problems.
Costly asset management. Toolings are expensive and proprietary assets, yet OEMs have difficulty managing these mission critical components. Since OEMs lack visibility into their use and performance, managing their lifecycles can be next to impossible.
An OEM will know how old a piece of tooling is. Its life span, however, is based not on its age but on how many “shots” or parts the tooling can make. Without visibility into a tooling’s use, OEMs are unable to predict when a component may reach its life’s end. If a tooling breaks down too soon, it can take two to three months to replace it, wreaking havoc on any supply chain.
Tooling digitization allows manufacturers to use data to manage toolings in real time, ensuring that these valuable assets are being utilized to their full potential. By monitoring tooling activity, and by benchmarking the performance of toolmakers and suppliers, OEMs can implement practices that will result in higher returns on their investments and provide them with competitive advantages.
Lack of visibility into late-delivery risk. OEMs depend on their suppliers to deliver parts on time. If a supplier can’t meet delivery expectations, the OEM needs to know in advance, so that it can make other arrangements and adjust its supply chain.
Suppliers also want to prevent late deliveries on their end, in order to remain in good standing with the OEM and to avoid paying contractual penalties. Since delivery performance can make or break an OEM-supplier relationship, both parties are incentivized to deal with parts delivery issues as constructively as possible.
Many assume that late parts delivery represents a logistics problem, when in fact its source often lies in the manufacturing process itself.
Late deliveries of parts generate negative ripple effects across OEM supply chains and beyond. Among other things, they burden OEMs with higher inventory costs when they have to warehouse the parts that were delivered but can’t be assembled without the delayed pieces. The longer they take to assemble, the more challenging it is for OEMs to honor their commitments to customers.
OEMs could avoid these problems if they had data on how many parts were produced and where. But since the parts are manufactured by external suppliers, that data is hard to come by. The COVID-19 pandemic exacerbated these issues, when many production facilities locked down for periods of time.
Tooling digitalization solves these problems by bringing visibility to production rates. When the OEMs know the quantity and location of the parts being produced, they gain insight into whether the ongoing parts production is adequate to meet delivery expectations. Artificial intelligence can also be used to assess the risk of late deliveries.
Inefficient sourcing practices. Molds and dies are operated all over the world, making it hard for OEMs to track their suppliers’ activities and performance. That’s especially true for complex products, which may require tens of thousands of parts contributed by hundreds or thousands of suppliers.
The data required to keep track of these supply chain activities can be dizzyingly complex. Often it’s stored in spreadsheets, but even when there’s a collection system, suppliers rarely have the time to update information on parts produced, cycle times and maintenance. OEMs may receive inaccurate data, making it impossible for them to know if their parts are being produced under suboptimal conditions. In any event, without the automation provided by tooling digitalization, supplier data is certainly not being delivered in real time.
In order to hone the efficiency of their supply chains, OEMs need to benchmark both the tooling and how the supplier is using it. Production data could identify a performance problem with the tooling, a condition which may be attributable to the toolmaker. Or it could indicate an issue with a supplier if there are variances in supplier performance.
By benchmarking toolmakers, OEMs can reward the better performers and demand that others make improvements, or be squeezed out. Similarly, historical and real-time data allows OEMs to make informed decisions on which suppliers they should stick with, and how confident they should be about the quality of the parts that suppliers produce.
Absence of data-driven process innovation. Manufacturing companies are always looking for ways to improve. Without data on the quality of their processes, and those of their suppliers, however, they risk stagnation. When OEMs have access to the information they need, they can supervise manufacturing processes from end to end, and flag underperforming components of their supply chain.
When it comes to process improvement, the crucial issues facing OEMs include identifying quality issues before they occur and finding the source of problems after they come to light. Deviations from OEM specifications on tooling use could compromise product quality — including conditions not necessarily discernible to the naked eye — and lead to problems down the road, after a product has been produced and is in use.
Analyzing comprehensive production data allows OEMs to benchmark supplier performance and optimize supply chains, leading to better strategic decisions, such as whether to make or buy specific components.
Production data may also enable OEMs to shift their manufacturing strategies to rely more heavily on the outsourcing of parts production. By identifying and contracting with the most reliable suppliers, OEMs can proceed along the outsourcing path, confident that their strategies will yield efficient supply chains and higher profits.
eMoldino Enables Transparency for Manufacturing Supply Chains
OEMs and suppliers alike require a solution that addresses the pain points in the manufacturing supply chain. For close to a decade, eMoldino has been working with leading companies in the automotive, electronics, consumer goods, cosmetics, medical devices, and aerospace and defense industries to deliver solutions that are easily implemented, provide immediate returns on investments, and quickly identify the sources of manufacturing problems by analyzing undisputed data.
eMoldino’s wireless IoT sensors are easily and swiftly installed on tooling assets and, once installed, transmit data to cloud servers on manufacturing cycle time, temperature, pressure and tooling location. “The ease of implementation makes it possible to implement the system on a global scale,” notes Chris Yeong, senior project manager at eMoldino.
Analyzing the sensor data provides visibility into production efficiency and tooling conditions, alerts OEMs of production anomalies, and delivers forecasts on late delivery of parts. Armed with this information, OEMs and suppliers can make process and production improvements and strengthen their relationship.
“By implementing tooling digitalization,” says Yeong, “manufacturers can make sure they’re optimizing the use of their significant investments in tooling.”
That’s how eMoldino’s very first customer, Samsung Electronics, was able to save millions of dollars a year within five years of implementing the system. “In 2012,” Yeong says, “Samsung was purchasing 15,000 new toolings per year. By 2017, they managed to cut that in half, not by producing less but because they were able to better manage their tooling assets.”
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