Demand for Analytics Tools Grows

This is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of "10 Biggest Big Data Trends."

New Digital User Cases Drive Analytics Demand

Although with these new digital user cases, the imperative is to analyze it because it's new. It's interesting. There's a lot of value in it, and our customers from all industries are actually interested. Many industries that you might not think of as IOT industries like high-tech industries are actually benefiting from connecting their devices to the back to their data platforms and starting to analyze that data. Bring it on. It's great.

Abhishek: And we are getting into our final three trends, and I see some great questions coming up in the queue so please keep up that up, and we will have some time at the end for them. Trend number: 8 self-service data prep becomes main stream as end users begin to shape data. Holly, can you tell us kind of what you are thinking with this one and how you see that playing up?

Holly: So this is both the opportunity and the challenge, because as we see in the increase in the variety of the data and the amount of data and the new use cases that are coming out, it's the business that knows their question, and it's the business that knows the next question and the data questions and how to find the data questions. So we have to put the data and the analysis in the hands of the business, and I think we all understand that what the implication is.

If we go back to our concept of the data like being the foundation, we have to get the data from the source to the business in a much more agile fashion, and so the self service plays a role in that. There was a question earlier on what was the implication of all this self service data? Is it only the business users who are doing to do the data preparation?

Empower Business Users with Analytics

No, that's obviously not the case, but more and more business users will be empowered, and they are all being empowered to prepare or participate in the transformation of the data from source to analysis to insight, and that's a big trend, and we will see more of that happening. We will also see the balance between end user data and self service and end user data preparation and system happening. The traditional data preparation tools are coming into the balance. It's not going to be one-size-fits-all like everything else, but we will see a lot more of this happening.

And the role, if you like, or the opportunity of IT organizations is to enable that in a global manner in a way that's both high-performance and functional, but that also lowers the risk. Keeping risk lower and enabling that, we are seeing definitely more of that on the self-service side. And again the more traditional data preparation tools still have a role to play. So there is a balance there, too.

Case Study: Enhancing Efficiency and Product Quality with Analytics Tools in a Roll Forming Company

Roll-A-Form is a medium-sized manufacturing company specializing in roll forming metal products for the construction, automotive, and industrial sectors. The company produces a wide range of custom metal shapes and components, including steel beams, channels, and trim profiles. With a focus on precision and cost-efficiency, Roll-A-Form has built a strong reputation for high-quality products and reliable delivery.

However, as market demands shifted toward more complex product designs and shorter lead times, Roll-A-Form faced increasing pressure to optimize operations, minimize waste, and ensure product quality. To stay competitive, the company recognized the need to leverage analytics tools to improve its production processes, reduce costs, and make more informed decisions.

Challenges:

Before adopting analytics tools, Roll-A-Form encountered several operational and performance challenges:

1. Production Downtime and Machine Inefficiency:
   The company experienced frequent production downtime due to unanticipated machine breakdowns, tool wear, and inefficient scheduling of maintenance activities. This downtime negatively impacted delivery times and reduced overall throughput.

2. High Scrap Rates and Material Waste:
   Material waste was a significant concern, especially in complex roll-forming jobs where precision was critical. The company struggled to control scrap rates and optimize material usage, leading to increased production costs and reduced profit margins.

3. Inconsistent Product Quality:
   Variations in the roll-forming process, such as inaccurate bend angles or inconsistent thickness, led to occasional quality issues. These inconsistencies resulted in customer complaints, product rework, and lost business opportunities.

4. Lack of Real-Time Visibility:
   Roll-A-Form had limited real-time visibility into its production floor. The company's legacy systems provided only periodic reporting, which made it difficult to respond quickly to emerging issues, such as machine malfunctions or production bottlenecks.

Solution: Implementation of Analytics Tools

To address these challenges, Roll-A-Form invested in a suite of advanced analytics tools, integrating them into its existing manufacturing environment. The tools included:

• Manufacturing Execution System (MES) with built-in analytics for real-time production monitoring.
• Predictive Maintenance Analytics powered by machine data and IoT sensors.
• Quality Control Analytics to ensure consistent product quality and reduce variability.
• Supply Chain and Inventory Analytics to optimize raw material usage and reduce waste.

Key Steps in the Analytics Implementation:

1. Data Integration and Collection:

Roll-A-Form began by integrating IoT sensors into its roll-forming machines to collect real-time data on machine performance, tool wear, and product specifications. These sensors gathered data on variables such as machine temperature, pressure, speed, and torque, which were then fed into the company's central analytics platform.

In addition to machine data, the company integrated data from its inventory management system, supply chain, and quality control processes. This comprehensive data collection provided a holistic view of the production environment.

2. Real-Time Production Monitoring and Alerts:

With the MES in place, production managers could now monitor real-time performance data from the shop floor. Dashboards displayed key metrics such as:

• Machine uptime and downtime
• Cycle times and throughput
• Material usage efficiency
• Work-in-progress (WIP) inventory levels

The system was also set up to send automated alerts whenever production anomalies occurred, such as a machine running outside of its optimal parameters or a significant deviation from the desired product specifications. This allowed production staff to intervene quickly, minimizing downtime and preventing defective products from moving further down the line.

3. Predictive Maintenance and Machine Optimization:

Using predictive analytics, Roll-A-Form could now predict when machines were likely to fail based on historical data and real-time monitoring. For example, the system analyzed vibration, temperature, and motor power consumption to identify signs of machine wear and tear. The company shifted from reactive to predictive maintenance, scheduling maintenance activities just before a failure was likely to occur, thereby reducing unexpected breakdowns and prolonging machine life.

In addition to preventing downtime, the analytics tools helped optimize machine settings to ensure maximum performance. The system continuously analyzed machine data to suggest optimal configurations for different product runs, adjusting variables like roller speed and pressure to achieve the best results for each product design.

4. Material Waste Reduction and Scrap Minimization:

The company implemented advanced analytics for material usage and scrap reduction. By analyzing historical production data and real-time machine settings, the system could identify patterns that led to excess scrap, such as incorrect machine calibrations or suboptimal material feeding rates.

Roll-A-Form used these insights to adjust its production processes, resulting in a significant reduction in material waste. The analytics tools also helped the company optimize material cutting patterns, ensuring that each roll of metal was used as efficiently as possible.

5. Quality Control Analytics:

To improve product quality, Roll-A-Form implemented quality control analytics that tracked and analyzed key quality metrics during production. The system monitored variables such as bend angles, material thickness, and surface finish in real time. If the product deviated from the desired specifications, the system immediately flagged the issue and allowed operators to adjust the settings.

The analytics tools also performed root cause analysis to determine the factors that contributed to product defects. This helped the company identify and address the underlying causes of quality issues, such as tool misalignment or incorrect machine settings, rather than just treating the symptoms.

6. Supply Chain and Inventory Optimization:

By analyzing data from the company's supply chain and inventory systems, the analytics tools provided insights into raw material usage, lead times, and supplier performance. This allowed Roll-A-Form to optimize its inventory levels, ensuring that materials were available when needed without overstocking, which tied up capital.

The company also used these tools to evaluate supplier performance, identifying the most reliable suppliers based on delivery times, quality, and cost. This enabled Roll-A-Form to make data-driven decisions about which suppliers to partner with for future projects.

Results and Benefits of Analytics Implementation:

1. Increased Production Efficiency:

With real-time monitoring and predictive maintenance, machine downtime was reduced by 30%, leading to a significant improvement in overall throughput. The ability to optimize machine settings in real time also resulted in faster cycle times, enabling the company to meet tighter production deadlines without sacrificing quality.

2. Reduced Scrap and Material Waste:

Through the use of material optimization analytics, Roll-A-Form reduced its scrap rate by 20%. The company achieved more precise control over the roll-forming process, minimizing material waste and improving overall efficiency. This reduction in waste also contributed to lower production costs, increasing profit margins on high-volume orders.

3. Improved Product Quality and Customer Satisfaction:

The implementation of quality control analytics led to a 15% reduction in product defects. The ability to monitor product quality in real time and make adjustments during production ensured that customers received consistently high-quality products. Customer complaints related to product quality decreased, and the company saw a 10% improvement in customer satisfaction scores.

4. Cost Savings from Predictive Maintenance:

By shifting to predictive maintenance, Roll-A-Form reduced maintenance-related downtime by 25%. The company also extended the lifespan of its machines by identifying and addressing wear and tear before it led to significant damage. This resulted in lower maintenance costs and fewer disruptions to production schedules.

5. Data-Driven Decision Making:

The analytics tools empowered Roll-A-Form to make more informed, data-driven decisions at every level of the organization. Production managers could optimize machine settings in real time, maintenance teams could proactively address issues, and executives had access to detailed performance metrics that allowed them to identify areas for improvement and make strategic investments.

6. Scalability for Future Growth:

The analytics tools provided Roll-A-Form with the scalability it needed to handle future growth. The company could easily expand its production capacity by adding new machines and sensors to the system without needing to overhaul its entire data infrastructure. This flexibility positioned Roll-A-Form to take on larger and more complex projects, driving future business growth.

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