How Business Intelligence Becomes Performance Management

Below is a continuation of the transcript of a Webinar hosted by InetSoft on the topic of Current Trends in Performance Management. The presenter is Mark Flaherty, CMO at InetSoft.

Mark Flaherty (MF): Something like performance management is fairly strategic. You need to have business support, but literally the ownership has to be by senior management. It’s not something that an individual in the bowels of an organization can start to implement.

So much of business intelligence is about building a data warehouse and giving users a tool. That’s all well and good, and for InetSoft, that is a successful sale, and the enterprise might actually get some value right away. But the question is does it really move the business forward?

What are the users looking at? Maybe we’ll give them a dashboard. What should be in the dashboard? I don’t know, whatever the users want in that dashboard. Is there a cause and effect relationship associated with what’s in the dashboard? Well, often not. What is it we’re actually tracking? That’s why planning, for instance, is a real critical piece.

Let’s explain it in a slightly different way. One kind of model to explain this is the “management system.” Any management system, it doesn’t have to be based on technology, has four things in it. There are four things that a management system actually does.

Management Sets the BI Strategy

Management conceives the vision, they set the strategy. They set the goals and objectives for the organization. They execute as an organization against those goals. And finally they evaluate their performance.

The problem in so many organizations is that different management activities get disconnected. It’s very typical that people have been executing against a strategy which is no longer in place. Our strategy might not have been fully vetted. So that the goals and objectives that have been created are nonsensical. They can’t actually succeed with that strategy as it was defined.

So performance management can be injected in there. So in between the notion of setting the strategy, goals, and objectives, there is a vetting process. So use modeling to actually vet the strategy to come up with multiple scenarios so that when we do set the goals, they are actually practical.

In between the goal-setting and execution, that is where you have to make a real commitment, and that’s where planning plays a critical role. So you fortify that commitment process with a planning solution. So there are two ways right there, by the way, that are unique to performance management that are not really business intelligence. So the modeling capability that is used in between the vision and goal setting and then the planning solution that is implemented between the goal setting and the execution process are unique to performance management.

From there you actually use more business intelligence capabilities. So in between the execution phase and the final evaluation, you’re constantly tracking and monitoring your performance so you can tune your execution. That’s where business intelligence with dashboards for analysis and monitoring come in. And in between the evaluation phase and incorporating your learnings or findings and adapting your strategy, you need to do some in-depth analytics.

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That’s the more traditional forecasting or predictive analytics as well as things like data mining. So there is a piece that is more business intelligence as it relates to the management system, and then there is a piece that is more performance management, and you couple them together.

Case Study: Silicon Wafer Manufacturing Company's Implementation of a Business Intelligence Performance Management Solution

WaferTech Innovations is a mid-sized silicon wafer manufacturing company that supplies semiconductor materials to major electronics and technology firms worldwide. With a workforce of 1,000 employees and three production facilities located across North America and Asia, WaferTech is positioned as a critical player in the semiconductor supply chain. The company's main products include 200mm and 300mm silicon wafers, which are essential for producing integrated circuits used in a wide range of applications, from smartphones to automotive electronics.

The Challenge

As global demand for semiconductors surged due to advancements in consumer electronics, automotive technologies, and Internet of Things (IoT) devices, WaferTech found itself under pressure to scale up production while maintaining high quality and competitive costs. However, the company faced several challenges that threatened its ability to meet market demand:

1. Data Silos: WaferTech's data was scattered across different departments, including production, quality control, supply chain management, and finance. Each department used its own systems, which made it difficult to have a unified view of the company's operations.

2. Manual Reporting: The company relied on manual data collection and analysis processes. Reports were often delayed, inaccurate, and lacked real-time insights. This hampered timely decision-making and caused inefficiencies in production scheduling and resource allocation.

3. Quality Management: Silicon wafer manufacturing is a highly precise process that requires constant monitoring of production parameters. Any small deviation in temperature, pressure, or chemical composition can result in defects, leading to scrap, rework, and delays in customer deliveries.

4. Supply Chain Volatility: The company's supply chain was vulnerable to disruptions, such as delays in raw material shipments or fluctuations in demand from customers. Without real-time data and analytics, WaferTech struggled to forecast demand accurately and manage inventory efficiently.

The Solution

WaferTech decided to implement a Business Intelligence Performance Management Solution (BI-PM) to address these challenges. After evaluating several vendors, the company chose a BI-PM platform that could integrate data from multiple sources, provide real-time analytics, and support performance monitoring across all levels of the organization.

The key objectives of the BI-PM implementation were:

• Integrating data across departments to provide a unified view of operations.
• Automating data collection and reporting to eliminate manual processes and provide real-time insights.
• Improving production quality by implementing real-time monitoring and predictive analytics.
• Enhancing supply chain management through demand forecasting, inventory optimization, and supplier performance monitoring.

Implementation Process

The implementation of the BI-PM solution at WaferTech occurred in three key phases:

1. Data Integration and Centralization WaferTech's first step was to break down the data silos by integrating data from various systems, including Enterprise Resource Planning (ERP), Manufacturing Execution Systems (MES), and Quality Management Systems (QMS). This integration created a centralized data warehouse that served as the foundation for the BI-PM solution.

2. Automated Reporting and Dashboards The BI-PM platform enabled the automation of reporting processes across departments. Customized dashboards were developed for different roles, from production managers to C-suite executives, providing real-time insights into key performance indicators (KPIs) such as production output, yield rates, equipment downtime, and inventory levels.

3. Predictive Analytics for Quality Control One of the most critical applications of the BI-PM solution was in quality control. The platform's predictive analytics capabilities allowed WaferTech to monitor key variables in the manufacturing process, such as temperature, pressure, and chemical concentrations. By analyzing historical data, the system could identify patterns that led to defects and provide early warnings when production parameters deviated from optimal ranges.

4. Supply Chain Analytics The BI-PM platform integrated data from suppliers and customers, enabling WaferTech to improve demand forecasting and inventory management. Real-time insights into raw material availability, lead times, and customer demand allowed the company to optimize its supply chain and reduce the risk of stockouts or excess inventory.

Results and Benefits

1. Improved Operational Efficiency With real-time visibility into production data, WaferTech was able to optimize its manufacturing processes. The company reduced downtime by 15% and increased production yield by 10%, thanks to predictive analytics that flagged potential equipment failures or process deviations before they became critical issues.

2. Enhanced Quality Control The implementation of predictive analytics in quality control led to a 20% reduction in wafer defects. By identifying early warning signs of defects, WaferTech could make adjustments to the production process in real time, reducing the amount of scrap and rework required.

3. Better Decision-Making Automated reporting and centralized data enabled WaferTech's leadership team to make more informed decisions. The company reduced the time spent on data gathering and analysis by 30%, allowing managers to focus on strategic planning rather than manual data manipulation.

4. Supply Chain Resilience By improving demand forecasting accuracy by 25%, WaferTech was better able to match production with customer demand. The company also reduced raw material inventory levels by 12%, freeing up capital and reducing storage costs. Additionally, the company could monitor supplier performance and identify potential risks in the supply chain earlier.

5. Increased Competitiveness The improvements in production efficiency, quality, and supply chain management allowed WaferTech to become more competitive in the global semiconductor market. The company was able to meet growing demand for silicon wafers without compromising on quality, leading to stronger relationships with key customers and a 5% increase in market share.

Key Takeaways

• Data Integration is Crucial: WaferTech's success hinged on its ability to integrate data from multiple sources into a single platform. This allowed the company to break down silos and gain a holistic view of its operations.
• Real-Time Insights Drive Efficiency: The shift from manual reporting to automated, real-time dashboards helped the company make faster and more informed decisions, which improved overall efficiency.
• Predictive Analytics Enhance Quality: By using predictive analytics to monitor production processes, WaferTech was able to significantly reduce defects and scrap, leading to higher product quality and lower costs.
• Supply Chain Visibility is Key: BI tools provided WaferTech with greater visibility into its supply chain, allowing for more accurate demand forecasting and better management of raw material inventories.