In the evolving landscape of modern manufacturing, tracking metrics has evolved from a good practice to an absolute necessity. It’s no longer about passive observations: data is the key to unlocking and maintaining efficiency, profitability, and competitiveness. The data-driven insights derived from metrics have taken center stage. They are the key to reshaping strategies, optimizing operations, and fostering resilience in the face of change.
Which manufacturing metrics matter most? The answer may vary based on a company’s goals, challenges, and organizational structure. But for many, key metrics revolve around standout manufacturing industry themes of profitability, sustainability, and growth. We find this to be true in our everyday conversations with leading manufacturers, but it also shows up in industry data. For example, the World Economic Forum’s recent study highlighting the top metrics that manufacturers are prioritizing emphasizes productivity, quality, speed, and flexibility – factors that contribute significantly to these overarching themes.
Let’s dive into the critical metrics that fuel profitability, sustainability, and growth and how leading manufacturers track and evaluate those metrics to drive success.
In the light of looming economic uncertainty and other challenges, many manufacturers are increasing their level of financial caution. They are focused on reducing expenses, enhancing operational efficiency, and raising product quality standards – all with the goal of increasing profitability. As a result, it becomes a critical, recurring process to monitor profitability metrics consistently across the entire business to identify bottlenecks, improve operations, and reduce unnecessary spending.
When it comes to profitability metrics, tracking the right data to optimize throughput and generate accurate forecasts is of paramount importance.
Optimizing throughput directly influences efficiency and profitability, making throughput metrics some of the most important measures manufacturers collect. Streamlining processes, reducing bottlenecks, and ensuring the proper use of resources can significantly boost your organization’s capacity to meet customer demands and reduce wasted time and money.
Tracking key throughput metrics such as production rate, cycle time, cost per unit produced, and defects per million is essential for identifying areas of improvement. With a comprehensive view of your operations and continuous monitoring of these production metrics, manufacturers can reduce wasteful practices across labor, materials, and operational expenses while producing more products at similar or reduced costs.
Accurately forecasting profitability requires a focus on tracking internal metrics – including metrics about your products, customers, staffing, operations, and supply chain – as well as external economic data and trends.
By closely monitoring metrics spanning the entire product and customer lifecycle, you can fine-tune your operations, offerings, and pricing strategies for maximum profitability. Going beyond prescriptive analytics and applying predictive models to forecast future profitability enables better decision-making in every area. With a comprehensive understanding of your financial trajectory, you can make better procurement decisions, staff at just the right levels, minimize carrying costs and excess inventory, and reduce stock-outs for your most profitable products.
A strong profit forecasting strategy relies on holistic data across the manufacturing ecosystem, but with the right metrics in place, it makes it easier for manufacturers to make data-driven decisions, optimize resource allocation, and adapt successfully to ever-changing market dynamics.
Manufacturers are experiencing growing demands from regulatory bodies, consumers, and shareholders to boost their sustainability efforts. While many consider supply chains to be the primary factor in sustainability initiatives, they are not the only piece of the sustainability pie. One critical way manufacturers can improve sustainability is by minimizing waste by improving the quality of manufactured products and extending the lifespan of machinery and tools.
Keeping a close eye on quality and machinery metrics is pivotal in establishing an efficient and sustainable factory environment that consistently produces high-quality products.
There are two critical aspects to product quality. First is optimizing the quality of products coming off your production line by identifying features and designs that are likely to result in flaws. AI and machine learning technologies can help identify factors resulting in out-of-spec parts and more quickly dial in designs and manufacturing processes. This minimizes waste and maximizes the volume of products meeting shipping criteria. Vigilant tracking of key metrics such as defect rates, yield, and production cycle times will help you identify issues in real time, reducing waste and production costs.
Quality optimization also helps manufacturers deliver higher-quality products to the customers, raising customer satisfaction, fostering brand loyalty, and yielding more word-of-mouth. Producing top-quality products with long lifespans and products designed to be serviced easily offers a better customer experience while reducing consumer waste. Tracking your product lifespan, designing products that can be effectively serviced, and identifying warranty claim trends that can be fixed through design upgrades can increase the overall sustainability of your products and meet customer and shareholder demands.
Predictive maintenance is a game-changer in manufacturing because this proactive approach extends the lifespan of machinery, optimizes maintenance schedules, prevents unexpected breakdowns, and reduces overall maintenance costs. Effective predictive maintenance strategies contribute to a company’s profitability and their sustainability, minimizing scrap from malfunctioning machines and increasing the lifetime of factory equipment. Reducing the frequency of equipment failure and replacements can improve both a manufacturer’s bottom line and the environmental impact of their factory operations.
By leveraging Industrial Internet of Things (IIoT) technologies and quality data, you can forecast asset failures long before they happen and schedule maintenance accordingly. This approach minimizes downtime and leads to substantial reductions in operational and maintenance costs. Tracking metrics such as mean time between failures (MTBF), mean time to repair (MTTR), and machine utilization rate will help maintain equipment health.
Traditionally, manufacturers have centered their efforts on mass-producing standardized products. However, a shifting landscape driven by the pursuit of faster time-to-market and increased control over brand and pricing demands a more in-depth understanding of customer preferences and demands. As customer expectations are shifting towards personalized products, businesses face the challenge of meeting these demands without the risk of overproduction, particularly for products tailored to specific market segments.
The crucial factor here is the ability to gain deep insights into the customer’s desires and needs, drawing from historical behaviors and purchasing patterns as well as understanding the details of demand, including the likelihood of product purchases. This knowledge empowers manufacturers to calculate production volumes and distribution accurately, ensuring products reach the right places at the right time.
Manufacturers that holistically understand their customers and anticipate demand are better able to fuel their company’s growth and make data-driven decisions that benefit the business.
Demand forecasting plays a pivotal role in efficiently planning production and supply chain activities. By accurately predicting demand, you can optimize inventory levels, reduce carrying costs, and avoid overproduction. All of this relies on tracking key metrics that measure historical sales data, market trends, customer behavior patterns, and economic indicators. Continuously assessing and fine-tuning these metrics allows you to better align production with customer needs and improve operational efficiency.
Machine-learning-driven models have created new opportunities to improve accuracy and account for uncertainty in forecasting. They can adapt rapidly according to changing market conditions, which is indispensable for manufacturers striving for efficiency and competitiveness. In addition, taking a stochastic approach to demand forecasting can help manufacturers better understand and evaluate a full range of possible outcomes by acknowledging that the future is variable and presenting a range of likely scenarios.
Too often, customer data split across different data sources and storage systems fails to offer a comprehensive picture of who your customers are and what they most need or expect from your company or your products. Implementing a customer 360 initiative that consolidates and harmonizes customer data in a single platform for holistic insights helps manufacturers drive growth by offering deeper insights into customer habits, wants, and opportunities.
Using a customer 360 platform can help manufacturers produce high-quality products by identifying what matters most to customers and offering increased personalization by identifying product opportunities for distinct customer personas. Several key metrics need to be tracked to measure the effectiveness of customer-centric efforts and enable these growth opportunities. This includes tracking and analyzing purchasing behaviors, maintenance requests, returns, and customer feedback.
Tracking manufacturing metrics is critical for achieving profitability, sustainability, and growth goals. To effectively monitor these initiatives and the associated metrics, it is essential to establish a robust data strategy and architecture and build a foundation for advanced analytics capabilities like AI and predictive analytics.
One common concern among manufacturers regarding off-the-shelf data and analytics solutions is their limited transparency. These solutions offer little visibility into their construction and functioning, which can raise concerns about the reliability of results and provide almost no flexibility.
This is where customized solutions shine. Wavicle’s data and analytics experts can help you design, develop, and implement tailored solutions that align with your specific business requirements, instilling trust and confidence in the process.
Ready to start your journey to metrics-driven manufacturing? Reach out today to learn how Wavicle can help you access data-driven insights designed to meet your specific goals.