AI-predictive analytics is no longer a dream. It’s here, it’s real and proven, and the manufacturers who adopt it thoughtfully are pulling ahead fast. What was once reserved for the biggest, most sophisticated operations is now within reach of almost any plant that’s serious about operational excellence. The best part? It doesn’t require ripping and replacing existing systems. It’s about layering intelligence onto the data and equipment you already have.
Predictive analytics works because it does what humans can’t: it sifts through thousands of variables in real time, identifies subtle patterns, and forecasts issues before they happen. Not in a vague way — in ways that directly impact uptime, throughput, and first-pass quality.
Manufacturers who are embedding AI into daily operations are seeing 10–20% increases in uptime, 5–10% improvements in throughput, and major gains in product quality — usually within the first six to twelve months. They aren’t waiting until problems appear on the plant floor. They’re seeing the future a few hours, days, or weeks ahead and making the right moves early.
Predictive AI is now a core manufacturing advantage. The simple reality is this: the manufacturers who treat AI-predictive analytics as an operations tool, not an IT experiment, are building a performance advantage their competitors will struggle to match. This guide shows how you can do the same — practically, profitably, and starting right now.
How Predictive Analytics Actually Drives Uptime, Throughput, and Quality
Predictive analytics is powerful because it attacks the real bottlenecks that slow manufacturers down — equipment downtime, production delays, and quality failures — before they spiral into bigger problems.
When it comes to uptime, predictive analytics uses data from sensors, PLCs, and SCADA systems to monitor the health of machines in real time. It identifies patterns humans usually miss, like a slight increase in vibration on a motor or a heat spike that’s statistically linked to bearing wear. Instead of relying on fixed maintenance schedules or reacting after a failure, manufacturers can fix issues days or even weeks before an asset actually breaks down.
In one hypothetical example, imagine a packaging line where a conveyor motor typically fails every 18 months. By analyzing historical vibration and current performance data, a predictive model flags a 70% chance of failure within 45 days. Maintenance swaps the motor during a planned weekend downtime, avoiding a three-day unplanned outage that would have cost $250,000 in lost production.
For throughput, AI looks beyond equipment failure and into production flow. It tracks real-time line speeds, work-in-progress inventory, operator inputs, and downstream demand. When a bottleneck starts forming — maybe due to slower-than-normal operation of a filling station — AI models can recommend corrective action before queues build up and output drops.
A manufacturer running multiple SKUs on the same line might use predictive analytics to dynamically adjust run schedules based on minor equipment slowdowns, avoiding ripple effects that would have otherwise cut daily output by 8–10%.
Quality improvements happen because predictive analytics doesn’t just look at finished products; it monitors the process parameters that lead to good or bad quality. It learns that a certain mold temperature, when combined with ambient humidity above a threshold, tends to increase defect rates by 15%.
Instead of discovering a quality problem after 500 units are built, predictive analytics recommends real-time adjustments — and defects are caught before they leave the machine. For example, a hypothetical injection molding plant could reduce scrap rates from 5% to 2% simply by using AI to monitor resin moisture content and automatically adjust drying times.
The bottom line is simple: predictive analytics helps manufacturers stop reacting and start anticipating. It shifts operational strategy from firefighting mode to foresight mode — and that shift alone can transform a plant’s performance metrics faster than almost any other single investment.
5 Practical Use Cases You Can Start With Right Now
The power of AI-predictive analytics is that you don’t need a massive, years-long rollout to start seeing value. Some of the highest-ROI applications are also the most accessible. Here are five practical use cases that manufacturing leaders can put into action immediately.
Predictive maintenance for critical equipment is the most obvious — and one of the fastest to deliver returns. Start by identifying your most critical assets: the handful of machines where unplanned downtime would cause the most damage to output and margins. Predictive models can monitor sensor data like vibration, temperature, and cycle times to forecast failures days or weeks before they happen. For example, a hypothetical food manufacturer installs simple vibration sensors on its mixers and cooling systems. Within six months, they avoid two major failures that would have shut down production for a week each, preserving over $1 million in product shipments.
Dynamic production scheduling is another powerful move. Traditional schedules assume everything runs as planned — but in reality, small disruptions constantly ripple through production. AI models can recommend real-time schedule adjustments based on equipment performance, labor availability, and even weather impacts. Imagine a plant where one of three packaging lines starts running 15% slower due to a mechanical issue. Instead of running late and creating overtime costs, dynamic scheduling algorithms reassign SKUs across lines on the fly, keeping shipments on time without additional labor costs.
Process parameter optimization uses AI to monitor and adjust critical production variables in real time. In one hypothetical metals plant, predictive analytics tracks furnace temperatures, feedstock quality, and throughput speed. When it detects a risk that high throughput combined with slight underheating could cause out-of-spec products, it automatically recommends adjustments. Over a year, a plant can improve first-pass quality yields by 8-15%, while reducing energy consumption.
Predictive quality control goes even deeper. Instead of waiting for traditional quality inspections to catch defects, predictive models monitor inputs and process trends to detect when the risk of defects is rising. For instance, a plastics manufacturer could use predictive analytics to identify when minor fluctuations in injection pressure predict higher short-shot rates. Catching this earlier can prevent hundreds of thousands of dollars in scrap, rework, and warranty claims.
Energy consumption forecasting may not sound as urgent, but it’s becoming a competitive weapon. Predictive models can identify patterns of unnecessary energy use across shifts, lines, and plants. In one hypothetical case, a beverage manufacturer uses predictive analytics to forecast energy demand based on production mix, weather, and shift patterns. By adjusting chiller operations proactively, they cut energy costs by 7% annually without impacting production targets — while also improving ESG performance for customers and regulators.
The big insight here is that you don’t have to “go big” right away. Pick one or two of these use cases, focus tightly, and drive success you can measure. Momentum beats scale at this stage. A single early win builds the internal credibility you need to expand AI’s role across the entire plant or network.
Common Pitfalls to Avoid When Rolling Out Predictive Analytics
While predictive analytics is powerful, how you implement it matters just as much as what you implement. Many manufacturers stumble not because the technology doesn’t work, but because the rollout strategy is flawed. Knowing where others trip up gives you a serious advantage.
The first major pitfall is starting too big. Ambition is good, but trying to apply predictive analytics across every machine, every process, and every line at once is a guaranteed way to create confusion, delays, and frustration. Predictive analytics thrives when it’s introduced to a specific, high-impact area first. For example, a manufacturer might focus initially on predictive maintenance for its bottleneck equipment — say, a critical press or extrusion line — before expanding plant-wide. By starting small, you create a proof point, you learn operational lessons, and you build momentum.
Another common mistake is ignoring frontline operators. AI models can predict a lot, but they can’t replace the practical wisdom of the people who work with the machines every day. Operators often know subtle signs of trouble that sensors and algorithms don’t capture — a slight smell, a sound, a vibration that’s just “off.” Smart manufacturers pair predictive analytics with frontline insights. In one hypothetical case, a specialty chemicals plant included operators in the development of its predictive models, leading to a 20% improvement in predictive accuracy because the models were tuned to real-world conditions, not just idealized data.
Failing to link predictions to action is another critical trap. It’s one thing to predict that a compressor is likely to fail in the next 10 days. It’s another to have a clear, fast workflow in place that triggers maintenance, secures parts, and adjusts production schedules if needed. Predictions only create value when they’re tied directly to decision-making processes. Without that link, even the best AI becomes little more than an expensive dashboard.
Poor data quality is another silent killer. Predictive analytics isn’t magic — it needs clean, reliable input data. Dirty data, missing fields, or inconsistent sensor readings can quietly poison even the best algorithms. In practice, you don’t need perfect data to start, but you do need good enough data, with processes in place to steadily improve it. Smart manufacturers treat data hygiene as a living, ongoing operational discipline — just like preventive maintenance or lean initiatives.
The real takeaway here is simple: success in predictive analytics is mostly an operational challenge, not a technical one. Start focused. Build credibility with frontline teams. Make predictions actionable. Invest steadily in better data practices. Manufacturers who master these basics consistently unlock faster, bigger returns — while others stall out chasing perfect technology or grandiose plans.
Key Data and Infrastructure You Need (and Don’t Need) to Start
One of the biggest myths about predictive analytics in manufacturing is that you need a perfect data lake, a brand-new MES, or a full digital twin model before you can start. You don’t. What you do need is much simpler — and much more practical.
At minimum, you need consistent, time-stamped operational data. This typically comes from sources you already have: sensors, PLCs, SCADA systems, historian databases. You don’t need every data point imaginable. Focus on the operational parameters that actually drive uptime, throughput, and quality: temperatures, pressures, vibration data, run rates, downtime events, scrap counts, energy usage. A medium-sized packaging plant, for instance, successfully launched predictive maintenance analytics just by tapping into three data points per machine — motor vibration, current draw, and temperature — streamed from existing PLCs. No massive upgrade required.
You also need basic connectivity to pull this data into an analytics platform or environment. Again, this doesn’t have to be a full Industry 4.0 stack. In many cases, lightweight middleware can pull data from machines into a cloud-based system or even a robust on-premise server. If you can get real-time or near-real-time data from your assets, you’re 80% of the way there.
What you absolutely don’t need to start is perfect data quality across the board. Early pilots can handle some missing values, occasional noise, and less-than-ideal tagging — as long as the core data streams are reliable enough to surface useful patterns. What matters more is building a process for gradual data improvement: start by cleaning and standardizing data for your initial use cases, then expand your efforts as you prove ROI.
Another thing you don’t need is a total IT overhaul. Many predictive analytics tools today are modular and API-driven, designed to integrate with existing MES, ERP, or maintenance systems without replacing them. In one hypothetical case, a mid-sized discrete manufacturer began its predictive journey by layering a predictive platform onto its 15-year-old MES. They improved critical asset uptime by 12% in the first nine months without touching the underlying system.
The key insight here: perfection is the enemy of progress. You can achieve serious operational gains by starting with the data and systems you already have, cleaned up just enough to be useful, supported by workflows that act on insights quickly. The sophistication can grow over time — but real-world impact starts with simplicity and focus.
How to Build a Pilot That Proves Real Business Value
Building a successful pilot is crucial in AI-predictive analytics, especially if you’re trying to demonstrate real business value quickly. A well-structured pilot program serves two purposes: it generates tangible ROI and lays the groundwork for broader adoption. The key to success lies in keeping it focused, measurable, and tied to business goals — not just technological outcomes.
The first step in designing a solid pilot is to define clear, measurable KPIs. Start with the problem you’re solving. Is it reducing downtime? Increasing throughput? Cutting defects? Then, work backwards to identify how success will be measured. For example, a manufacturer might set a target of reducing downtime for a critical piece of equipment by 20% within six months. Or, they could aim for a 5% improvement in first-pass yield across a specific production line. Make sure these KPIs are quantifiable and aligned with your broader operational goals. Without clear metrics, the pilot will be hard to evaluate, and executives will struggle to see the value.
Next, pick a manageable scope for the pilot. Don’t try to tackle the entire plant at once. Focus on one key area that has a measurable impact and is relatively contained. For example, a company might begin with predictive maintenance on a high-cost, high-importance machine — say, a CNC machine or a bottling line. This allows for fast, clear results. It’s easier to show ROI with a single line or a small subset of machines before scaling up to the full plant.
It’s also essential to select the right data sources for the pilot. In most cases, you’ll already have access to enough data from existing sensors or PLCs. If not, consider implementing inexpensive IoT sensors to collect the data you need. Make sure the data is rich enough to provide insights — but remember, you don’t need a massive overhaul of your IT infrastructure. It’s more about leveraging the data that’s already there.
When setting up the pilot, integrate it with existing workflows. AI isn’t a replacement for human oversight; it’s a tool that should fit seamlessly into current processes. The predictive model will generate forecasts, but it’s the operators, engineers, and maintenance teams who will make the final call on action. Ensure that the results from your AI model are actionable and that operators have clear guidance on what to do with the insights. For example, predictive maintenance alerts could prompt a technician to conduct a specific check or repair during scheduled downtime.
Lastly, track both hard and soft metrics. Hard metrics include things like uptime percentages, throughput rates, and defect counts. Soft metrics, though, can include improvements in team confidence, reduced stress from last-minute breakdowns, or even better communication between operators and maintenance staff. Don’t underestimate the value of these softer outcomes; they can be just as important in proving the value of predictive analytics, especially as you scale it to larger operations.
A successful pilot often leads to a smooth transition to a full deployment. In one hypothetical case, a manufacturer in the automotive parts industry started by focusing on predictive maintenance for three key presses on one production line. After six months, they reduced downtime by 18% and improved throughput by 8%. With clear ROI demonstrated, they expanded the program plant-wide, gradually adding other lines and refining their models. This staged approach enabled them to refine processes, tweak data collection, and iterate on the AI model before making large-scale investments.
The bottom line is this: a successful AI-predictive analytics pilot isn’t just about demonstrating cool tech. It’s about solving a real, measurable problem that ties directly to business goals. Keep it focused, keep it practical, and keep it actionable. Once you prove value in one area, you’ll have the momentum and confidence to expand further.
The ROI of Predictive Analytics in Manufacturing: What You Can Expect
Understanding the return on investment (ROI) for predictive analytics in manufacturing can be tricky, especially in the early stages when results are still building. However, the good news is that once you start measuring the impact, the numbers can be striking. Predictive analytics provides both direct and indirect returns, and both should be considered when calculating ROI.
The most straightforward direct ROI comes from reduced downtime. Predictive maintenance alone can generate significant savings by preventing unplanned failures, which are among the most costly operational disruptions. For example, one hypothetical auto parts manufacturer used predictive analytics to monitor vibration and heat data from its stamping machines. By predicting failures before they happened, they cut downtime by 25%, which saved them over $500,000 in lost production time over the course of a year. In high-volume, high-cost plants, this type of ROI can quickly justify the initial investment in AI and sensors.
Beyond downtime, increased throughput is another significant area where AI can impact ROI. Predictive analytics allows manufacturers to continuously optimize production schedules, equipment utilization, and even energy consumption. By minimizing bottlenecks before they become critical, throughput increases. Consider a packaging facility that used AI to optimize the speed and flow of machines based on predictive data. The result was a 10% increase in daily throughput without adding extra shifts or overtime. This led to an additional $1.2 million in revenue annually — a clear ROI on their AI investment.
Quality improvements also lead to substantial ROI, particularly in industries where defect rates have a large impact on margins. Predictive analytics allows manufacturers to continuously monitor and fine-tune process variables that impact quality. In a hypothetical case, a semiconductor manufacturer applied predictive analytics to monitor temperature fluctuations in its clean room environments. By adjusting parameters in real time based on AI insights, they improved yield by 12%, avoiding thousands of defective units that would have otherwise been scrapped. In this case, predictive analytics turned a quality improvement into a direct savings, making the ROI measurable in terms of cost reductions and improved revenue.
Beyond direct financial savings, indirect ROI comes from factors like improved employee morale and increased operational flexibility. Predictive analytics gives workers and managers a more proactive approach to problem-solving, which not only leads to smoother day-to-day operations but also boosts team confidence. When employees know that predictive tools are helping prevent surprises, it reduces stress and enables faster decision-making. In the long run, this leads to lower turnover and higher employee satisfaction — intangible but powerful benefits.
Another indirect benefit is improved customer satisfaction. By using predictive analytics to ensure consistent uptime and higher quality, manufacturers can consistently meet or exceed delivery deadlines, maintain product quality, and respond faster to customer needs. For example, a manufacturer that can deliver more consistent product quality and on-time shipments can charge a premium for reliability — and customers will likely be more willing to renew contracts or increase their orders based on this dependable service.
What’s crucial to remember is that the ROI of predictive analytics often compounds over time. The more you integrate AI into operations, the more the system learns, improves, and fine-tunes itself. What might start as a few thousand dollars saved in year one can quickly balloon as models become more sophisticated and actionable. The 18% downtime reduction seen in the first six months of a pilot project can continue to improve year after year as the system learns to identify new failure modes and optimize processes even further.
One important final point: don’t be discouraged if the ROI isn’t immediately overwhelming in the first few months. While predictive maintenance and optimization deliver fast wins, the broader organizational benefits — like better decision-making, more empowered employees, and more responsive customer service — take longer to fully materialize but are equally important when assessing overall ROI.
How to Scale Predictive Analytics Across the Entire Organization
Once you’ve successfully implemented a predictive analytics pilot and demonstrated clear ROI, the natural next step is scaling the solution across the entire organization. Scaling, however, requires careful planning and a systematic approach to avoid overstretching resources and to ensure the technology delivers sustained value at a larger scale. Here’s how you can do it effectively.
First and foremost, standardize your data processes. As you move from pilot to full-scale implementation, you’ll encounter a broader range of data sources, equipment types, and plant locations. The key to scaling predictive analytics is to standardize the data collection and integration process. This might involve refining data pipelines, ensuring all assets are connected to the same predictive analytics platform, and standardizing the formats and tags used across machines.
For example, you may find that one plant is using a different protocol for sensor data than another, which could slow integration. To overcome this, start by creating a unified data strategy, aligning on the same KPIs, sensor types, and data points across the board. This ensures that no matter where you are scaling the solution, the insights will be comparable and actionable.
Second, automate insights and actions. One of the limitations of a smaller pilot is that it often relies on manual intervention for decision-making — a technician is alerted to an issue, and they manually adjust the equipment or the schedule. As you scale, however, you’ll want to automate more of these decisions, particularly in high-frequency environments like assembly lines or packaging. For example, AI could not only alert a technician that a piece of equipment is likely to fail but also automatically trigger a maintenance work order and reschedule production around the failure window. The more you automate, the faster and more efficient your plant will become, reducing the dependency on human intervention and driving faster reaction times.
Next, invest in training and change management. One of the most critical factors in scaling is ensuring that your employees at all levels are equipped to interact with the new technology. This means not only training technical teams to interpret predictive data but also educating operators, managers, and other stakeholders on how predictive insights fit into their day-to-day decision-making.
Change management is essential. Employees need to feel that the AI tools are there to support them, not replace them. In a hypothetical case, an automotive manufacturer scaled predictive analytics across several plants, offering comprehensive training to machine operators on how to respond to AI recommendations, improving plant efficiency without resistance. A dedicated support team helps guide employees through the transition, making them feel more empowered by the technology rather than intimidated.
Cross-functional collaboration is also crucial as you scale. Predictive analytics doesn’t work in a silo. It touches various departments — maintenance, operations, IT, and quality control, to name a few. Establishing a cross-functional team that collaborates and shares data can help ensure the successful expansion of predictive analytics. For instance, IT can ensure data integration and security, while operations teams focus on how to incorporate predictions into their day-to-day decision-making. This team can also monitor performance metrics across all plants and provide continuous feedback to improve the predictive models.
Another factor to consider as you scale is the evolution of the AI model itself. As you expand, you’ll need to continuously refine and retrain your predictive models to adapt to new equipment, product lines, and operational changes. The more you scale, the more data you’ll accumulate, which means your models can be retrained to become more accurate and sophisticated. Over time, your AI platform will be able to detect even more nuanced patterns, providing deeper insights and more advanced optimizations. Regular model updates — perhaps every quarter or after every significant change in the manufacturing process — will ensure that your models stay effective as conditions evolve.
Finally, ensure robust monitoring and reporting. As you scale, the volume of data and insights will grow exponentially, and you need a system to manage this growth. A centralized dashboard or monitoring system that tracks the performance of predictive models, equipment health, maintenance alerts, and operational KPIs is crucial. This gives leaders and decision-makers visibility into how predictive analytics is affecting plant-wide performance. It also allows teams to quickly spot any issues or areas for improvement. In practice, this might look like a real-time view into asset performance across all plants, with actionable recommendations for each site that can be reviewed and acted upon immediately.
In one example, a global manufacturer of electronic components successfully scaled predictive analytics from a single plant to a global network. Initially starting with predictive maintenance on a few key machines, they quickly expanded the program by integrating more sensors, adding quality control applications, and improving predictive models across all lines. By focusing on standardized data collection, employee buy-in, and automated workflows, they were able to reduce downtime by 30% globally, cut production bottlenecks, and improve product quality by 12%.
The key takeaway here is that scaling isn’t about pushing the same solution across more plants or lines. It’s about building a scalable foundation — data, processes, technology, and people — that allows the system to grow, adapt, and continuously deliver value. With careful planning, you can expand predictive analytics across your organization, unlocking new efficiencies and transforming how you operate.
What to Expect in the Future of Predictive Analytics in Manufacturing
The future of predictive analytics in manufacturing is incredibly promising, with technologies advancing rapidly and becoming even more integrated into the core operations of manufacturing plants. While the current applications already deliver substantial ROI, the next wave of innovations will take predictive capabilities to a whole new level, offering even more transformative possibilities for manufacturers.
One of the most exciting developments on the horizon is AI-driven autonomous manufacturing. As predictive analytics becomes more sophisticated, we will see a shift from predictive maintenance and process optimization to autonomous decision-making. Essentially, AI will not only predict failures or inefficiencies but will take immediate corrective action without human intervention.
For instance, if a piece of equipment is predicted to fail, the system could automatically reroute production, adjust machine settings, and even schedule preventative maintenance without requiring a technician to take action. This level of automation could significantly reduce operational costs, improve throughput, and minimize the impact of unplanned downtime — all while freeing up human resources for higher-value tasks.
Along with autonomous decision-making, edge computing will play a significant role in the future of predictive analytics. Currently, many predictive analytics solutions rely on cloud computing to analyze data and generate insights. However, with the rise of edge computing, manufacturers can process data closer to where it’s being generated — right on the shop floor. This results in faster decision-making, as data doesn’t need to travel to and from the cloud for analysis.
For example, a machine with an embedded edge device could instantly calculate whether it’s operating outside optimal parameters and take corrective action on the spot, all without waiting for cloud-based analysis. This will be especially valuable in environments that require rapid response times, such as high-speed production lines.
The integration of digital twins — virtual replicas of physical assets, processes, or systems — is another key trend that will revolutionize predictive analytics. While digital twins are still in their infancy in many industries, their potential for manufacturers is vast. With a digital twin of an entire production line or even an individual piece of equipment, predictive analytics can simulate a wide range of scenarios, testing different operational strategies before they are implemented in the real world. This not only enhances predictive accuracy but also allows for more efficient and risk-free decision-making, optimizing production processes and reducing costs.
Looking further ahead, machine learning models will continue to evolve and improve, allowing predictive systems to provide even more accurate insights and anticipate failures long before they occur. As more data is collected over time, the models will learn from past patterns, fine-tuning their predictions with greater precision. For instance, a machine may begin to predict not only when it will fail but also how that failure might impact the entire production line — enabling teams to adjust workflows or resources preemptively. This type of anticipatory analytics is expected to drive new levels of efficiency, cost savings, and quality control.
Another major trend is the increased integration of IoT (Internet of Things) and smart sensors. The future of predictive analytics will rely on a vast, interconnected network of sensors feeding real-time data into AI platforms. These sensors will continue to become more advanced, more affordable, and more ubiquitous across manufacturing environments.
With the ability to monitor everything from temperature and pressure to vibrations, humidity, and energy consumption, manufacturers will have a continuous stream of real-time data that can be leveraged for more accurate predictions. This increased connectivity will allow predictive systems to monitor not just individual machines but entire production systems, factory floors, and even supply chains, providing insights that were previously unimaginable.
Manufacturers will also see a growing focus on sustainability driven by predictive analytics. The ability to optimize energy consumption, reduce waste, and improve resource management is becoming increasingly important in today’s global economy. Predictive analytics will play a significant role in helping manufacturers achieve sustainability goals. For example, AI models can predict energy usage patterns and suggest ways to reduce consumption during peak hours, or they can identify inefficiencies in material use and suggest ways to cut down on scrap or waste. As regulations around sustainability tighten, predictive analytics will become an essential tool for manufacturers looking to improve their environmental footprint while maintaining profitability.
Finally, we can expect greater democratization of AI tools in the manufacturing sector. As AI becomes more accessible, smaller manufacturers will also be able to leverage predictive analytics. Tools that were once only available to large enterprises will become more affordable, user-friendly, and scalable. With intuitive interfaces and low-code/no-code platforms, even non-technical staff will be able to implement and use AI-powered tools, allowing manufacturers of all sizes to take advantage of the benefits of predictive analytics.
The future of predictive analytics in manufacturing is set to redefine the industry. As the technology advances, we can expect even more seamless integration with existing systems, more automation, and a greater ability to manage complex manufacturing environments in real-time. The key to success will be staying ahead of the curve by embracing these innovations and being willing to adapt as new technologies emerge.
In conclusion, predictive analytics is not a passing trend but rather the future of how manufacturers will operate, compete, and thrive. The journey may have started with small, focused projects aimed at improving uptime, throughput, and quality, but as AI continues to evolve, its applications will expand, delivering deeper insights and driving unprecedented operational improvements. By adopting predictive analytics today, manufacturers are laying the foundation for the future of manufacturing — one that is more efficient, more responsive, and more profitable than ever before.