Boosting Throughput: Using AI/ML to Optimize Production Lines

In today’s competitive manufacturing landscape, even small gains in production efficiency can lead to significant cost savings. Traditional optimization methods often fall short in environments with complex machine interactions and nonlinear behaviors. Enter boosting regression models—a modern AI approach that learns from data to pinpoint and optimize the factors that matter most.

What Are Boosting Regression Models?

Unlike linear models, boosting regression models are a type of ensemble machine learning method used for predictive modeling, especially in situations where relationships between inputs and outputs are complex, nonlinear, or affected by interactions among variables. In the context of production lines, boosting models can predict key outcomes like throughput, cycle time, or defect rate based on real-time machine and process data.

Boosting works by combining many weak learners, typically simple decision trees into a strong learner. Each tree is trained in sequence, where each subsequent model attempts to correct the errors made by the previous one. This process continues iteratively, improving accuracy step by step. Similar to a team of experts reviewing and refining a strategy.

Descriptive Analytics

Boosting regression models provide useful descriptive insights. A key descriptive is feature importance. Meaning which input variables have the greatest impact on predicted outcome. This includes which unit operations have the greatest impact on overall line performance and even which KPI’s within those operations have the greatest influence.

In one real world example, there were two series machines on the line: a bundler and a tray packer. The tray packer’s overall KPI’s were lower than the bundler and the root cause algorithm cited the tray packer significantly more often. However, the model ranked the bundler as the higher impact machine. On further analysis, the bundler had more frequent short stops and a high variance in stop frequency which degraded the accumulation upstream of the pair. Although it rarely stopped the next upstream machine, the degraded accumulation time caused cascaded stops when the tray packer incurred longer stops.

Prescriptive/Predictive Analytics

Boosting regression models provide prescriptive/predictive analytics which guide decision-making by suggesting specific actions or optimal settings based on model predictions. These include “what-if” scenario analysis. For example, test how changing machine KPI’s would affect line throughput.

In the example above, different scenarios were tested. Reducing the frequency of stops on the bundler (increasing MTBF) showed a significant impact on the line performance. However, this was difficult to accomplish in practice since much of the variability was due to the material quality and other factors. However, when the scenario of increasing the rate of the machine was tested it also showed a significant impact. With some tuning, the rate of the bundler was increased creating a measurable lift in line output.

Using an Optimization Algorithm

Boosting regression models can handle “what-if” scenario analysis but this can be a cumbersome process as it involves human guesswork. However, a boosting regression model can be combined with an optimization algorithm such as Bayesian Optimization to automate the process to achieve a specific goal or goals.

For example, a Bayesian Optimization can be used to determine the optimal rates for machines on a line. The model is given a goal which is to maximize the line output and instructed that the machine rate inputs are variable. The algorithm will then experiment with different scenarios autonomously until it finds the optimal line output and which input values facilitate that output.

Another example is optimizing a production schedule to minimize changeover times. A boosting regression model can be built using changeover data either using SKU-SKU or by using SKU characteristics. The model is given a goal of minimizing the total time of a set of orders. The algorithm will then experiment with different sequences autonomously until it finds the optimal sequence.

Using Generative AI to Deploy and Utilize Models

One of the traditional barriers to using boosting regression models is the time and knowledge required to build effective models as well as interpreting them. However, GenAI can help users build boosting regression models by guiding them through each step of the process from data preparation to model evaluation and optimization. It can provide code examples, help define the target variable, and assist in splitting the data for training, validation, and testing to ensure the model generalizes well.

GenAI can also support users in improving performance through Bayesian Optimization, interpreting model outputs, and translating predictions into actionable insights. It can build pipelines, embed models into prescriptive analytics workflows, and generally serve as an end-end collaborator in deploying models for real-world impact.

What Kind of Data is Needed?

There is some flexibility, but the ideal data input is “utilization” data which is sequential event data detailing the changes in machine state such as transitions from running to non-running states such as blocked, starved, failed, etc. This data stream should also include production counts and optionally defect counts. It should also include categorical variables such as SKU, Shift, etc.

Quality over quantity. Depending on the goals, as little as a few weeks of data can train an effective model. In fact, using too much data can be detrimental to model integrity. Things change over time. Machine upgrades, changes in material suppliers, etc. over time can invalidate older data. For production output optimization, the ideal frame of data is between 2 and 6 months.

The importance of quality data cannot be understated. Since boosting focuses on correcting previous errors, it can overemphasize outliers or erroneous values. Without proper cleaning or filtering, a few bad data points can skew the model’s learning process. The cleaning process should only remove a small percentage of samples. If too many samples are removed in the cleaning process, the integrity of the model is compromised.

Starting with feature– rich, high fidelity utilization data stream is the key to producing effective models quickly.

How to get Started

The first step is always understanding your organization’s goals. Increasing productivity, reducing scrap/waste, improving product quality, etc. Then conduct a data readiness assessment. Do we have the data needed? Or how do we get the data we need?

SmartSights can provide the tools and knowledge to help manufacturers become data ready and assist them in transforming data into effective models to achieve their goals. We deliver small scale solutions directly and more complex comprehensive solutions via our Systems Integrator partners.