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Optimization & Simulation
A manufacturing production schedule system answers a series of important questions:
The answers to these questions generate a recipe for manufacturing operations and determine the allocation of machines and labor. A production scheduling system can be based on simple rules-of-thumb, operator-insights, data science, machine/reinforcement learning, or mathematical optimization.
“Garbage in, garbage out” is a common phrase to describe what happens when your input data quality is not good. Whether your production scheduling system is based on heuristics or mathematical optimization, the quality of the inputs the system receives will determine its success. A production scheduling system that uses inaccurate inputs is bound to produce suboptimal schedules or potentially unimplementable ones due to violation of production constraints.
As an example, consider demand forecasts. We need demand forecasts for production scheduling systems to decide the number of goods we need to manufacture. Simple heuristics such as historical averaging demand over the past few years in the same quarter can lead to inaccurate forecasts. This can lead to lost revenue with unmet sales or excessive inventory costs. Instead, a more accurate demand forecast that accounts for seasonal, cyclic, and trend patterns in data can lead to more accurate forecasts and minimize the risk of insufficient production or excess inventory.
Integrating DS methods can help solidify your inputs to generate better results in often less time than traditional methods. Machine learning and artificial intelligence algorithms can often generate production schedules extremely fast with only a small decrease in solution quality. Below, we describe how data science can augment production scheduling systems:
Machine learning and artificial intelligence-based methods can improve the decision-making algorithms employed by a production scheduling system.
The steel manufacturing client faced challenges in developing optimal schedules for its manufacturing processes. One of the key issues was that the processing time for a batch was uncertain and varied depending on the grade being manufactured and other operational and environmental factors. Using discrete event simulations, our team accurately characterized uncertainty in the processing times. Armed with this information, our team generated optimal production schedules projected to improve the client’s throughput by up to 25%.
At Aimpoint Digital, we can help you with all stages of your production scheduling journey. Our team of data engineers can set up optimal data pipelines and storage for you to maximize the value your data can provide you at the lowest cost. Our team of analytics experts can process the data to identify the bottlenecks and pain points in your production processes. While, our team of data science, machine learning, and optimization experts can develop advanced decision-making tools to provide you with optimal scheduling decisions.
Contact us to optimize your production schedule processes.
Whether you need advanced AI solutions, strategic data expertise, or tailored insights, our team is here to help.