How to Improve a Production Schedule Using Data Science 

Posted by Yash Puranik

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.  

Importance of Data in Production Scheduling 

“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.  

Improving Production Schedules with Data Science 

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: 

  • Improve forecasts: Predict customer demand and raw material availability with higher accuracy using statistical time series analysis or deep supervised learning methods. Better inputs to a production scheduling system lead to better outputs.
  • Predict Resource availability: Estimate when equipment may need maintenance by modeling equipment health and expected maintenance downtimes using time-to-event analysis. By planning for the availability or unavailability of machines, you can reduce the disruption to production schedules allowing for more optimal use of available resources.  
  • Characterize uncertainty: Understand how variability in your manufacturing processes affects production outcomes. Manufacturing processes often face inherent uncertainty in critical attributes such as processing times. Methods such as discrete event simulations can help production schedulers understand this uncertainty and carry out what-if scenario analysis. These scenarios will help to generate schedules that are feasible in a variety of uncertain process conditions. 

Machine Learning and AI-based Methods

Machine learning and artificial intelligence-based methods can improve the decision-making algorithms employed by a production scheduling system.  

  • Reduce computational time: Generate good schedules significantly faster in production than the time taken by mathematical optimization methods. Mathematical optimization methods attempt to find the true optimal solution at the cost of significant computational runtime. In contrast, ML/AI-based methods can generate solutions much faster with the accepted risk of slightly less optimal solutions.  
  • Efficiently reschedule under uncertainty: Adapt your schedules when faced with disruptions in an uncertain world using machine learning. A static manufacturing schedule is rarely preferable. With ML, manufacturers can respond to disturbances and adapt production schedules to support efficient operations.  

A Scheduling Solution for a North American Steel Manufacturer 

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%. 

Improve Production with Aimpoint Digital

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.  


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