How to Increase Plant Productivity Using Data Analytics

Posted by Nem Kosovalic

Plant productivity is about manufacturing quality products efficiently to maximize the throughput of the facility. Whether the plant makes intermediary products or finished goods, the push is often to meet consumer demand. If a manufacturer cannot meet demand, they will fail to capture revenue which may go directly to competitors. Fortunately, there are a variety of methods from data analytics one can leverage to increase plant productivity. Such methods include dashboards, machine learning, statistics, optimization, and simulation. Using data analytics to increase plant productivity either maximizes revenue or improves quality by exploiting data to inform business decisions. 

Why is Plant Productivity Important?

Plant productivity is the output a plant can generate in some window of time. The more output generated, the higher the productivity is. Naturally, the increase in the productiveness of the plant directly affects revenue. Unplanned downtime, or idle machines, is estimated to cost industrial manufacturers 50 billion USD each year. Thus, poor machine allocation, or inefficient use of machines, also directly impacts throughput, and therefore, revenue.

How can Data Analytics Help?

Increasing productivity through analytics requires a sound strategy based on situational analysis of the plant. Gather and validate data, test theories, implement solutions, and monitor changes over time to reap the benefits of plant data analytics.

Minimize risk of inventory shortage or raw material shortage with interactive dashboards:

  • Visualization techniques allow you to quickly ascertain which inventory thresholds or raw material deliveries are at risk. Allow operations to make proactive changes based on your data to keep production flowing and positively impact your bottom line. 

Minimize delays in production related to late supplier shipments via Machine Learning: 

  • Late supplier shipments reduce inventory and slow production, as the plant cannot use materials if they are unavailable. Use machine learning to predict which shipments may be delayed before it is too late. Visualize and identify the right levers to pull for intervention using model interpretability tools. 

Plan for possible machine failure: 

  • Predictive analytics coupled with robust data can determine potential machine downtime. Use analytics to reduce the impact on production via various methods, including survival analysis. By accurately predicting when a machine will fail, reduce the effect by intervening preemptively.  

Increase throughput effectively with optimized schedules: 

  • Generate an optimal schedule that considers competing objectives to produce efficiently. A schedule that minimizes cost while maximizing throughput will improve productivity using either linear or mixed-integer programming. 

Identify Bottlenecks and Simulate “What-If” Scenarios: 

  • Identify bottlenecks in the facility with simulation techniques. Once identified, simulate potential solutions before rollout – test before you implement. Interfering with plant operations in real-time is both costly and time-consuming. By using advanced techniques, simulations walk through various “what-if” scenarios in less time. Without any interference with day-to-day operations, the potential solution can be evaluated with minimal risk.  

Aimpoint Digital: A Proven Track Record of Increasing Plant Productivity 

The Aimpoint Digital team has worked extensively with manufacturers using data analytics to increase their plant productivity. Most recently, for a large US manufacturing client, our team utilized advanced techniques to increase throughput to assist the manufacturer in meeting consumer demand. The high number of machine instances and hundreds of product lines created a complex problem to solve. We set up a discrete event simulation tracking each machine instance and product line combination to explore various “What-If” scenarios. Our solution also prescribed optimal strategies for the manufacturer, which resulted in the machine learning solution generating explanatory insights of variables affecting processing times tailored to each machine. Overall, our solution improved the client’s initial baseline model by 25%, generating millions in revenue.

Aimpoint Digital Will Maximize Your Plant’s Productivity 

The Aimpoint Digital Team has a proven track record of using cutting-edge tools from data analytics, including dashboards, machine learning, statistics, optimization, and simulation to maximize plant productivity and exceed expectations. 

Contact us to increase your plant’s productivity.  

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