Predictive analytics in Manufacturing
Data Analytics / February, 09 2023

Predictive Analytics in Manufacturing- Use Cases and Benefits

How do manufacturers stay agile in a post-pandemic world? What are the methods they use to cope with a 40% surge in demand within six weeks? What steps do you take to accelerate efficiency, reliability, and agility in manufacturing operations? The manufacturing industry has always relied on automation to boost productivity and efficiency. With the advent of Industry 4.0, the integration of advanced technologies such as Artificial Intelligence and data analytics has further enabled optimizing the processes. In this blog, we will explore how predictive analytics in manufacturing can significantly impact and augment the processes to make informed decisions, optimize processes and increase efficiency.

Digital transformation in manufacturing

According to Gartner, combining predictive and prescriptive capacities will benefit enterprises and industries by solving business problems and deriving intelligent decisions. Let us find out how manufacturers can use predictive analytics to gain a competitive edge. It may have been a common sight at many companies to see technicians in a manufacturing unit walking around and manually checking the machinery and its parts, filling out forms, note down the operation and maintenance history for the machines in the plant. These methods are tedious and prone to error, and the data can also be inaccurate. With the onset of Industry 4.0, manufacturers are readily switching to sensors, connected equipment, and operations, which provide better data than manual data collection and provide it in real time. Software and connected devices reduce labour and magnify the power of predictive analytics, leading to accurate and better results.

What is Predictive Analytics?

Predictive Analytics is a branch of advanced analytics that is used to make predictions about future events based on historical data, machine learning algorithms, and statistical models to identify the likelihood of future outcomes based on specific variables. Not only the analysis is data-driven and logical, the application of predictive analytics in various domains, including the manufacturing sector, can help to improve decision-making and understand the relationships between the variables.

Use cases of predictive analytics in manufacturing:

  • Predictive Maintenance: 

Machines and equipment play a huge role in manufacturing. Without maintenance, it is evident that any machine will eventually break down. Thus, manufacturers follow maintenance programs so that operations run smoothly, reduce costs, and make the process reliable. Corrective and even preventive maintenance may cost heavily in terms of expenses, time consumption, downtime, and safety concerns. 

With the advent of AI (Artificial Intelligence), the latest way of revolutionizing maintenance in manufacturing is Predictive maintenance. It encompasses using data analytics to identify and predict equipment failures. It aims to predict adverse events in the future and allows to schedule maintenance in a much better way.

Predictive analytics can use the cumulative data from the real-time sensors present on the machines to predict when they require service or replacement. AI algorithms can also evaluate data from other sources and identify the trends and patterns that hint towards potential problems.

  • Demand forecasting:

For manufacturers, it is necessary to predict future demands to manage the costs appropriately. One instance is predicting the demand according to the seasonality of the consumer goods- such as warm winter-wear clothes during winter. Simply, considering the history of demands along with high-impact indicators, manufacturers calculate a lot of variabilities and can plan capital expenditures or temporary shutting downs.

With the help of predictive analytics, manufacturers can analyse the history of demands. The analysis can provide valuable insight into consumer buying habits, availability of raw materials, impacts of a trade war, supplier issues, shipping barriers, and other hidden disruptions. Predictive analytics can also establish connections between the various key factors and variables influencing demand. This information allows manufacturers to have effective strategies to equip the supply management and gear up according to the demand.

  • Improve KPI Analytics for workforce management:

With a competitive labour market, a shortage of skilled professionals, workforce management is crucial for the endurance of the manufacturing business. Moreover, fluctuating consumer demands, equipment downtime, and several other factors waver employee productivity. According to the Bureau of Labor Statistics data, the annual total separations have been rising in the manufacturing sector industry year after year. Thus, manufacturing organisations need to predict staffing, scheduling, training, and productivity challenges in order to tackle workforce management obstacles.

By analysing data from a variety of sources, manufacturing industries can gain deep insights into their workforce and gauge the following areas:

  1. Consumer demands
  2. Industry hiring trends
  3. Employee productivity
  4. Safety incidents
  5. Employee engagement
  6. Contract negotiations
  7. Seasonal leave usage
  8. Key performance indicators by the employee

Using predictive analytics, the organisation can harness all this data to predict:

  1. The right workforce balance- whether full-time or contract-based.
  2. Handle attrition by predicting which employees are on the verge of leaving.
  • Enhance manufacturing execution systems:

Indeed, the absolute transformation of raw materials into finished products is much more challenging than what most manufacturers recognize. Raw materials, machinery/components, supply costs have a direct relation with profits. Subsequently, these prime factors fluctuate due to reasons like availability of materials, seasonality, shipping locations, global demand and much more. 

46.4% of manufacturers bear the rise in raw materials costs as a primary challenge. The sudden increase in raw material prices stretches the margins and compels many manufacturers to revise pricing. Using predictive analytics, organisations can analyse the data and foresee major shifts in raw material costs. This knowledge will enable them to predict volatility and revise their plans. Additionally, apart from material costs, predictive analytics can improve manufacturing execution in the following ways:

  1. Identify cost drivers
  2. Pinpoint bottlenecks
  3. Finetune the control loops to improve efficiency and profitability.
  4. Manage supply chains
  5. Determine accurate timelines

Benefits of Predictive Analytics in Manufacturing:

Predictive analytics can assist manufacturers in several ways to optimize their processes and productivity. Here are the benefits:

  • Improved efficiency: Predictive analytics can help manufacturers identify areas for improvement and optimize processes, leading to increased efficiency.
  • Cost savings: Predictive analytics can help manufacturers reduce costs and improve profitability by helping them make better decisions.
  • Increased production: By providing accurate predictions on future production levels, manufacturers can allocate resources effectively and increase production.
  • Improved quality: Predictive analytics can identify quality issues in real-time and support manufacturers in improving product quality and reducing defects. 
  • Reduced downtime: Scheduling maintenance of machinery and equipment based on predictive analytics’ prediction of when equipment is likely to fail or err; can augment production and cost effectivity by reducing downtime.
  • Better decision making: Valuable insights enable data-driven decisions and respond to changing market conditions. Predictive analytics allows manufacturers to weigh the pros and cons well and drive better decisions.
  • Increased customer satisfaction: By providing a slew of benefits such as improved product quality, reduced defects, product availability, and much more, predictive analytics increases customer satisfaction.

Predictive analytics in manufacturing can provide a competitive advantage and support long-term success through its application. It helps you face the ever-changing market, consumer demands, equipment management, supply chain optimization, and more.

How can we help?

As a manufacturer, are you looking for a technology partner to help you with the mentioned features? At Saxon, we help enterprises on their journey to optimize their organizational structure with tailored solutions and services. Get in touch with us to start your journey toward Predictive Analytics today.

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