Improving Productivity with Manufacturing Data Analysis

Process optimization in any business venture is vital for success. For instance, manufacturing executives face pressure to improve profits and reduce production time and cost. A manufacturing process can affect the outcome of any business, increasing the need for improved accuracy.

Improving productivity with manufacturing data analysis (MDA) is one way of optimization. It enhances quality, improved performance, cost reduction, increased yield, optimized supply chain, output, profitability, and process automation.

How MDA Works

This procedure follows the use of events-data, operations-data, and technology. Data analysis can be complex sometimes, but the ability to create, store and access large volumes of information has made it easy and achievable.

The analysis is made possible through the continued advancement in computing technology.

Advancement in computer or information technology helps many companies to capture real-time production data.  The information leads to logical visibility, reporting, and analysis.

Successful manufacturing data analysis relies on capturing the right information and understanding key processes such as the supply chain. Without correct data to solve productivity problems, the whole process becomes inefficient and riddled with unnecessary costs.

Benefits of MDA

Data analysis can solve different issues and improve the projected outcome of set targets. Below are some of the problems manufacturers seek to solve through this technique.

• Downtime reduction
• Predictive maintenance
• Scrap limitation

Productivity is improved by solving such problems, but it becomes exceedingly difficult without accurate manufacturing data analysis.  Capturing the correct information will always give manufacturing executives real-time contextual analysis.

MDA and Production

Detecting defective processes

In every production process, proper use of materials is required to prevent unprecedented costs. Manufacturers use data analytics to analyze raw materials making sure they meet production standards.

This helps detect faulty processes due to sub-standard materials.

Discovery of new information and patterns

Manufacturing data analysis helps manufacturers access resourceful information that

improves volume and consistency. It also helps identify new patterns that enhance production processes and increase supply chain efficiency.

Product scheduling and sales forecasting

Data analysis facilitates the ease of monitoring machines and their reliability. Such useful information is utilized in developing new propositions and reduce production costs. It assists in sales forecasts and effective production schedules.

Other ways companies use data analysis

• • Prediction of product preferences
  • Distribution and logistics optimization
  • Facilitation of data-driven decision making
  • Improvement of cash flow forecasting accuracy
  • Reduction of working capital and stock investment

Productivity will always be dependent on the constant development of new and successful ways of streamlining operations. In the past, manufacturers spent months analyzing and examining data to develop such productive ways.

Unfortunately, this outdated approach sunk or led to many companies’ deterioration. This regression was before making technological improvements and integrations in their analysis.

How MDA Improves Productivity

Maintenance Prediction

A proper manufacturing analysis gives an insight into the performance and probable causes of equipment failure. A company can analyze the factors that lead to breakdowns before they happen and act accordingly.

Manufacturers also understand what drives tear and wear and apply measures that increase productivity.

Product Optimization

Through data examination, a company understands the driving force of quality products.  It helps improve a product’s efficiency and prevents wastage.

Effective Time Management

A significant challenge for many manufacturers is time wastage.  Machine failure can stall productivity.

Companies can detect poor installation, improper downtime coordination, and misuse through practical data analysis.

With this information, there is time management improvement and increasing productivity.

Warehouse Management

Understanding storage requires developing reliable arrangement structures and efficient product flow management. Improving productivity with manufacturing data analysis must focus on this overlooked aspect of warehouse management.

These are four main ways productivity can be improved with manufacturing data analysis. If you’ve yet to do so, now is the best time to see what it can do for you.