Data Science & Manufacturing

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Manufacturing & Data Science

Manufacturing businesses are undergoing a huge transformation nowadays. Rapid developments in digital world and broad application of Data Science is catalyzing modern day manufacturing aka Industry 4.0. The 4th Industrial Revolution brings in robotization, automation and broad application of data.

Sooner or later the data science jargon and marketing hype is going to subside, and manufacturing companies, among many other sectors, are going to find themselves sitting with broken promises. It is therefore important that these organizations understand clearly how they stand to benefit from and be empowered by data science.

Data science brings multiple benefits to manufacturing. It allows to automate large scale processes and reduces execution time among other benefits. Data Science is going to change the manufacturing industry dramatically.

Why Data Science cannot be ignored?
  • Predictive Analysis: Data Science is enabling manufacturing to predict the future on the basis of analysis of present data. This helps companies to plan and prepare in advance for problematic situations. Manufacturers are deeply concerned about factory functioning and its high performance. Over production, idle time, logistics, inventory planning are a few areas where Data Science enables accurate predictions.
  • Fault Prediction & Preventive Maintenance: Both these data science models are aimed at correctly predicting the moment with the equipment(s) is likely to fail. Secondary goal of reducing these failures or at least reduce these instances may then be achieved.
  • Warranty Analysis: The manufacturing industry spends considerable time and money in settling warranty claims every year. Warranty claims disclose valuable information on quality and reliability for the product. Data Science helps to reveal early warning or defects of the product.
  • Other Areas: Inventory Planning – Demand Forecasting, Price Optimisation, Robotisation, Product Development, Computer Vision Applications and Managing Supply Chain Risk.
Challenges
  • High Costs to Implement Data Science: Industrial data is produced from diverse sources that includes: Large Scale Devices Data, Production Lifecycle Data, Enterprise Operations Data, Manufacturing Value Chain Data and External Collaboration Data. Diverse data modelling and then subsequent patterns identification coupled with correlation among data sets makes Data Science a complex and capital-intensive affair.
  • Data Confidentiality: Most of the manufacturers do not have inhouse capabilities on Data Science. They, hence have to share their data with professional agencies which puts them on data compromise. Most manufacturing companies do not want to share their sensitive data with external parties. They hence are not able to leverage the power of Data Science.
  • Lack of Data Visualisation: Diverse and massive data brings in the need of better data presentation. A good visualisation of original data can inspire good business decision. Currently it is difficult to visualize data coming in from heterogenous sources and domain expertise on all processes is not available to all workers. It is hence logical for the manufacturing companies to skill their workers who have process and technical knowhow on Data Science.

Industry 4.0 – Smart Factory | Data Science

Data Science is an incredibly broad and exciting field for industrial and discrete manufacturing. Major disruptions are triggering rise of demand for Data Scientist in the coming years. Using data science, the smart factories can double up their output without doubling their assets. Lean manufacturing is not that optional anymore. Most business owners are looking at cost cutting as primary objective. The common denominator in each of the processes is data.

Data Science and Data Scientists are imperative for companies involved in manufacturing. Upskilled shop floor managers and data analysts will be easily able to apply new data science techniques and models on real time data to infer and reach business / production critical decisions.

Advantages of an upskilled Workforce

  • They will be able to explore data patterns, form hypothesis and design routine tests.
  • In business environment, data scientist can help test variables that influence company’s profitability and efficiency.
  • The upskilled workers will be more effective in QA, defect tracking, supplier relations, throughput forecasting and achieving greater energy efficiency.

How Can SkillUp Technologies help you leverage Data Science?

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