1) Data science uses large amounts of data from various sources to derive meaningful insights through statistical analysis and machine learning. This helps manufacturing companies with decision making. 2) Data analytics has become integral to manufacturing, with data-driven companies seeing 30% annual growth on average. The market for data analytics in US manufacturing was $904 million in 2019. 3) Data science helps manufacturing companies with quality control, predictive maintenance, demand forecasting, supply chain optimization, automation, and product development.

1. HOW IS DATA SCIENCE
SHAPING THE
MANUFACTURING
INDUSTRY?

2. What is Data Science?
Data Science is the modern innovation tool in the arsenal for industries. It is
the mashup of huge volume, variety, and velocity of data from which
insights and intel can be derived in a meaningful and methodical manner.
It’s a multidisciplinary field where the cocktail of statistics, math, and
business acumen creates multiple variations of data visualization. With an
appropriate & vast amount of data fed into the data model, it can cultivate
powerful insights for any industries especially manufacturing.

3. What is the impact of Data Science in
Manufacturing?
Data Science for modern manufacturing is the backbone for the decision-
making process. Within a very short period, Data analytics has become an
integral part of the manufacturing industry. As per Forrester, data-driven
organizations have gone ahead of the curve from their traditional
competitors and on average, report around 30% growth annually along
with better profitability and client retention and acquisition. In 2019, in the
US alone Data Analytics for manufacturing was clocking a market share of
904 million USD. According to Grand View Research, by 2025, global smart
manufacturing is poised to be estimated approx. USD 395.24 billion, with a
CAGR of 10.7%.

4. What are the opportunities that data
science and ML provide in manufacturing?
Quality assurance, performance, and loophole tracking
Predictive and conditional maintenance of machines and tools
Demand and throughput forecasting
Supply chain Optimization
Ongoing Automation and new and innovative designing
product development cycles and application and testing of new production techniques
Sustainability and achieving energy efficiency
Data Science and ML go hand in hand in manufacturing industry transformation. Data
Analytics services provide ample benefits in manufacturing. A few of them are:

5. The journey to embracing data science in the industry has just begun. Data Science for
manufacturing will grow exponentially over the next 5 years. According to PwC, data
science applications and utility will grow double-digit on an average in all the respective
sub-fields of manufacturing – be it predictive maintenance, integrated planning, or
transfer of production parameters. What we are witnessing here is a full-scale industrial
revolution in digital terms.
Future Scope for Data Science in
Manufacturing

6. Data Science
Applications in the
Manufacturing Industry

7. Predictive Analytics for Real-Time
Performance & Quality Check
For manufacturers, the capability of generating a quick and timely response to issues has
a direct impact on the downtime cost and productivity. Predictive analysis in the
manufacturing industry can be leveraged in multiple ways – monitoring machine
performance, prior identification of machine downtime, accurate prediction of the nature
of yield gain, scrap reduction, and also any considerable influence of external change.
Predictive analytics takes care of everything!
One of the most important KPI or Key Performance Indicators where data science and
analysis impact heavily is the Overall Equipment Effectiveness or OEE. It collects data
from all machines and operators to create a set of KPIs. In case of any discrepancy or
deviation, it enables the management to do root cause analysis of downtime and scrap
and its impact on productivity. Data Science as a unit offers a proactive and responsive
approach to tackle machine optimization and maintenance, cost management, and
quality improvement.

8. Predictive Maintenance Along With Fault
Prediction
Prior Detection of anomalies
Prior Failure mode detection
Accurate Time to Failure (TTF) detection
Optimal maintenance time detection
Unplanned downtime is the biggest threat to the manufacturing industry. Machine
breakdown and unplanned downtime are the biggest contributors to increasing overhead
costs in manufacturing. On average, over the last 3 years, it has cost businesses
approximately a staggering $2 million! The biggest headache for the management is, it
has maintained its pace with time and grown significantly.
For example, in 2014, downtime cost on average used to be $164,000 per hour which went
up by 59% in just 2 years to $260,000 per hour. The industry has incorporated new-age
technologies such as prior detection and condition-oriented monitoring with the help of
techniques like PCA-T2, autoencoders, neural networks, regression models, survival
analysis, classification models and one-class SVM. It helps in the following –

9. Price Optimization
In this fiercely competitive market, every organization wants to stay ahead of its competitors.
The manufacturing industry is also crawling with competition from domestic and foreign
entities. In such a case, to stay ahead of the game, data science becomes a crucial tool and
rather an element of surprise for the manufacturers. What used to be a completely manual,
boring, and labor-intensive process, can be done with a snap of a finger. From raw material
purchase to distribution cost, all the factors are taken into consideration while calculating the
final cost of the product. The cost also needs to look attractive to buyers to be successful.
In such a volatile scenario, finding the best possible quotation is the game-changer, and data
science in manufacturing does the trick. Data science tools perform analysis and aggregation
of data, costing from internal sources, market competitors into main consideration and model
around product efficiency and maximization of profit to provide the best possible price in
market attractive terms. Change in consumer behavior, fluctuations in internal and external
factors, along with present time market competition all are the key factors of data collection for
price optimization.

10. Supply Chain Optimization
The supply chain is the backbone for the manufacturing industry and managing it is a
herculean task. With the modern-day complexity, it becomes even tougher due to increased
complexity and unpredictability. Data scientists here come to the rescue. From collating the
inputs from shipping and fuel expenses, the difference in pricing to scarcity in the market to
local tariffs all the pointers are thoroughly gathered, segregated, and analyzed with the right
data science model.
Data Science along with Machine learning and Artificial Intelligenceempowers supply chain
management in an intelligent manner. Modern-day supply chain management systems
powered by Machine Learning, can analyze vast sets of data from multiple fields such as –
material inventory, work-in-processes, present market trends, inbound shipments, consumer
sentiments, and behavior along with weather forecasts and take data-oriented decisions to
find out the optimal solutions.

11. READ THE FULL ARTICLE
https://www.datatobiz.com/blog/data-science-in-
manufacturing-industry/