Big Data Analytics for Manufacturing
Published on : Thursday 12-05-2022
Data analytics allows business owners to make decisions based on statistical facts, says Darshana Thakkar.
Information storage capabilities have revolutionised in the past few years. From Analog data storage in paper, film, audiotape, and videotape to digital storage ranging from CD, DVD, and hard drive to cloud storage has provided non-linear growth to the data storage technology. This vast data storage capacity has initiated the need for data retrieval in a timely and efficient manner.
Such massive data storage has resulted in complexity in retrieving the necessary information effectively. That has created the need for extensive data management and data analytics.
The Industrial Internet of Things (IIoT) is now fully utilised in the manufacturing industry. That aids in increasing connectivity and generating lots of structured and unstructured data. These data are very much valid for predictive and prescriptive action on plants and processes. Now it’s time to capitalise on the full power of this data to accelerate intelligent manufacturing transformation. The effective utilisation of these data to increase competitiveness is brutal with the traditional approach and standard software. The usage of emerging technologies like Artificial Intelligence, Machine Learning, and Cloud Computing are aiding the management of complex data for the benefit of businesses.
Furthermore, with the Covid-19 pandemic and its economic disruptions, businesses now realise the need to utilise better data for surviving and managing business operations. The increased cybersecurity incidents have accelerated awareness of data governance operations.
All this is changing how businesses collect, manage, utilise and analyse growing volumes of data. This trend is big data and big data analytics. Let us understand this technical jargon.
Big data is data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many fields offer greater statistical power, while data with higher complexity may lead to a higher false discovery rate.
Big data analytics uses advanced techniques against enormous, diverse data sets that include structured, semi-structured, and unstructured data from different sources and sizes, from terabytes to zettabytes.
Nowadays, more and more organisations are switching to the cloud for data storage. Various platforms like Google Drive, One Drive, Amazon Web Services, and Microsoft Azure are used by the companies for the data storage requirements. The main reason is that the cloud data can be accessed anytime. That helps organisations with appropriate decision-making.
But the organisational data are scattered across various platforms, and even part of the data is on the premises. Data analysis has a challenge while there is a need for information to be derived by combining all these scattered data on various platforms. Traditional ERP software cannot provide the necessary information that drives the action.
Usage of AI and ML-based advanced software tools for data analytics is increasing to manage multi-location complex data.
Big data is a collection of data from different sources and is described by five characteristics 5 Vs of Big Data: Volume, Value, Variety, Velocity and Veracity.
i. Volume: It indicates the size and amounts of big data that companies manage and analyze
ii. Value: It is the most important “V” from the perspective of the business. The value of big data usually comes from insight discovery and pattern recognition that leads to more effective operations, stronger customer relationships, and other clear and quantifiable business benefits
iii. Variety: It shows diversity and range of different data types, including unstructured data, semi-structured data, and raw data
iv. Velocity: It is the speed at which companies receive, store and manage data – e.g., the specific number of social media posts or search queries received within a day, an hour, or another unit of time
v. Veracity: This term indicates the “truth” or accuracy of data and information assets, which often determines executive-level confidence
Data analytics in manufacturing
Manufacturing Analytics is the statistical and rule-based analysis of manufacturing data that enables users to understand the process better. It improves their operations, identifies and reinforces best practices, reacts quickly to process events, and anticipates potential problems before affecting product quality, yield, or cost.
In manufacturing industries, records of data analytics can be used to increase equipment utilisation, reduce cost, drive process improvement, reduce human-based errors, and do so at a depth that reveals accurate machine conditions and trends in production.
Apart from these, Big Data tools help manufacturing companies understand the sales patterns of the products. It allows the Sales Department to plan the supply chain for various products actors in the geography.
Critical points for success in manufacturing data analytics
1. Ensure capturing the correct data.
2. Make sure to capture a good amount of data.
3. Ensure not to utilise the data analytics team for manual data preparation.
4. Focusing on the data for appropriate decision making.
5. Ensure that the results are actionable.
Application of data analysis in manufacturing industries
With data analytics, predictions about the various requirements of business can be made accurately and effectively. In the case of manufacturing, utilisation of these analytics can be done across the value chain and the business functions.
1. Predictive Maintenance
With accurate information about the condition of plant and machinery, the maintenance and repair activities can be planned in such a way that helps to achieve:
i. Production target
ii. Reduce maintenance cost
iii Reduce downtime
iv. Increase the life of plant and machinery, and
v. Optimal utilisation of plant and machinery and workforce.
2. Demand Forecasting
Sales and market data analysis helps to derive sales patterns in different seasons across the geographical area of the target market. This helps maintain an inventory that includes raw material, WIP, and finished goods at the plant level and across the distribution network or warehouse. It helps to improve:
i. Utilisation of plant and machinery
ii. Negotiation power of purchase function
iii. Human resources planning, and
iv. Optimising logistic cost.
3. Price Optimisation
An organisation can derive a pricing strategy for the product range with an accurate set of data of different variables, including operation cost, market competition, and demand pattern. Appropriate and timely decision making in pricing helps:
i. To increase sales
ii. To increase the market stack, and
iii. Penetration in the new market/customer base.
4. Product development
With the increase in technology usage globally, no company can survive with the same product for years. Adding new features to the existing product and new product development is necessary for the survival and growth of any organisation. New features, safety measures, and new products are added to the market more often than ever before. It keeps all manufacturing companies on their toes for innovation. Accurate and sufficient information about product performance in the market, competition, and pricing structure of the rivals helps an organisation. Such data analytics allows companies to decide its:
i. Spend on Research and development
ii. Pricing strategy
iii. Target schedule for new product launch, and
iv. Diversification decision.
5. Warranty analysis
Usually, the warranty period offered by companies is relatively lower than the actual healthy performance period of the products. The warranty period provided by the company affects the buying behaviour of the company. With accurate data analysis of the customer complaint in and out of the warranty complaint, the company can take an appropriate policy-making decision. Apart from this, due to the technological revolution, spare parts of the products are also frequently obsolete. Based on the accurate information and replacement pattern of the spare parts, the company can decide on its warranty and warranty for the availability of the spare part for the specific model, which will help to improve:
i. Brand loyalty
ii. Market reputation
iii. Referral sales, and
iv. Reduction in replacement cost.
Benefits of data analytics to manufacturing companies
For manufacturing organisations, data analytics offer the opportunity to harness the knowledge and value hidden within enterprise information systems only. That crucial information within the organisation helps to strengthen the following pillars of the manufacturing organisation:
i. To revolutionise innovation
ii. Enhance supply chain management
iii. Reduce maintenance cost
iv. Improve production
v. Improved marketing and sales efforts, and
vi. Develop and manage profitable after-sales and services.
In short, data analytics allows business owners to make decisions based on statistical facts. That fact can be used to choose an appropriate strategy for future company growth by evaluating a long-term view of the market and competition.
Some facts about Data Analytics
1. 60% of companies worldwide use data and analytics to drive process and cost-efficiency (MicroStrategy, 2020).
2. 53% of businesses adopted big data analytics in 2017 (Research Gate, 2019).
3. 78% of organisations believe that they are using and data and analytics effectively (MicroStrategy, 2020).
For MSME companies, it is high time to utilise historical data for business growth. To all my MSME clients, I always advise and handhold for customer retention and build brand loyalty for referral business before seeking a new marketplace.
It is achieved only by analysing internal business data and external market forces.
Darshana Thakkar is MSME Transformation Specialist and Founder, Transformation – The Strategy Hub. An Electrical Engineer followed by MBA – Operations with rich industry experience, Darshana is an expert in transformation, cost reduction, and utilisation of resources. She has invested 25 years in transforming Micro and Small Enterprises. Her rich experience in resolving pain areas and real-life problems of SMEs helps organisations achieve quick results. Her expertise in managing business operations with limited resources helps clients transform their business practices from person driven to system driven with existing resources.
Darshana has helped many organisations to increase profitability and achieve sustainable growth. She is passionate to support the start-up ecosystem of our country. She is associated with CED, Government of Gujarat as a Business Function Expert in the Entrepreneurship Development program, as faculty for industrial subjects in the Second Generation Program (SGP), and as a start-up mentor and member of the start-up selection committee in the CED incubation centre. She is a certified corporate director registered with IICA and the Ministry of Corporate Affairs, Government of India. Apart from this, she is an author and publishing her blog, article, and case study related to the MSME industry. Email: email@example.com