Right now, India is emerging as an international manufacturing hub. Thanks to the ‘Make in India’ initiative, manufacturers like Boeing, HTC, GE, Toshiba, IKEA, Siemens and others, have either already commenced or are planning to move their manufacturing operations in country. At the same time, Indian manufacturers are leveraging international momentum by becoming future-ready; a 2016 report by FICCI and Tata Strategic Management Group revealed that more than 80% of Indian manufacturers were willing to adopt advanced manufacturing technologies like advanced robotics, Augmented Reality and IIoT to improve current practices.
This shift is expected to usher in a new phase of digitalisation for the Indian manufacturing industry, it will also increase the volume, density and quality of data available to manufacturers. As a result, experts are betting that Big Data will increase in value within the country’s manufacturing sector; International Data Corporation (IDC) has estimated that data-led businesses can unlock nearly $430 billion in productivity gains over competitors, who do not leverage business analytics. However, business analytics is not just about processing vast quantities of data – it is about finding relevant business insights from the available information that can help in enhancing existing practices and driving greater optimisation.
The following are a few ways in which business analytics is revolutionising the manufacturing industry:
1. Increasing data accessibility, availability, and agility
One of the biggest benefits of employing an analytics-led approach lies in the simplification of data. Traditional manufacturing data sets are very complex and often require specialists with system-specific knowledge and expertise to glean insights from them. The sheer volume of information that manufacturing processes generate every second also makes it virtually impossible to make accurate, data-led decisions in a timely manner without business analytics models.
Take the transformation witnessed by SABMiller India, for instance, which implemented the Qlik data analytics platform across various operational verticals. Qlik’s innovative solution demystified the data to make it more accessible and agile; information about various operational parameters, such as, freight costs, distribution expenses, manufacturing cost and variances, market working & compliance of sales team, and out of stock and ageing of stock situations in markets was quickly made available in an easily consumable and understandable visual format. This not only helped the organisation cut down on the time and effort spent in creating manual reports, but also triggered the transition from a report-led approach to an insight-driven work culture.
2. Boosting collaboration opportunities across the board
Business analytics makes it possible for different data sets to be accessed by all stakeholders – from C-suite executives to process owners, plant management, and assembly line operators – at the same time and across multiple devices or screens. This enhanced sharing of information across an organisation leads to more collaboration that can ultimately improve product quality, as well as minimise manufacturing costs, time, and wastage. For instance, an on-floor operator can share the floor data with the maintenance team to understand if the recent slowdown in production numbers is due to a technical fault within the assembly line, allowing the organisation to identify and address operational issues before they lead to significant losses.
Having initially deployed Qlik for its after-sales environment, Mercedes-Benz India noticed the immediate impact made by the solution in driving operational optimisation. Reporting became faster and more accurate as processing times reduced substantially, while context-driven insights allowed for better and more informed decision-making. This led to the deployment of Qlik across other functions, such as, plant operations, sales, and HR. With a single data architecture overseeing all business intelligence, the improved access to information thus facilitated across the organisation and led to an increase in cross-departmental data collaborations.
3. Optimising business functions and processes
As effective as automated data analytics is, algorithms are often designed towards finding a particular outcome. This often makes it difficult for manufacturers to gain a holistic overview of their complex data sets. Business analytics, on the other hand, enables multiple stakeholders to conveniently interact with massive data sets in real-time. Such a mechanism allows for the integration of operational and experimental data to generate highly-detailed insights, which can in turn drive process optimisation and enhance manufacturing processes. In addition, inbound and outbound supply chains can be strengthened, while sourcing and utilisation of resources can be optimised to meet current and future demand. Business data also makes it possible to more readily identify additional revenue streams and business growth opportunities for manufacturers.
4. Enabling swifter and more accurate decision-making
By providing a holistic view of all the data across a business ecosystem, including from multiple sources, business analytics enables manufacturers to dig deeper into their available information sets and visualise various trends and patterns relevant to them. This overview style approach allows them to analyse data using specific models and take precise, proactive decisions. Users are additionally able to factor in various data points, historical precedents, anomalies/exceptions, actions taken and their degree of efficacy, acceptability of the outcomes, etc. to arrive at more accurate decisions. This helps them in not just the decision-making process, but also in understanding the reason why they arrive at a particular decision.
5. Driving efficiency and minimising downtime
Visibility over the supply chain is amongst the major challenges that manufacturers face. This is due in part to the massive volumes of data – such as, product manufacturing, packaging, shipping/logistics, and coding, etc. – involved in supply chain management. Business analytics brings this information to the fore, enabling manufacturers to identify supply chain bottlenecks and address them. This helps in augmenting the responsiveness of the supply chain, mitigating risks and decreasing production costs.
A 2016 survey conducted by Honeywell Process Solutions and KRC Research Inc, Data’s Big Impact on Manufacturing: A Study of Executive Options, revealed the importance of data in today’s manufacturing processes. Of the 200+ North American manufacturing executives surveyed under the study, nearly 68% outlined their plans for investing in data analytics to become more competitive in a cut-throat business landscape. This highlights how data is fast establishing itself as the new gold standard in the manufacturing industry. Having been on an impressive growth trajectory recently, the Indian manufacturing industry is expected to adopt a similar data-centric approach to optimise its operations and drive further growth. Manufacturers in the country have an unparalleled business opportunity right in front of their eyes – and to experience further growth and deliver competitive advantage, data must continue to be at the forefront of manufacturing in India. ☐