Early in 2020, as the effects of the coronavirus epidemic spread over the world, manufacturers from all sectors scurried to adapt to the new normal. The manufacturing industry is currently experiencing a different kind of crisis less than 15 years after the Great Recession.
The damaging effects that a recession can have on the manufacturing industry are nothing new. Historical evidence shows that manufacturing suffered the most severe financial impact during previous U.S. recessions. According to a Deloitte report, from 2007 and 2009, the industrial manufacturing sector’s GDP fell by 10%, versus a 4% drop for the entire economy. Similarly, while total corporate profitability fell by only 14% during the Great Recession, industrial manufacturing business profits plunged by 53%.
Currently, manufacturers in hard-hit areas have noticed significant decreases in production order volume and the ensuing waves of layoffs as companies servicing essential (important for work-from-home comfort) industries such as technology and healthcare struggle to meet skyrocketing consumer demand. According to an article from Deloitte, manufacturing is susceptible to recessions because of the consumer. During an economic downturn, consumers buy from industries that supply basic human needs, such as food and healthcare. Since demand dips due to conservative buying, manufacturing is adversely affected.
However, the industry has a history of being more vulnerable to recessions and among the earliest to recover from them. According to Forbes, industrial manufacturers recovered their profits 300% faster from the recession of the early 2000s to the Great Recession of 2008. As a result of this boom in production, all industries experienced growth, and the economy emerged from recession.
Could DATA be the answer to manufacturers’ recession woes? How and Why?
The Big Data market in the manufacturing industry is predicted to reach $9.11B by 2026. They generate large petabytes of data daily, and over 80% – 90% of enterprise data is unstructured and is growing at a rate of 55% – 65% per year. It comes from various sources, forming a complex web of unstructured data in the industry. This leads to discrepancies in data management, security threats, storage costs, time, and resources. The massive amounts of data generated daily make it challenging to integrate new technology and digitally convert an organization.
Manufacturers must first determine how much data they have available to them to improve yield with advanced analytics. The large amount of data generated is primarily used for tracking purposes rather than to improve operations. To make the most of preexisting process data, manufacturers must invest in technologies and skill sets that will enable them to maximize their value.
To understand how manufacturing enterprises use data to grow their company, one must first understand Industry 4.0. Industry 4.0 is the current automation and data exchange trend in manufacturing technologies. It includes cyber-physical systems, the Internet of Things, cloud computing, and cognitive computing.
Did you know that 56 percent of all manufacturers have reported improved productivity due to cloud adoption?
The use of data is pivotal in Industry 4.0 as it allows companies to gain insights that were not possible before. With data, companies can improve their products, optimize their processes and make better decisions. Data is so essential that it has been dubbed the “new oil.” There are many ways in which manufacturing enterprises can use data to grow their company. Below are a few drivers:
- Predictive maintenance is a type of maintenance that uses data to predict when equipment will fail. This allows companies to fix the issue before it becomes a problem.
- Supply chain analytics is the process of using data to improve the efficiency of the supply chain. By using data, companies can identify issues and optimize their supply chain.
- Data can be used to improve customer service. Data analysis allows companies to identify customer needs and preferences and tailor their products or services accordingly. Additionally, data can be used to track customer satisfaction levels and target areas for improvement.
- Improve production process by collecting data on production times and analyzing it; companies can identify areas where they can speed up their process or make it more efficient.
- Track inventory levels and trends so companies can order suitable materials and avoid shortages or surpluses.
Overall, data provides a wealth of information that can be used to support the growth of manufacturing enterprises. In a nutshell, manufacturing data management goes beyond simply organizing data; it also includes collecting, consolidating, analyzing, and optimizing it, especially when enterprises are exploring ways to reduce data storage costs and make better decisions that increase efficiency and profitability.
However, the most difficult step is making sense of the massive amounts of unstructured data – What and where is the data? Who has access? Is it secured and compliant? How can it be optimized?
Here’s an example of a Fortune 50 multinational automotive company that effectively analyzed, cleansed, tiered, and archived 3+ petabytes of unstructured data using lightweight single data management software.
Modernizing and Optimizing Manufacturing through Advanced Data Strategies with Data Dynamics
As we have seen in the blog, manufacturing enterprises are inundated with massive amounts of data. Untangling this tangle of data and extracting maximum value from it is critical for businesses to stay ahead of the competition and understand changing customer & market needs. Data Dynamics assists the manufacturing industry in leveraging the extraordinary knowledge and expertise found in data already within their organizations to achieve maximum efficiency and achieve both short- and long-term business value.
Data Dynamics is a leading provider of enterprise data management solutions, helping organizations structure their unstructured data with their Unified Unstructured Data Management Platform. The Platform encompasses four modules – Data Analytics, Mobility, Security, and Compliance. Proven in over 300+ organizations, including 28 Fortune 100 and 5 Fortune 500 manufacturing enterprises, the Platform is a one-stop solution that enables manufacturing organizations to fully capitalize on the capabilities of unstructured and high-volume data and realize competitive advantages.
- Smart Factory: New technologies like AI, machine learning, IoT, and predictive analysis must be implemented to transform traditional factories into smart ones. This can be accomplished by structuring the unstructured through context, content analysis, and intelligent data-driven cloud migrations. Through data alignment and refinement, manufacturing enterprises can transform data from stored assets into business assets.
- Secure: Deploy secure content analytics technologies powered by AI that offer a unique duality of addressing critical business challenges around the cost of operations and security of PII/sensitive data while bolstering business velocity and revenues. By melding existing workflows and data infrastructure with analytics, we help enterprises achieve the benefits of more contextual decision-making, better customer experience, and risk reduction.
- Optimize: Enable enterprises to decide what types of data will be useful for them, where to get it and how to store it—categorizing, tagging, indexing, analyzing, and migrating data across heterogeneous sources using context analytics and automated mobility. As a result, they can tier and archive data based on hot, cold, ROT, and dark data, reducing data sprawl, consolidating data lakes and centers, and optimizing storage.
- Save: Offering enterprises AI-driven analytics to gain critical and accurate insights into unstructured file metadata for accurate PII/PHI/sensitive data discovery, storage visibility, and infrastructure optimization. Optimize costs of data-related technology through analysis, TCO model, and ongoing data management processes.
- Transform: Provide support for the modernization of existing infrastructure, such as the cloud, with end-to-end data analysis, migration, and augmentation to attain the transformation goal.
