Upgrade Your Manufacturing Data Stack for Advanced Analytics

Manufacturing is a process involved in just about every industry – from consumer-packaged goods to pharmaceuticals. With such widespread applications and meticulous practices, how are businesses effectively keeping track of their data?

Manufacturing can be plagued by a variety of concerns such as equipment failures, product liabilities, supply chain interruptions, cyber risks, and more. Most of these associated risks are directly related to data storage environments and analytic processes, many of which are becoming outdated thanks to new cloud-based technologies that are less demanding and offer more benefits than their older counterparts.

All things considered, “old-school” data stacks such as Hadoop, Cloudera, Hortonworks, or Oracle no longer make the cut to protect modern businesses effectively, nor do they offer the Advanced Analytics power of cloud-based data stacks which can help efficiently process the largely unstructured data that comes from manufacturing processes.

Preventing Risk in Manufacturing

A modern data stack can help your business avoid down time while simultaneously improving traceability of products and increasing overall productivity and profitability. Read on to learn how legacy data stacks compare to new technologies employing Advanced Analytics to combat some of manufacturing’s most common challenges.

Equipment Failure

Equipment failure rates are directly correlated with reliability. Higher failure rates lead to unreliable equipment, which not only puts your product at risk, but also your personnel. Machine failures account for almost $1 trillion in losses each year for some of the world’s largest manufacturers, and the average 27 hours of production time lost monthly only adds to the financial toll.

Equipment failures can be more easily tracked and predicted with modern cloud-based data stacks that utilize Advanced Analytics to process data in real-time. This approach can detect circumstances that may lead to equipment failure and notify the appropriate parties, reducing overall risk and even protecting employees from potential endangerment should they be injured due to machine failure. Real-time analytics also means that production quality can be tracked, and machines can be turned off remotely to avoid producing poor quality products.

Supply Chain Interruptions

The supply chain itself consists of many steps that each face their own set of challenges. From order fulfillment to managing returns, it is imperative to have access to data that allows these processes to take place efficiently. Any interruptions can cause a domino effect of delays further down the supply chain and result in significant losses.

Legacy data servers typically do not have sufficient resources to protect against drops in coverage on a locally hosted level. Cloud-based stacks, however, are built strategically to be redundant across multiple servers. This means that your data is protected and accessible in case of an outage, and your business does not need to suffer delays that could impede shipment or production, spoil products, cause customer dissatisfaction, or lead to other issues.

Product Liabilities

While the main goal of every manufacturer is for their products to meet or exceed the expectations of customers, product liability can have more serious implications depending on the industry in question. Life sciences and pharmaceuticals, for example, must place a priority on this aspect of manufacturing. When producing items such as vaccines or surgical implants, it is imperative to be able to store and track data from every step of the way to ensure process validation in accordance with the FDA’s policies.

Process validation requires copious amounts of data to be tracked and stored long-term in order to ensure traceability for every product that is manufactured in case of later issues. A legacy system built to handle these demands would not only be costly, but it would also require designated physical space with room for constant expansion, as well as a well-staffed and educated team to maintain it. Modern cloud-based data stacks connect you instantly to thousands of computers if needed, handling larger volumes of data and more complex tasks without the demand for on-site resources. With these limitations removed, it is easier to keep track of important data used to validate these high-risk products.

Cyber Risk

Lastly, cyber risk is a growing issue as new technologies mean new ways to break through existing security systems. It can be difficult and time consuming to upgrade cybersecurity practices for large-scale on-premise data servers, which are more vulnerable to attacks by threat actors than their cloud-based counterparts.

Cloud-based servers, on the other hand, are hosted remotely and thus add an additional layer of security into the mix. Cloud-based software like Snowflake, AWS, Redshift, and Synapse have specially designated Cloud Security teams that protect everything from physical networks to data servers, operating systems, middleware, and end-user hardware. With the most demanding part of cybersecurity being managed off-site, businesses can focus their time and effort on productivity and profitability.

Why SDG?

SDG can help your business upgrade from your legacy data stack to take advantage of less demanding cloud-based services. With custom solutions equipped with Advanced Analytics strategies, businesses can improve how they use their data. SDG’s experience across a variety of industries including life sciences, manufacturing, retail, and consumer-packaged goods means that our expert teams are equipped to tailor a solution to your specific needs.

Contact us today to learn more about our custom data management and analytics services.