By analyzing traditional and new data types, including social media sentiment, manufacturers can stay one step ahead, fine-tune their forecasting models, and meet customer demands with precision and efficiency, writes Sunil Kardam, head of logistics and supply chain at data science and AI company Gramener.

SunilKardam-Gramener

Sunil Kardam

“Water, water everywhere, nor any drop to drink.”

The famous line from Samuel Taylor Coleridge’s poem captures the ongoing challenge in data management within the manufacturing industry: There’s an ocean of data available, but manufacturers struggle to extract its true value.

An effective data strategy can be a game-changer in delivering precise updates on order status and estimated arrival times.

To identify new revenue opportunities and future-proof their businesses, manufacturing companies collect a plethora of data from different channels such as machines, software solution platforms, social media, and in-house data tools. However, only 30% of companies have a well-articulated data strategy in place, according to the NewVantage survey.

At its core, a data strategy acts as a roadmap that defines how an organization will collect, manage, and analyze information. But how does setting explicit data policies and practices help manufacturers optimize their business operations? Let’s dive right in.

advertisement

Reduce unplanned downtime and empower efficiency

Did you know that unplanned downtime costs industrial manufacturers a staggering $50 billion yearly? However, data-driven maintenance strategies hold the key to turning this situation around.

By tapping into the power of data analytics and machine learning algorithms, a new-and-improved data strategy empowers organizations to uncover patterns that signal equipment failures or maintenance attention. This way, manufacturing companies can accurately predict upkeep cycles and meticulously schedule preventive maintenance activities, reducing unplanned downtime and extending the lifespan of assets.

On top of that, when downtime occurs, a data strategy enables streamlined root cause analysis involving equipment sensors, maintenance logs, and production data. This means manufacturers can take proactive actions to avoid similar incidents in the future, leading to substantial cost savings.

More efficient cargo shipment

From commercial invoices and customs declarations to certificates of origin and packing lists, shipping goods internationally involves a significant amount of documentation. Since a data strategy can help seamlessly organize and manage these documents, companies can unlock a multitude of advantages.

advertisement

For starters, manufacturers can avoid unexpected delays by minimizing errors that may arise from the manual handling of documents. Additionally, digital systems allow them to ensure compliance with regulatory requirements, mitigating the risks of penalties or legal complexities.

Now, equipped with real-time visibility,  a well-crafted data strategy is an ally in allowing businesses to monitor the status, location, and estimated time of arrival of shipments.  Manufacturing companies can make proactive decisions to tackle any potential delays, disruptions, or challenges that may emerge during the transportation process.

The significance of hypervisibility extends to consumers as well, as nearly all of them actively track their orders. That’s why an effective data strategy can be a game-changer in delivering precise updates on order status and estimated arrival times. This transparent approach fosters trust, boosts customer satisfaction, and facilitates prompt communication for any delivery changes.

Improve demand forecasting accuracy

Manufacturing companies can leverage historical sales, marketing data, and customer feedback to improve demand forecasting. However, a robust data strategy takes things a step further by seamlessly integrating external data sources such as market research reports, social media sentiment analysis, and industry trends.

advertisement

With this powerful combination, manufacturers can stay one step ahead, fine-tune their forecasting models, and meet customer demands with precision and efficiency.

To illustrate this, a fashion apparel manufacturer can monitor social media platforms such as Pinterest, Instagram, and TikTok — or fashion blogs — to gather insights on the latest fashion trends. If they identify that a particular color or style is gaining popularity among consumers, they can adjust their production plans to meet the anticipated demand for trending products. This way, organizations can also reduce stockouts, excess inventory, and associated costs.

Wrapping up, research shows that 93.9% of organizations are aiming to ramp up their investments in data in 2023. However, it is critical to emphasize that a mere intention is not enough: Creating a data strategy is the most effective way forward. Only then can manufacturers say goodbye to unplanned downtime, welcome the era of efficient cargo shipments, and enhance demand forecasting accuracy, positioning themselves as frontrunners in a competitive market.

About the author:

Sunil Kardam is the head of Logistics & Supply Chain at Gramener, a data science and AI company.

advertisement

Sign up

Sign up for a complimentary subscription to B2B News, published 4x/week, covering technology and business trends in the growing B2B ecommerce industry. Contact editor Paul Demery at [email protected] and follow him on Twitter @pdemery.

Follow us on LinkedIn and be the first to know when new B2B News content is published.

Favorite