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Manufacturing 4.0 and digital transformation

By Steve Cavolick

The economic downturn from the coronavirus has rewritten the rules of business and caused entire verticals to change direction overnight, and manufacturing is one of the industries that has morphed quickly in order to lead the country through the pandemic.

When demand exploded earlier this year for goods that sanitized and machines to help very sick people breathe, facilities that produced consumable alcohol switched to making anti-microbial gels and auto manufacturers stopped producing cars in order to build ventilators.

But even for companies that were able to adjust to the new conditions, the economic outlook is harsh. In fact, almost 80% of respondents to a National Association of Manufacturers survey expect the virus to have a negative financial effect on their business, which includes impact to operations, liquidity, and capital resources.

So knowing how to retool in the face of black swan events should now be in the playbook of every manufacturer, and digital transformation is one of the best ways to get you there. Organizations that have achieved digital transformation have systems that stress intelligence, connectivity, and automation. These are some of the main tenets of Manufacturing 4.0 and all require mastering your data.

Digitally transforming your business means that you can deploy analytical applications that address operational pain points and drive bottom line impact. Some of these applications include, but are not limited to: 

New Product Development

By using Big Data to understand customer behavior better, manufacturers can bring new products to market faster or improve existing ones. By considering consumer desires in the design of goods, manufacturers provide more valuable products to their customers while reducing the risk of introducing a new product. 

Demand Forecasting

One of the biggest bugaboos for manufacturers is estimating demand. You can slog through traditional ways of forecasting using spreadsheets or other statistical methods, but AI adds power to this task. AI uses self-learning algorithms to automatically recognize patterns, find complex relationships in large data sets, and flags demand signals. With the AI approach to demand forecasting, you can leverage your historical demand data, but also pull in outside, unstructured data from surveys, weather forecasts, and customer social media activity. 

Price Optimization

Manufacturers must sell a product for more than it costs to make. It sounds simple, but price something too high, you sell less. Price it too low, and you leave profit on the table. Many factors go into determining the correct price: raw materials, labor, where to produce, and development and distribution costs, to name a few. Price optimization uses internal cost data and competitors’ pricing information to determine a price that is just right for customers and the manufacturer. 

Reducing Supply Chain Risk

We touched on this in last month’s post, but it bears repeating. With predictive analytics, companies can predict supply chain delays and impacts to production. Manufacturers can use this insight to identify backup partners and develop continuity plans.

There can be no digital transformation without data, and manufacturers are collecting more data than ever. But is it the right data? Is it clean data? Helping organizations collect, organize, and analyze their data is what we do, and we’ve done this for a variety of manufacturers, including those that make center pivot irrigation systems, locomotives, and water fountains and sinks.

Not sure how to get started? Our strategic offerings can help you align business and technology teams, discover right use case, and determine an ROI. 

The LRS Big Data and Analytics team has over 20 years of experience in analytics, information management, and data warehousing. If you are interested in understanding how we can help you find value in your data, please fill out the form below to request a meeting. 

About the author

Steve Cavolick is a Senior Solution Architect with LRS IT Solutions. With over 20 years of experience in enterprise business analytics and information management, Steve is 100% focused on helping customers find value in their data to drive better business outcomes. Using technologies from best-of-breed vendors, he has created solutions for the retail, telco, manufacturing, distribution, financial services, gaming, and insurance industries.