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A New Revolution

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By Steven Bowen

Data analytics has been focused mainly on the front end as a way to glean insights about customers and improve the customer experience. But with the growing investment in industrial Internet of things (IIoT), combined with dramatic changes in global supply chain logistics, a back-end focus is emerging in what is often referred to as the fourth industrial revolution.

Companies looking to improve their supply chain logistics won’t find the solutions in specific software packages, compliance initiatives or collaboration strategies. Although those are all important, the biggest factor in maintaining a competitive edge is how the enterprise will use the overwhelming amount of data that comes into the enterprise every day like a fire hose. Use of that data will transform the supply chain into a competitive weapon by moving it from a simple, loosely-connected network of suppliers and transportation, to a much more refined ecosystem based on the IIoT, a smart factory, ubiquitous interconnectivity and enhanced relationships.

The advantages of this revolution include the ability to deliver improved time-to-market, cost savings, cash flow and operational improvements, but those advantages will be hard won.

Growing profitability through enhanced EBITDA and cash flow while remaining competitive depends increasingly on predictive modeling. According to multiple surveys, manufacturers would be able to leverage data analytics to drive billions of dollars in benefits, much of which is derived by deploying predictive forecasting aligned with an on-demand supply chain and operational improvements. 

We’re not quite there yet. Many enterprises still struggle to extract value from their vast quantities of data, failing to capture the full potential value of the combined power of IIoT and data analytics. This gap between potential benefit and actual results is driven by multiple factors, including data not being available in a timely and consistent manner, lack of in-house data scientists, lack of data standards and policies and a lack of ownership and governance which results in confusion around who owns what data. These gaps are fueling the need for better data analytics capabilities that combine deeper functional knowledge, with data science expertise for translating that data into actionable outcomes throughout the supply chain. 

Driving Profitability

The opportunity for increased EBITDA, cash flow and growth all comes down to data. Traditional revenue drivers – opening up new markets, driving new sales, and releasing new product mixes – are all rooted in the data that is available and how it is analyzed. By the same token, driving profitability through operational improvements is driven by the same data. And finally, supply chain issues such as over- or under-production can be avoided by using predictive data analytics to understand more precisely what products are needed, in what quantities, and in which markets. 

Leveraging data and analytics along the buy-make-move-fulfill supply chain, delivering the greatest value, while achieving the lowest costs, taking into account the dimensions of procurement, logistics, and operations, helps companies adapt more quickly and optimize value, while achieving a more forward-looking and efficient supply chain. Known as Total Value Optimization (TVO), this approach is based firmly in data analytics and supply chain transformation based on actionable insights, in an effort to improve EBITDA, cash, and enable growth. 

Over a third of CEOs do not trust the information they have at hand for decision-making. They often struggle to make decisions due to poor data integrity and limited visibility due to data being held across disparate data silos. The solution therefore isn’t just more data, it’s more effective aggregation and visualization of data that helps drive actionable insights. This can be described as a pyramid representing five levels of data maturity, beginning with a reactive, backward-looking data model (level one), to a forward-looking, prescriptive data model (level five). 

At the highest level (prescriptive analytics), organizations are able to drive insights into both existing and emerging opportunities, and make proactive decisions. At this level of maturity, organizations can unlock the value of the supply chain through advanced strategies including machine learning, simulation modeling, and optimization and automation of decisions in real-time. 

Manufacturers may find that their biggest challenges though do not lie in production, rather, in shipping and transportation. A major shortage of long-haul truck and rail transportation is already upon us, causing many manufacturers to re-think their supply chains. In the face of this transportation crisis, solutions may involve new relationships with new shippers. In the area of transportation, as with manufacturing and operations, the solution lies in data and analytics to gain a deeper understanding of the effects of this current crisis, and what the potential solutions will be. 

As with any disruption, the fourth industrial revolution is comprised of both challenges, and disruptive solutions. Making the most effective use of data is a key step towards this leveraging the IIoT revolution.

Steven Bowen is the Chairman and CEO of Maine Pointe, a firm specializing in driving EBITDA and cash improvements across the areas of procurement, operations, and logistics to enable growth. Bowen has more than thirty years of procurement and logistics experience, leading turnarounds, high-growth businesses, and Fortune 1000 companies. In 2017, he published his first book, “Total Value Optimization: Transforming Your Global Supply Chain into a Competitive Weapon.” The book describes the pressure today’s corporate leaders are under to deliver differentiated, lasting performance fast. For more information, visit www.stevenjbowen.com.

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