A core principle of One Network Enterprises is that reducing information latency – even eliminating it, if possible – is fundamental to how it brings value to its customers, and it now has the science to back it up.
A study recently released by the University of North Texas (UNT) as part of a funded project between the school and the company has demonstrated that One Network Enterprises’ Real Time Value Network for leveraging a new inventory management strategy called the Science of Theoretical Minimums (STM) that minimizes inventory by reducing physical and informational lead times from the customer back through the entire value chain.
One Network Enterprises, which has established itself as a LinkedIn-type business network with a platform that supports business solutions to customers and helps them connect with each other in real time, wanted to see scientific validation of its business model, said senior vice president of products Adeel Najmi in an interview with EBN. “No one had really spent the time understanding the value of information and information flows,” he said. “We wanted to see some scientific validation for our hunches.”
The company had also been getting exposed to its customers’ digital transformation efforts, prompting One Network Enterprises to partner with UNT about 18 months ago so that an “academic and scientific lens” could be applied to the company’s models, said Najmi. The work was done with UNT’s Center for Logistics Education and Research and Jim McNatt Institute for Logistics Research.
The UNT study essentially found that One Network Enterprises’ Real Time Value Network effectively embraces the Science of Theoretical Minimums (STM) that minimizes inventory by reducing physical and informational lead times from the customer back through the entire value chain.
The Real Time Value Network transforms the supply chain by enabling all parties to focus on the customer and lowering costs by eliminating information delays across the extended supply chain.
The latest findings from the ongoing research partnership between UNT and One Network has, for the first time, identified how organizations can apply a quantitative approach to incorporate information lead times in supply chain designs to drive innovation and achieve digital transformation objectives.
STM provides a methodical approach and accompanying governance structure that enables managers to uncover fundamental latency and the costs that result because those delays. The key to STM is the development of a method to define supply chain-specific minimum resource requirements such as time, inventory, transportation and correlate those requirements using an advanced algorithm based tool to potential cost avoidance.
Dr. David Nowicki, director of the Center for Logistics Education and Research at UNT, said the decoupling of information and physical delays lets organizations get actionable data faster, even in real time, and thereby make decisions faster.
There is an important distinction is between artificial delays and genuine delays, he noted. The artificial delays are those that can be proactively addressed when identified, and are often the result of policy decisions. For example, he said, if a decision has been made to only do inventory every two weeks, which affects available information and can contribute to a supply chain’s lack of speed.
Nowicki said it’s important to realize that any time a supply chain has a delay, it introduces volatility. “Those delays are cascaded and applied upstream into the supply chain,” he said. This adds variability to the supply chain process, he said, something that can be accounted for to a degree using estimated averages, as well as building resiliency into the supply chain.“ERP systems can capture and analyze standard deviations,” Nowicki explained.
The One Network Enterprises-sponsored research did not produce software, however. Rather, it’s a framework using algorithms that can be used as demonstration tool and not dependent on any single technology, said Nowicki. “What this tool allows you to is articulate benefits you can achieve,” he added. Nowicki said the big surprise is how well STM resonates with people when they see it.
STM is ultimately able to help One Network Enterprises quantify how it makes a difference, said Najmi, and the company has been using the research different ways, including illustrating the trade offs of putting money in place or another and identifying low hanging fruit in terms of driving improvements.
Najmi said the most significant – and pleasant – surprise was how remarkably close UNT’s findings and models could predict the benefits of reducing information delays with product flows. “We were surprised at how well the models were aligned with reality,” he said.
One specific example he cited was a consumer products studied by UNT, which was able to achieve an 80% reduction in bias by applying the STM. “This directly relates to bullwhip effect. When there are delays in information between customers and suppliers, and supplier’s suppliers, and so on, everybody is second guessing each other because they have to act and they have to run their business,” said Najmi. “They have to make decisions and they can’t wait for the order to drop when it finally comes.”
By second-guessing what’s going on, different players in the supply chain introduce bias. “This is how the bullwhip comes to bear,” he said. One Network Enterprises was able to cut out that bias by getting everyone on to what the company calls “single version of truth” through its platform. “Whenever companies take out latencies and unnecessary delays, they see tremendous value.”
UNT’s Nowicki said the underlying mathematics of the STM will be public domain, with one article already published and another pending. In the meantime, it’s included in a supply chain class as part of the university’s MBA program. “There's a lot that adds to the domain of knowledge from an academic perspective.”