Supply chain disruptions are inevitable. Unpredictable consumer behavior, traffic patterns, port behavior, severe weather, natural disasters and labor unrest are all examples of external events that can cause supply chain disruptions that lead to increased costs and customer service challenges. Traditional supply chain visibility solutions, reliant on latent EDI-based status updates, alert users to disruptions after the fact, drastically limiting a company’s ability remediate issues quickly before problems worsen. This results in lower customer service levels, increased expedited freight, lower margins and the need for more buffer stock. And while unplanned disruptions will always occur, the emergence of newer technologies provides the ability to predict potential future disruptions and act on them accordingly, moving to a more proactive view of visibility.
In part 1 of this 3-part blog series leading up to JDA FOCUS 2017, learn why JDA partnered with TransVoyant to address these market challenges.
A Partnership that Drives Value-Driven Global Visibility
TransVoyant is at the forefront of the predictive analytics space. The company collects over one trillion global events each day through sensors, satellites, radar, video cameras, smartphones and other devices that make up the Internet of Things (IoT), giving them one of the largest repositories of real-time big data in the world. Their proprietary machine learning algorithms analyze these massive big data streams in real-time to produce live and predictive insights that help companies achieve competitive advantage.
TransVoyant has become a key part of JDA’s innovation strategy due to its ability to take large streams of data, analyze it and discover new insights and opportunities. TransVoyant’s ability to provide new and powerful insights, by factoring the impact of weather, news, local events, social media and natural disaster data, helps improve demand-supply balancing and avoid disruptions to the supply chain.
JDA and TransVoyant Partnership Benefits
The partnership between JDA and TransVoyant combines best in class supply chain planning and execution with real-time visibility and predictive analytics. The result is a more dynamic supply chain that enables organizations to make more active, dynamic decisions that reduce network latency, shorten cycle times and protect profit margins.
The initial phase of the partnership has focused primarily on transportation, providing accurate predicted times of arrival and optimal carrier and routing recommendations factoring the impact of external events, as well as alerts related to orders at risk due to upstream supplier issues. This approach merges big data and prescriptive insights using a combination of advanced processing power and algorithms and looks to formulate and communicate predictive, rather than reactive states.
The next phase of the partnership will move upstream from transportation to the areas of forecasting and replenishment, enabling customers to not only predict future disruptions, but also to synchronize planning resources and inventory with market and supplier activity. This will result in improved return on inventory, on-time performance and effective inventory movement. Early visibility and prescriptive responses to supply related disruptions and fluctuating demand trends gives organizations the ability to pivot and plan production and distribution more effectively. This increased visibility and responsiveness leads to fewer lost sales and lower logistics costs associated with expediting or transferring product.
The next two blogs in the series will discuss the benefits of each solution in detail. In the meantime, you can learn more about the partnership between JDA and TransVoyant here.
Are you attending FOCUS? If so, check out JDA and TransVoyant to see how you can improve new product launches with real-time visibility to the market response through data, such as social media buzz, news, local events and more. Additionally, find out how to plan labor and make more effective deployment decisions by spotting and highlighting short-term changes in demand that are associated with local events, traffic, weather and other important data.