Digital transformation is an exciting phase in the evolution of business automation and value creation enabled through enhancements in mathematics, software and hardware. It’s all about further extending information, intelligence and automation to drive value. Traditionally, companies used latent technologies like Electronic Data Interchange (EDI), along with legacy supplier and customer collaboration platforms, based on static or latent data, to drive value. These solutions can improve collaboration and shorten decision cycle times, but they produce limited long-term business gains as they are only as good as the accuracy and timeliness of the data and analytics. In other words, many of these traditional approaches follow the axiom of “latent, inaccurate data inputs lead to slow and bad decision output”.
As technology advances, big data, machine learning and artificial intelligence have become key capabilities enabling digital transformation. These transformational capabilities are moving enterprises to real-time decision-making and giving them an understanding of continuous global business behavior throughout their digital enterprise. Leveraging external and ecosystem data to support continuous behavioral analytics is a key aspect of digital transformation. These external conditions and behaviors impact desired plans and operations. External and ecosystem behavior is required to create the right optimization and fault tolerance in global company operations. Behavior driven digital transformation creates a digital supply chain which runs on real-time information. A supply chain that is able to see and communicate with every other actor or node, sense and respond to changes, predict and avoid disruptions and exploit global opportunities. The goal is clear – digital enterprises must see behavior and predict the world around them to eliminate problems, create continuous efficiencies, take advantage of living opportunities and innovate for greater customer and shareholder value.
However, enabling a digital supply chain does not mean all decisions and actions will be 100% automated. Supply chain professionals will still need to determine which decisions in their global supply chain network can be made “machine to machine” versus which aspects still need to be handled by a human. A key value of a digital supply chain is the ability to analyze massive amounts of real-time data quickly and to apply advanced machine learning algorithms to those data streams to create intelligent insights and optimal decisions. Keep in mind a digital supply chain for each company differs based on the processes and approaches their business traditionally has taken within their world.
Many companies begin to digitize and then transform their processes, based on the technology and data sources they have to leverage (internal, ecosystem and external data). For example, some companies start their digital journey in the supply chain by tracking the real-time behavior and location of multimodal shipments, calculating accurate times of arrival, predicting delays or disruptions and more accurately promising orders to customers. Once this first transformation is made, these companies move to the next digital enterprise process improvement initiative. A next step is often the transformation of manufacturing processes with new machines, data and automation that expedite workflows and create operational efficiency beyond traditional/manual (human) process. Once this second initiative is in place, companies realize they understand and can predict “in node” and “between node” behavior continuously across their global enterprise and ecosystem. This realization leads to rapid digitization and accurate optimization across the most complex global value chains.
For more information on the digital supply chain read our white paper The Digital Supply Chain: The Future has arrived. If you would like to speak with someone about your digital supply chain strategies please email firstname.lastname@example.org.