By Lars Magnusson & Thomas Gaal
In 2017 in an earlier ASCM blog, we introduced the topic “The Internet of Supply Chains (IoSC), a Model in the Cloud” in an earlier blog. Digital readiness as a supply chain network participant, be it shipper, consignee, trucker, airport or handling agent doesn’t have a clear definition or destination any more than trying to define personal happiness. It is a Holy Grail we search for, but have difficulty defining. Global supply chain giants and digital evangelists have the resources and influence to plan, pilot, and execute projects to become digital ready, but small- to medium-sized companies and organizations simply cannot keep up. Here is the chicken and egg dilemma – similar to the creation and use of the original internet, the promise of digital supply networks doesn’t provide enough value to the participants until the network has reached a certain critical mass. This is part of the well-known network effect.
We need a new paradigm because the old “one power-player in a supply network” is a thing of the past. Forcing suppliers and service providers into multiple dedicated supply portals revolving around individual big players with incompatible data models is missing the core value of digitalization- to join a platform to become better, faster, cheaper in a value network that enables all the players in the network to become more responsive to each other’s’ needs.
Data Integration – Still the Problem and “Dumb Big Data”
Growing floods of data with different formats and meanings from many different, loosely-connected sources (from individual spreadsheets to ERP and all types of booking and handling systems) makes it difficult to quickly onboard a new supplier and exchange data beyond the scope of traditional EDI messages. We are drowning in “Big Data” that is by itself “dumb” until we solve the data integration problem across our entire supply network to result in “Smart Big Data”.
Smart Data – Data Integration – Start with Data Standardization
We all have a collection of data silos and spend far too much time trying to translate between them. The growth in conversations around the promise of data lakes is also not the answer. There are emerging digital technologies that show much greater promise, such as knowledge graphs that help databases evolve from simply storing data, organizing access, and providing reports to organizing data in relationships based on its meaning and the many ways multiple players in the network use it. This is the science of semantic data management. Every wonder how Facebook, LinkedIn, Google and Co. “link”, or “connect” you to friends, contacts, or the local restaurant that happens to match your interests? That all happens with data organized in a knowledge graph, not a legacy database. Reuse, repurposing, and sharing are greatly enhanced, as well as reliability and visibility.
Semantic data science acts as data glue linking data we consider as business concepts through a data model that is easily adaptable and based on common data definitions. It is based on the same logic we use in grammar. Example, Mark (person – subject) has a Date-of-Birth (property) of July 4 (a value). We use this logic in our natural language when constructing sentences to understand context. Our companies need to become more digital ready, but it takes the entire village to adopt the concept for us all to collectively reap the promised value.
Digital Readiness is Defined by Maturity in Using Data
As we start creating and using smarter data for more added value, we move from simple, incremental improvements to our current mode of operations to collaborating with partners in a value network, leading to an advanced environment where AI/Machine Learning algorithms can thrive and become digital partners for individuals, companies, and collaborators.
When we talk about the path to digital readiness, what do we really mean? We have to get pragmatic to ensure a focused business outcome. We are suggesting three levels of digital readiness that will also reflect your company’s individual journey on the digital maturity model:
Level 1of digital readiness reduces transaction effort by having data with common data formats, or standardized, but typically in just a traditional one-to-one or one-to-many relationship with a set of suppliers or customers.
Level 2 of digital readiness includes process automation, providing buyer/supplier order exchange and synchronized updates in product/process master data systems.
Level 3 of digital readiness is achieved when you have smart collaboration with customers and suppliers, defining and exchanging more and more data that has the semantic meanings and context information necessary for smarter systems to understand how to use it as part of automatic or semi-automatic transactions (e.g. tier 2/3 supply status, sensor data from IoT devices, etc.).
Digital Readiness – A tool for the Supply Chain practitioner
The pressure to become digital ready is growing. To prepare yourself, become familiar with ASCM’ comprehensive Supply Chain Operations Reference (SCOR) model and the SCOR “Racetrack”. As stated in the new digital best practice “SCM Object Synchronization – “3/4-way match”, your company’s processes need to have the ability to follow an object through the entire lifecycle across a supply chain as a foundational element in creation of visibility in order to gain supply chain control.
The focus is to create data object synchronization from Sales to Cash to enable systems integration & digitalization. There is also work underway to provide a more intelligent SCOR that provides the level of smart data and smart processes we have discussed in this blog.
The winners are the shippers, consignees, truckers, airports or handling agents that control their data to enable smart collaboration, aligned meaning and consistent provisioning of these data (objects) to all its stakeholders and systems with no degradation of the quality or concerns for security.
Sounds Good? What’s Next?
First, assess your organizations level of digital readiness as presented above. Chances are that different parts of the organization are at different levels of readiness. Run small-scoped proof of concept digital readiness workshops at your organization, using the SCOR project methodology to develop a holistic strategy to deliver short-, medium-, and long-term capabilities and deliverables. You can’t do it alone, so you must develop a path to move from level to level by redesigning your data and process flows to incorporate the semantic data definitions that help your company operate better with suppliers and customers as part of a larger Internet of Supply Chains (IoSC).