Supply Chain Disruptions – Is the Dynamic Unified Logical Data Model the Silver Bullet?

Enterprises have invested millions in supply chain software, customizations, and consulting firms in the past 2 decades. No one could respond to Covid-19 disruptions effectively and play the waiting game irrespective of the operational systems.

Enterprises are in the middle of the digital transformation dealing with one or more of these combinations such as Legacy Black Boxes reliant on reports and ETLs, Silo Systems bound by rigid integration layers, and Sophisticated Systems aimed for Functional Modules wrapped with APIs and Microservices.

All software systems are designed for specific business functions, modules to perform “predetermined” operations with known variables and implicit assumptions.  Even exceptions cases are predetermined with an intended outcome.  It is not designed to achieve data uniformity across the supply chain ecosystems or deal with unwarranted supply chain disruptions.

Businesses are restricted to carry out any new business cases for the below reasons: 

1. Salvaging the real-time data from operational systems is the first step of the hurdle, as we need specialized knowledge to understand each vendor’s system flows and architecture.  
2. If we consolidate the data, then the quality of data, the accuracy of data, and the interpretation of the data remain the major challenge. Without data insights, no business decision is accurate. 
3. Incorporating new business flows within the execution systems is another struggle as the original implementation may not have kept that in scope. Building them takes time and effort as it has to be taken under the change request stream. 
4. Bridging data back into upstream and downstream systems with their respective schemas states the 3rd hurdle.  

Building the data lake solves some of the problems, but it won’t solve the core issue. Data lake does have challenges around Data Latency, Rigid Schema, Restricted Insights, and Bi-directional orchestration.  

An Adaptive Unified Logical Data Model augmented with semantics creates a real-time master data lake with a deep business context. It allows businesses to gain real-time supply chain insights from customers, markets, partners, production, distribution, logistics, and environments. 

Unified data model converges the data points across the ecosystems beyond IT standardizations, functional modules, systems, and data types and presents the current state of a customer order, manufacturing schedule, truck stop, inventory positions, and whatnot. As a result, associates can inquire and make business decisions.  

For example, in a standard supply chain operation, if there are any mishaps in clearing the container from the port and transporting them to the predestined location, there is a ripple effect in entire supply chain operations. It impacts inventory allocation strategies, production schedule, customer promised orders, impaired product launches, financial losses.   Each of those business functions is tracked and managed by functional systems that expect predetermined inputs and outputs. Unfortunately, there is no supply chain cockpit to assess the level of impact and steer business outcomes from a single point.   

In the real world, a group of functional owners would come together to assess the impact on a case by case and deploy a workable solution that mostly executes alternative business actions that compromise cost or customer satisfaction.

Developing a dynamic, unified logical data model enables businesses with 360 degrees of their supply chain ecosystem, run what-if analysis and be ready for these unknown factors. As a result, they can pre-stitch few business responses ahead of time, preview their outcomes, and lock plausible options without compromising cost or customer satisfaction.

Do you have a supply chain cockpit that speaks the business language and understands business lingo? If not, it is time to think about a unified logical data model to overcome future supply chain disruptions.