Table of Contents
Introduction: State of the Industry
State of Analytics: Defining End to End Transparency (below)
State of Automation: Digitization is Not Digitalization
Today’s supply chain leaders have a problem. They are drowning in data, yet low on insights. In short, they cannot see. Data is everywhere, but not yet in useable formats.
The evolution of supply chain analytics is an exciting opportunity for business leaders. Traditional approaches focus on building analytics as an extension of existing enterprise applications: advanced planning, customer relationship management, enterprise resource planning, supplier relationship management, transportation management and warehouse management. New analytics approaches are designed based on data flows and requirements.
In this new world, data pools and streams inform business teams using new forms of analytics. The analytics infrastructure synchronizes, harmonizes and visualizes data flows. The analytics layer sits between the traditional application layer and workforce productivity.
These advancements are happening at both the enterprise and inter-enterprise level. The significant advances are being made in the following areas:
Cognitive Computing: Within five years, decision support technologies like supply chain planning, revenue management and sourcing will be transformed through the use of cognitive computing. This new approach will allow teams to drive new business value through semantic reasoning. The challenge for most teams will be trusting new forms of analytics. Companies will have to divorce themselves from spreadsheets.
Open Source Analytics: When e-commerce providers could not scale on relational database technologies, they evolved open source capabilities on Hadoop. Massive parallel processing enables schema on read handling and greater flexibility in the design of analytics. Instead of being stuck with inflexible hierarchies, schema on read enables the building of hierarchies and relationships by reading the data. This allows capabilities to evolve.
Machine Learning: Companies are plagued by disparate data. The view is that it’s “dirty” data. The reality is that most silos within an organization need data with a different context. But the traditional approach of hand-coding master data is expensive and outdated. The use of machine learning facilitates the read of data when needed and the shaping of data to the business context. Machine learning will transform master data processes within two years.
Streaming Data Architectures: Today’s businesses operate on batch processes and latent data. With the evolution of the Internet of Things and advanced sensors, new processes will evolve to facilitate decisions at the speed of business. This includes redefining replenishment from the outside in, redefining digital manufacturing to transform maintenance, and building new processes for service of heavy equipment, utilities, and asset-intensive operations.
Unstructured Data Mining: Within the organization, 70% of data is unstructured. This data is essential for understanding consumer sentiment, warranty and quality data. Mining this unstructured data enables its use for building a better understanding of how consumers view their products based on consumption patterns.
These advances are not an extension of existing processes and technologies. Testing and learning is required to understand the value and limitations of these technologies, which then will drive the design and implementation of new processes.
As shown in Figure 2, the evolution will be based on a confluence of new technologies. The continued dependency on spreadsheets is limiting the evolution of supply chain visibility (see Figure 3).
Electronic data interchange is the backbone of visibility today, but the future will change dramatically through blockchain.
How so? There is a movement to multi-tier lineage and supply chain finance. While blockchain is not the replacement for integration from one company to another, it is useful for the transformation of multi-tier processes. Why is this important?
Outsourcing is a reality. For the average company, outsourcing of manufacturing and transportation is a certainty. Over 90% of companies are involved in outsourcing: 30% outsource 40% or more of their manufacturing, and 55% outsource at least 40% of their logistics on a volume basis.
Supply chain visibility has many forms. But few are being delivered well. Visibility within the company is being addressed by current IT architectures, but business-to-business architectures that support emerging supply chain visibility requirements are evolving. Blockchain is promising to improve track and trace, supply chain finance, and product lineage.
The gaps in supply chain visibility are large. Satisfaction with EDI is high, but it’s brittle. The average company has seven different ERP instances; 49% of companies report that ERP spending plays a major role in their IT budgets. The gaps for supply chain visibility are high, and the answer will not come from ERP. Improving visibility is critical as supply chains grow more complex. Leaders will pave a new path using blockchain.
To improve transparency through analytics, here are three steps to take:
1. Define Priorities and Align Solutions. It is important for companies to document requirements for supply chain visibility for transportation, sourcing and manufacturing. This includes planning and unstructured data.
2. Get Clear on What You Are Doing Today. Document the “As Is” and the “To Be” states. The goal is to have transactions flow hands-free and to have the right data for the right people chain when they need it.
3. Align IT Strategies with Future Goals. Line of business leaders need to work with IT to align IT spending and future plans. Rationalize ERP spending, maximize private networks and qualify new public visibility solutions.
Companies have to “unlearn” the past to rethink how to use new technologies to improve transparency. It requires nothing less than a paradigm shift.
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