With COVID-19 exposing a number of vulnerabilities in today’s supply chains, companies affected by unexpected disruptions and shortages caused by the pandemic are racing to digitise their supply chain and logistics, future-proofing their organisations.
To achieve this, one key investment area is data and analytics, which is currently expected to have a market growth of US$5.5bn to US$8.9bn by 2025.
“COVID-19 has given the world a sharp reminder that manufacturing and their enabling supply chain ecosystems remain the most real and significant force in the global economy,” explains Felipe Bezamat, Head of Advanced Manufacturing Industry and Memia Fendri, Project Specialist, Advanced Manufacturing at the World Economic Forum.
“Emerging from the crisis, companies will need more resilient supply systems to prepare for future shocks as well as higher productivity in their operations to free up liquidity for future investments.”
Different Application Types of Big Data and Analytics
The World Economic Forum categories the different application types of Big Data and analytics into three main areas:
- - Productivity increase in manufacturing and supply systems
- - Enhanced customer experience through improved products and services
- - Positive impact on society and the environment
“Depending on the type of application and enterprise, companies can unlock value from many applications themselves using only internal data. Such applications include tracking and tracing within a factory or navigation of automated guided vehicles used in logistics,” says Bezamat and Fendri.
“However, to unlock the full potentials, it is often required the exchange of data beyond company boundaries to effectively train artificial intelligence (AI) algorithms and to support collaboration in complex networks that require full transparency. Yet, several challenges prevent companies from sharing data with one another and building effective data ecosystems. At the World Economic Forum’s Platform for Shaping the Future of Advanced Manufacturing and Production, we look after identifying these value opportunities and overcome roadblocks to maximise value from data and analytics,” adds Bezamat and Fendri.
Gaining a Competitive Edge and Future-Proofing the Supply Chain with Big Data and Analytics
In the supply chain and logistics environment, there are many applications of Big Data and Analytics that provide organisations with a competitive advantage. Bezamat and Fendri explain that “end‑to‑end data flows enable the tracking and tracing of material throughout the supply chain and inventory reduction, as well as facilitating enhanced sales and operations planning. Moreover, it could allow data‑driven supply chain risk management and the simulation of quick re‑configuration scenarios.”
“Beyond productivity improvements, data and analytics applications allow companies to enhance the customer experience such as just‑in‑time delivery of critical goods. More accurate sales and operations planning as well as real‑time tracking and tracing of critical materials (for example, medical goods or perishable food) prevent stock‑outs and improve availability for customers,” continue Bezamat and Fendri.
While many Big Data and Analytics applications can be implemented on their own, those looking to future-proof their supply chain and logistics operations should consider collaboration with external partners in order to reach their full potential.
“Data collaborations along supply chains have, for instance, allowed manufacturers to gain visibility on their supply chains and integrate their suppliers, exchanging information on inventory, capacity or shipment status. Using advanced analytics, they have then managed to better integrate supply and demand,” explains Bezamat and Fendri.
“For this, organisations need to assess their maturity in implementing applications and technological and organisational enablers. A community of companies hosted by the World Economic Forum’s Platform for Shaping the Future of Advanced Manufacturing and Production have developed the Manufacturing Data Excellence Framework to compare their individual maturity versus the benchmark and define their individual target state. Companies are currently using the framework to forge new partnerships to develop applications or advance the maturity of critical enablers,” adds Bezamat and Fendri.
Challenges Experienced by Supply Chains When Adopting Big Data and Analytics Capabilities
When it comes to the adoption of Big Data and analytics, organisations usually require a complete, end-to-end data set that covers the entire supply chain or that provides visibility of an entire process. It is also important that this data set is large enough to drive valid insights in order to establish statistically significant correlations to train artificial intelligence (AI).
“As a consequence, data from external partners plays a critical role. This makes it essential for manufacturers to build an ecosystem in which companies can collaborate to exchange data within a common data ecosystem that provides a single source of truth for promoting mutual understanding and collaboration. Supply chains can overcome these challenges by focusing on the data excellence organisational and technological priorities outlined below,” says Bezamat and Fendri.
- - Define a data‑to‑value strategy and roadmap
- - Incentivise internal and external ecosystem partners
- - Build capabilities to capture and use data
- - Implement an open platform to unlock data silos
- - Enable connectivity for low‑latency, high‑bandwidth data flows
- - Ensure data security and privacy
Specifically, when it comes to using technology to overcome adoption challenges, Bezamat and Fendri explain that “the key is finding the right applications where collaborations around data and analytics offer clear benefits, and aligning on a data to value strategy and roadmap.”
“Pioneering technologies are then essential to generate insights and value from data. They first allow supply chains to boost data extraction through physical acquisition and access to different databases and systems. Cloud, for instance, allows supply chains to unlock data silos, combining data from different sources and systems to increase and enhance the overall data pool. Automation, as well as artificial intelligence algorithms, are then needed to make sense of the enhanced data pools. Finally, technology allows supply chains to ensure privacy and security when dealing with personal or competitive data,” concludes Bezamat and Fendri.