How data-driven logistics can fortify supply chains

Published: 30 June 2020 - 8:06 a.m.

Recent events have brought into sharp focus the vulnerability of supply chains.

Much of that vulnerability relates to the lack of visibility of inventory, whether static or in-transit, and how it can be affected by factors that are typically beyond the scope of a company’s own supply chain.

Given this, the ability to effectively collaborate with the various stakeholders across the different tiers of the supply chain is becoming increasingly critical. Reliable, accurate data shared by – and visible to – all parties is key to improving results and cutting costs.

Logistics specifically is a matter of networks – including strong partners and startups, whether in business operations or in IT. Regardless of industry, companies are increasingly turning to emerging technologies like AI/ML, Blockchain and IoT to understand on an even more granular, real-time and physical level how their supply chain might be impacted by problems with their own operations, with their suppliers (and supplier’s suppliers) or with their customers (and customer’s customers).

I have been working with some of our partners recently on a variety of data-driven logistics use cases for some of the most innovative companies. These engagements have helped to derive a number of repeatable paths to value for our clients.

Let´s take a closer look at three areas of focus:

Supply Chain Resilience & Risk Management

Be it climate change, freak weather conditions, the growing number of physical attacks on trucks/vessels or the threat of severe cyberattack, all of these interruptions can jeopardize service and critical supply levels, cause loss of trust by customers, significant brand reputational damage, and incur major costs that impact bottom line performance.

Managing both mid-term supply planning and ongoing transport operations with the awareness of risks and safety incidents is becoming vital. Many firms have taken steps to digitize their supply chains - but they have not necessarily focused on the possibility to enhance supply chain visibility/traceability and resilience planning by incorporating external data.

“… This is why Oracle has been working, for example, alongside DHL Resilience360, a web-based Big Data Supply Chain Risk Management Solution, to provide logistics planners and customer service clerks using Oracle Transportation Management with the ability to:

- achieve enhanced visibility of potential risks on a global level to supply chain operations - react to near real-time incidents during both planning and execution, including the ability to re-plan shipments where necessary to avoid disruptions

- pro-actively inform end customers of potential delays and delivery disruptions, offering value-added information services, avoid loss of reputation and protect their company’ s top line.”

For some time DHL Supply Chain has been leveraging the integration of both solutions to offer their Lead Logistics Partner service to global shippers across multiple industries including Automotive and Life Sciences.

Track & Trace across multi-tier networks

Modern supply chains are incredibly complex, frequently involving stakeholders from within inter-connected networks that span multiple tiers and geographies.

Monitoring the transactions and the movement of assets or goods across organizations, detecting the conditions of products, and ensuring the validity of the pedigree, serialization and genealogy of product components has become increasingly challenging. The monolithic, centralized IT systems of the past are not best suited to coping with these distributed organisational structures and operations, which is why some companies are now turning to the architectural benefits of the distributed ledger that is inherent within blockchain.

Volvo Cars, for example, will become the first carmaker to implement global traceability of cobalt used in its batteries by applying blockchain technology.1

Technology firm Circulor and Oracle operate the blockchain technology across CATL’s supply chain, one of Volvo´s two global battery suppliers.

With the supply chain now visible, traceable and verified by blockchain, both Volvo Cars and their customers can be assured of the integrity of car parts, responsible sourcing and compliance with regulations.

Blending and filtering huge volumes of data for timely decision making

Adopting new technologies like blockchain and IoT is all well and good, but the inevitable by-product is that we are exponentially generating huge quantities of data. For supply chain practitioners it is becoming an increasing struggle to harvest, cleanse, analyse and interpret this mountain of data within a short enough time horizon to make the resulting conclusions meaningful and any recommended outcomes actionable.

Thus it has become crucial to find the appropriate balance between machine and human intelligence in order to maximize the value that new enabling technologies offer in day-to-day supply chain operations, with companies increasingly turning to machine learning and AI to enhance the decision-making process.

Take, for example, consumer goods giant Unilever, who spoke recently about how they are leveraging cloud, IoT and AI in order to take data-driven decisions. They are combining their internal data from transportation planning with their external data from track and trace to deliver real time visibility and analytics, whilst utilizing IoT and Machine Learning to drive better predictive maintenance.

Setting the course

Supply chains and logistics processes are destined to become more customer-centric, more self-aware, more swarming, more autonomous and certainly more resilient. At the same time, however, they will be vulnerable to an ever greater variety of risks.

As manufacturing operations fire back into life, and supply chain operations gradually return to normality, companies are being presented with a unique situation in which they can evaluate their supply chain process and put in place the technological foundations that will let them sustain and harvest meaningful benefits in the mid- and long-term.

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