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The Intelligent Automation Imperative: Taming Supply Chain Complexity

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The sheer volume of data can be overwhelming, but if leveraged effectively, all of this data can help tame today’s supply chain complexities – even turn supply chain into a competitive advantage.

Supply Chain Complexity

Supply Chain Complexity

Supply Chain Complexity- We create 2.5 quintillion bytes of data every day, according to Forbes. Global supply chains are responsible for a good portion of these data. Transactions in sourcing, manufacturing, logistics and distribution generate hundreds of millions of data points. This data volume is representative of today’s supply chain complexity – and it’s increasing. Thousands of SKUs, hundreds of transportation lanes and countless business partners must be managed in tandem to optimize business performance and serve the end-customer.

The sheer volume of data can be overwhelming, but if leveraged effectively, all of this data can help tame today’s supply chain complexities – even turn supply chain into a competitive advantage. It all depends upon how we use this data.

Intelligent Automation: Build the Digitization Puzzle One Piece at a Time

Supply chain and logistics leaders are investing in digitization and automation. The Intelligent Process Automation Market is expected to reach $8 billion by 2023, growing 40 percent, according to Market Research Future.

The challenge is that many leaders are thinking of digitization as a single step that will reap grandiose – and often unrealistic – results. Instead, think of digitization as a puzzle: each piece plays a critical role, but you can’t put the puzzle together in a single move. The puzzle looks nicer with every piece, but the final picture takes commitment, flexibility and patience.

Building an effective intelligent automation (IA) capability requires a step-by-step approach, with each step tied to tangible business outcomes.

Smart workflows facilitate human-machine interactions enabling real time visibility, tracking and proactive management of end-to-end processes. RPA automates repetitive, mundane tasks that are best suited for software. Machine learning and advanced analytics identify patterns and emulate logical thinking which provides insights and recommendations at the user’s fingertips.

With low cost, off-the-shelf technologies readily available to assemble the digitization puzzle the ball is now in the ‘business court.’ Supply chain leaders need to choose their use cases and crystalize the business impact.

Digitization Use Case: Logistics Exception Management

For example, let’s consider a common supply chain headache – logistics exception management. Importing a widget, drug or fruit that is produced abroad can face several exceptions, and a typical one is customs holds due to missing or incomplete documentation. Today a pack of documents needs to accompany a shipment for import clearance – documents can be in the box, on the box, sent electronically, handed to brokers, etc. There are several potential failures points that can quickly downstream operations.

At the simplest level, digitizing logistics transactions on an orchestration platform can make shipping documents available on-demand and can alleviate the need for paper-based processes. Supply chain partners can increase visibility throughout the product journey and can drive multi-party collaboration. Everyone can see what’s happening, in real time, which makes it easier to spot and resolve exceptions like missing information.

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Article Credit: SDC

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