Digitisation Transforming the Supply Chain, Reinforcing Sustainability and Compliance

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Thank you to Tom Augenthaler for his help with this post.

Emerging technologies, such as blockchain and artificial intelligence (AI), are playing a pivotal role in transforming the supply chain. By changing how businesses engage in commerce and collaboration, these innovations are allowing for possibilities that previously would have been scarcely imagined. And the impact that AI is having on business is enormous. 

According to PwC, AI could contribute up to $15.7 trillion to the global economy in the next 11 years. Furthermore, a recent study by McKinsey suggests that supply chain management is one of the top three industries standing to benefit most from AI applications.

Blockchain, AI and other cloud-based technologies form a significant basis of what is known as Industry 4.0, referring to the many ways digitisation is transforming the manufacturing sector. In a “smart factory,” a connected system visualises the entire production chain, allowing the most practical actions to be taken independent of any human intervention. This transition is so impactful that it is considered to be the fourth revolution in manufacturing, unleashing major shifts in the economy and helping to optimise the many benefits of computerisation that Industry 3.0 ushered in previously.

The emergence of Industry 4.0 means that computers connected to the cloud are now capable of making decisions and communicating with each other without human involvement — and the more data these systems are given access to, the smarter their decisions will be. As these decisions become smarter, manufacturing processes benefit from increased efficiency and productivity and can be carried out in ways that reduce waste.

Time, labour, money, fuel, and valuable raw materials can all easily go to waste when a supply chain has not been optimised for maximum efficiency. Perishable items can rot before they reach their intended location; ventures can stall and end up faltering; and inventories can accumulate to excess, contributing to negative environmental impact.

What are the beneficial effects of Industry 4.0?

Industry 4.0 can reduce or prevent such outcomes. But it can also have other beneficial effects, such as when AI enables robots or computers to perform tasks that may be otherwise dangerous for humans. This is important in procurement since organizations are looking more closely at their own supply chain in order to become circumspect in their own conduct. Robots can be used to organise and collect items in warehouses or take inventory of large collections. In general, a typical supply chain will involve an amount of data so enormous that AI would be capable of handling it much more efficiently than even the most capable of humans — and it can simultaneously clean and track the data, check for any anomalies, and make predictions predicated on the real-time flow of information not only from an organisation’s own operations but from those of its trading partners as well.

What does this mean for diversity initiatives?

In the process, AI helps to meet customer demand as well. Companies such as Uber and Amazon make use of this type of technology in order to minimise delays, reduce costs, and generally make the product-to-customer process frictionless and fast. Customers are becoming so accustomed to this convenience that it is becoming difficult for businesses that have yet to implement digital technologies to keep up.

At the same time, customers are seeing the businesses across an array of industries harness the power of digitisation to advance diversity and inclusion. When it comes to using AI to encourage inclusion and diversity, data is at the heart of the issue. Diversity needs to be built into Industry 4.0 by drawing upon the appropriate data to train AI systems to function without bias. Data that promotes diversity and inclusion will help develop an AI system that is inclusive and diverse, much in the same way that data tainted by bias perpetuates harmful outcomes. These need to be actively unlearned by those inputting the data, as many societal biases are ingrained and unconscious.

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