In particular, Covid-19 has made these challenges all the more prominent, with unprecedented pressure on the supply chain after lockdowns and varying restrictions imposed by different countries around the world. Businesses within the supply chain must be resilient and adaptable as the combination of changes that are underway, such as increased globalisation, digitalisation, and driver and other skill shortages, have increased the industry's complexity. While Covid-19 restrictions have eased and many countries are learning to live with the virus, the supply chain crisis isn't going away. Political unrest has hampered the movement of products and services worldwide, notably to and from China and, more recently, Russia.
Artificial intelligence (AI) has been cited as a solution to some of the problems businesses within the supply chain. Over half (53%) of UK supply chain decision-makers believe AI advances are crucial to managing disruption. On the finance side, technologies such as AI are being used by innovative companies to better understand their capital through data analytics and performance insights so they can meet their goals through effective financial management. However, data and the overarching strategy must be in the right state to effectively utilise AI, analytics, and data science.
AI can be embedded into your data platform – it enables you to use predictive analytics to get better insights into all levels of the supply chain – an improved understanding of demand fluctuations and their effect throughout the supply chain. AI data models can help deliver competitive advantage, improve financials and help businesses gain control across many areas. Implementing a big data platform is critical to get insights in real-time or daily. With so much data at hand, the platform must be scalable to ensure success.
This requires breaking down data silos, joining data across the organisation, and using modern advanced analytics in a performant, scalable, and cost-effective data platform with data governance in place.
With AI and machine learning systems in place, historical data can be used to determine the effects of various occurrences, such as wars, natural disasters, significant price swings, and similar events. Then, based on previous data, they offer predictions about what is likely to occur in the future. These forecasts provide another layer of knowledge, which can help the algorithms find more optimal solutions within the chain management decision-making process.
Logistics companies already have excellent conventional data warehouses (ERP, WMS, FP&A). But, modern data warehouses have massively scalable new sources of data and analytic methods, for example:
The effect on energy usage when processes in the supply chain go wrong and are delayed is a new consideration for many businesses operating in the current climate of high energy costs and scarce energy supplies. The embedded carbon in a product can be analysed through environmentally extended input-output (EEIO) analysis, an environmental accounting, production, and consumption structure. As such, it is becoming an essential addition to material flow accounting and, alongside the AI-driven solutions discussed, may help with the challenges faced by businesses within the supply chain.
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.
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