There was massive disruption in supply chains worldwide due to the pandemic, which led to several enterprises turning to artificial intelligence (AI) for help – deemed as strategically senseless.
AI’s future cemented in the supply chain industry due to pandemic and it seems to be happening rapidly. Shippers are coming to terms with supply chain planning in the face of volatility and rapid swings in demand, data science and artificial intelligence (AI) are rapidly emerging as essential tools.
Business and supply chain leaders are examining their actions in reaction to the pandemic, many have concerns that AI adoption in the supply chain has accelerated way too fast. It is time to get strategic. Here’s a look at why it is crucial and what it means in terms of the future of AI in the supply chain.
As we know covid-19 hastened AI adoption by many leaders of supply chain.
Supply chains were depending on traditional models earlier. These models were using statistical and time-series methods to create data that could be used to predict supply needs or forecast demand. However, pandemic was not predicted. COVID-19’s upheavals turned these models unsuccessful and revealed a new need for supply chain agility.
Environments at supply chains are changing fast, requiring leaders to exhibit better level of adaptiveness and attention to detail.
Accepting AI by supply chains is imperative as speed and accuracy is required by firms in changing market dynamics. Companies will need AI technologies in order to make noteworthy, upgradable productivity profits and remain competitive.
AI also aids organizations to predict unforseen events and remain enterprising
Regulations needed to slow adoption
Experts feel that the pandemic is accelerating the pace of change. The retail disruption over the last year or so has equalled to about five years of normal disruption.
Retail business leaders feel that AI adoption is moving too fast. Their response is not surprising, considering retailers are digitally adapting more quickly than predicted.
There is an over-reliance on isolated tests that don’t aim for true applicability.
As per experts – most supply chain leaders are focusing on pilots rather than engineering for scalability. Many companies are like testing AI and ending up with multiple pilot projects that don’t provide a decent ROI.
Executives are required to understand how AI applications can create problems, including data quality issues that reduce accuracy. Work culture, governance and regulation surrounding AI and how those may create risks for companies are also critical concerns to address.
