AI is assisting the sector in the planning and optimization of operations. COVID-19 has proved the importance of planning forward and anticipating supply chain challenges. Artificial intelligence can be used to evaluate supply chain risks and identify possible bottlenecks.
Supply chains have become significantly more challenging to manage in recent years. Physical flows are becoming longer and more interconnected as product portfolios become more sophisticated. Market volatility has increased the demand for agility and adaptability, which the pandemic has worsened.
The supply chain has invariably been intricate and tough to control. However, since the outbreak, everything has become exponentially more difficult, as it has ushered in a slew of new obstacles.
Let’s take a gander at some of the AI supply chain difficulties.
AI systems are typically cloud-based, which necessitates a large amount of bandwidth to power the system. Operators may also require specialized hardware in order to access AI capabilities, and the cost of this AI-specific hardware can be too expensive for many supply chain partners.
Artificial intelligence, like other computational processes, requires excellent data. In order to train algorithms and construct prediction models, Machine Learning (ML) in particular necessitates massive amounts of precise data. However, most businesses lack the necessary data, both in terms of quality and quantity.
Enterprises must increase the quality of their data by implementing good master data management and integrating real-time data as much as possible into processes and systems.
While continuously syncing external systems, real-time, multi-party digital business networks maintain a “single version of the truth,” ensuring that businesses operate on the most available information.
Also Read: Leveraging AI to Optimize End-to-End Supply Chain Management
Solutions with pre-trained, ML-based algorithms that rely on vast volumes of data from similar scenarios and corporations should also be considered. Digital business networks may swiftly refine well-trained algorithms and intelligent agents that new members of the network can exploit due to their large volumes of transactions.
An AI-controlled machine has a complicated network of individual processors that require regular maintenance and substitute. The problem is that the operating investment might be rather large due to the potential cost and energy involved. Manufacturers would also have to replace them, which would raise the cost of utility bills and directly influence the operating costs.
Every procedure and change has a price tag. When it’s not taken into account when making decisions, the result might sometimes be worse than if nothing was done at all. It’s simple to lose sight of the long-term repercussions of action in supply chains with multiple partners and systems. Many approaches make this mistake by re-planning the entire supply chain, causing “nervousness” in the system and causing significant and unneeded change and expenses when a minor or more local adjustment would suffice.
To mitigate this issue, optimizations should be ongoing rather than one-time, and they should be limited to the smallest number of entities possible to minimize network disruption. These minor modifications build up to huge gains without generating a ripple effect throughout the supply chain.
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Umme Sutarwala is a Global News Correspondent with OnDot Media. She is a media graduate with 2+ years of experience in content creation and management. Previously, she has worked with MNCs in the E-commerce and Finance domain
