Using AI to Overcome the Great Supply Chain Disruption

2022-10-24 02:27:58
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The increasing mayhem at ports with no end in sight has caused panic in the business world. It has become a big issue, and the world faces a great supply chain disruption. The inability to supply products on time creates chaos for both customers and manufacturers. Due to supply chain disruption, companies often face spoilage of perishable products, reduction in demand, and non-returning customers. The supply chain issue is not limited to a single sector but is blanketing almost every industry. Let’s look at how artificial intelligence can be used to start tackling these supply chain issues.



Behind the Disruption

Could it be the lack of truck drivers? No, we cannot blame one single thing for the disruption. Issues like lack of advanced technology, real-time data availability, and hesitation toward adopting new technologies contribute to this issue.

The reason behind the emerging challenges in the supply chain is that current inventory and planning systems run on fixed lead times and demand forecasting, whereas the real world functions on dynamic lead times. It results in poor decision-making and bad planning by the procurement leaders and financial executives, ultimately causing port congestion. Leaders must withhold planning initiatives and vigorously manage their shipments to correct this.

Whenever there is a change in transportation medium while shipping goods, long queues emerge, adding to the problem. While it might appear that a new means of transportation can relieve congestion, this is not a real and practical solution. Therefore, choke points cannot flourish without a substantial investment so the port infrastructure limitations are repaired.

Planning Shipments Accurately

Retailers require real-time inventory visibility across their enterprises to plan more precisely. Generally, stowage plan information is shared with terminal and third-party logistics companies exiting the gate as one value chain. It enhances the efficiency of the first-in, first-out process. Artificial Intelligence can support the supply chain in determining changes in transportation or routes early to ensure on-time and seamless delivery of critical items.

Despite AI implementation being new to supply chain management, early adopters are already leveraging this technology. As per McKinsey & Co., companies embracing AI-enabled supply chain management saw improved logistics costs by 15 percent and inventory levels by 35 percent. As AI technology grows, more companies are attracted to it to churn immense benefits from its potential. Therefore, AI in the logistics and supply chain markets is predicted to expand at a compound annual growth rate of around 42.9 percent between 2017-2023.

Use Cases for AI in the Supply Chain

With the increasing popularity of AI, there is a great chance that it can enhance and make the supply chain process seamless. Let’s take a look at some critical use cases:

#1: Shipment Prediction

Customers expect to receive their ordered goods in a few days. Nevertheless, World Economic Forum data reveals that delivery times within the U.S. and Europe will continue to rise. Moreover, the current environment shows us that increased time frames will remain part of the future. In fact, amid unforeseen circumstances like a natural disaster or poor weather, customers expect that companies have a backup for these situations and deliver their orders on time. AI can assist companies in predicting on-time, in-full drops early using past data to know the way vendors fulfill orders. It permits companies to establish deadlines to switch modes of transportation for customers who create the most significant profit margins. In addition, AI also offers full visibility of materials across the entire value chain, making it easy to find and eliminate bottlenecks promptly.

#2: Deprioritize High-Cost Customers

Garner forecasts that 75 percent of enterprises will drop poor-fit customers by 2025. However, some companies might be unable to discontinue the relationship with costly clients. These loss leaders should not be part of their priority lists. It can appear as a big challenge for businesses to detect these customers. With sorting algorithms, artificial intelligence can automatically identify customers at scale who are not good enough for market-share gains and drain prized capacity. Further, AI can find new opportunities for improvement and discover how these opportunities will influence the bottom line.

Without knowing consumer demand, companies often risk selling products that do not show much demand, costing millions of dollars in loss. AI-driven forecasting can support companies in detecting demand changes as soon as possible, permitting them to optimize products for the best profit margins.

#3: Increase Profit Margins

AI-powered supply chain management offers a 65 percent reduction in lost sales caused by out-of-stock products. On the sales side, AI can support the sales team in identifying upsell and cross-sell opportunities for key accounts. Often, companies have very less knowledge of whom they should be upselling. Nevertheless, the sales team continuously gathers data because most sales tasks occur digitally. AI can use this information to support teams selling more efficiently.

#4: Faster Shipping

In a survey conducted by Convey, 28.6 percent of consumers shared that they like to place an order with companies that can deliver products as soon as possible or within a week of the order. This is a really small window of time, which means faster shipping is important if companies want to attract customers. In this instance, AI can also find shippers who slow down the supply chain. Once detected, companies can remove the players who cannot keep pace and replace them with others who are more efficient. Similarly, suppliers can also use AI to create simulations based on bottlenecks and disruptions.  Once the AI identifies that the specific portion of the supply chain is bottlenecked, it can predict when companies can expect a shortage based on inventory stock levels or extending lead times.

When Will We Resolve the Disruption?

It might take years to resolve the Great Supply Chain Disruption. If businesses wish to deliver products seamlessly, they must change their plan. Adopting artificial intelligence technology will equip companies with the important information required to ease today’s supply chain challenges.



参考译文
利用人工智能克服巨大的供应链中断
港口混乱局面持续加剧,引发了商业界的恐慌。这一问题已变得十分严重,全球正面临巨大的供应链中断。无法及时供货给顾客和制造商都带来了混乱。由于供应链中断,公司经常面临易腐产品的腐败、需求的减少以及顾客的流失。供应链问题并不仅限于某个单一行业,而是几乎波及所有领域。让我们看看人工智能如何开始应对这些供应链问题。**混乱背后的原因** 是由于缺乏卡车司机吗?不,我们不能将混乱归咎于单一因素。缺乏先进技术、实时数据的可用性以及对新技术的犹豫态度,都是这一问题的诱因。供应链中新兴挑战的根源在于,目前的库存和计划系统是基于固定的交货时间和需求预测,而现实世界却是基于动态交货时间运行的。这导致了采购主管和财务高管做出的决策和计划不佳,最终引起港口拥堵。领导者必须暂停计划工作,并积极管理其货物运输,以纠正这一问题。 每当货物运输过程中更换运输方式时,长长的人流就会出现,进一步加剧问题。虽然看起来新的运输方式似乎可以缓解拥堵,但这并不是一个真正且实际的解决方案。因此,如果没有大量的投资来修复港口基础设施的限制,瓶颈问题就无法解决。 **精确地规划运输** 零售商需要在其企业内部实现实时的库存可见性,以便更精确地进行规划。通常,装载计划信息在货物离开闸门时与码头和第三方物流公司共享,形成一个价值链。这有助于提升“先进先出”流程的效率。人工智能可以协助供应链在运输或路线发生变化时及早识别,以确保关键物品准时无缝地交付。 尽管人工智能在供应链管理中的应用尚属新兴,但早期采用者已经开始利用这项技术。根据麦肯锡公司的数据,采用人工智能供应链管理的公司已实现物流成本降低15%、库存水平下降35%。随着人工智能技术的发展,越来越多公司被其潜在优势所吸引。因此,预计人工智能在物流和供应链市场的年复合增长率将在2017-2023年期间达到约42.9%。 **人工智能在供应链中的应用案例** 随着人工智能的日益普及,它极有可能增强并使供应链流程无缝衔接。让我们看看一些关键应用案例: **#1:运输预测** 顾客期望在几天内收到他们的订单货物。然而,世界经济论坛数据显示,美国和欧洲的交付时间将继续延长。此外,当前的环境表明,较长的交付时间将成为未来的一部分。事实上,在自然灾害或恶劣天气等不可预见的情况下,顾客期望企业能够应对这些情况并及时交付订单。人工智能可以帮助公司利用历史数据尽早预测准时、完整交付,了解供应商是如何履约的。它使公司能够为利润最高的客户提供运输方式的转换截止日期。此外,人工智能还能提供整个价值链中材料的全面可见性,使企业可以轻松找到并迅速消除瓶颈。 **#2:减少高成本客户优先级** Garner预测,到2025年,75%的企业将放弃不适合其业务的客户。然而,一些公司可能无法与这些高成本客户断绝关系。这些客户不应被列入优先级名单。对于企业来说,识别这些客户可能是一个巨大的挑战。借助排序算法,人工智能可以在大规模范围内自动识别出那些对市场份额增长无益且消耗宝贵产能的客户。此外,人工智能还能发现新的改进机会,并评估这些机会对公司利润的影响。 在不了解消费者需求的情况下,公司往往会冒险销售缺乏需求的产品,造成数百万美元的损失。由人工智能驱动的需求预测可以帮助公司尽早识别需求的变化,使他们能够优化产品以获得最佳利润。 **#3:提高利润率** 由人工智能驱动的供应链管理可减少因缺货而导致的销售额损失达65%。在销售方面,人工智能可以协助销售团队识别关键客户中的追加销售和交叉销售机会。通常,公司对于应向谁进行交叉销售了解甚少。然而,销售团队不断收集数据,因为大多数销售任务都是数字化的。人工智能可以利用这些信息来支持团队更高效地销售。 **#4:更快的配送** Convey 的一项调查发现,28.6%的消费者表示,他们更喜欢选择能尽快或在一周内配送商品的公司。这时间窗口非常短,意味着如果企业想要吸引客户,就必须实现更快的配送。在这种情况下,人工智能也可以识别出拖慢供应链的承运商。一旦发现,企业可以淘汰无法跟上节奏的承运商,并用更高效的承运商取代他们。同样,供应商也可以使用人工智能,根据瓶颈和中断进行模拟。一旦人工智能识别出供应链中的特定瓶颈部分,它就可以根据库存水平或延长的交货时间预测公司何时会出现短缺。 **我们何时才能解决这场混乱?** 要解决这场巨大的供应链混乱可能需要数年时间。如果企业希望无缝地交付产品,就必须改变他们的计划。采用人工智能技术将为公司提供解决当今供应链挑战所需的重要信息。
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