Trends last for years. But how often do significant new things happen? In most years, there is nothing new in supply chain management. In the past year, we have seen two genuinely new developments.
The US Customs and Border Protection Agency’s stringent enforcement of the Uyghur Forced Labor Protection Act has importers concerned. The UFLPA prohibits goods mined, produced, or manufactured wholly or in part in Xinjiang from entering the U.S. This act is in response to the People’s Republic of China arbitrarily detaining more than one million Uyghurs and other Muslim minorities in China’s Xinjiang Uyghur Autonomous Region. The US Department of Labor reports that 100,000 Uyghurs and other ethnic minority ex-detainees in China may be working in conditions of forced labor following detention in re-education camps.
CBP statistics show that more than half a billion dollars of goods presumed to have been made by Uyghur slave labor have been detained and prohibited from entering the country since active enforcement of the UFLPA law began on June 21st of last year.
The detentions have occurred across a variety of industries. But electronics; industrial and manufactured; and apparel, footwear, and textiles make up the bulk of products detained. But forced labor is even being used in the Chinese sea fleet and seafood detentions are occurring as well.
Surprisingly, most of the goods seized did not originate in China but rather in Malaysia and Vietnam. In the case of goods detained from Malaysia, manufacturers in those countries were producing goods often made with ores that were mined and refined in Xinjiang. In the case of Vietnam, it was more likely that Vietnamese companies were making apparel with cotton grown or processed into fabric in Xinjiang.
Outside of the U.S., several countries have enacted legislation to ban products made with forced labor. However, these regulations don’t have the same bite as the US law. What gives the US act teeth, is that “the ‘rebuttable presumption’ part of UFLPA is truly unique. Anything coming out of Xinjiang is presumed to have used forced labor unless you can prove the negative. There is also a lack of a de minimis exception; this means that even an insignificant input of product produced in whole or in part with forced labor could result in enforcement action.
It is commonly believed that Europe leads the US in environmental, social, and governance legislation and enforcement. However, no nation’s enforcement of any ESG issue comes close to the US Customs and Border Protection Agency’s enforcement of the Uyghur Forced Labor Protection Act.
Historically, omnichannel software solutions were not capable of optimization. This meant that retailers were not maximizing their profitability.
Omnichannel refers to the ability of a consumer to get products fulfilled to them in various ways. A customer can of course buy goods in a store. They can also order online and have goods shipped to their home. Or they can order online and pick up the product at the store. There are several other fulfillment and product return options as well. An omnichannel order management system helps companies meet growing customer expectations.
An omnichannel order management system allows an organization to execute order fulfillment across all channels and fulfillment flow paths. Historically, an OOM has access to all the company’s inventory. The system allocated which orders would get inventory from a given location to fulfill an order.
Rules were used to make these decisions. For an online order that needs to be delivered within 24 hours, for example, the rule might state that the goods should be shipped to the consumer from the closest location. These rules can be put into a hierarchy. For example, look to fulfill from the closest store location to the consumer first, but if not all the ordered items can be fulfilled from this location, then fulfill from this the e-commerce warehouse. The rules hierarchies in these systems can get quite complex.
The problem with the rules that retailers use for omnichannel fulfillment is that they leave money on the table. The hierarchy of rules is too simple to fully optimize profitability.
Retailers don’t want to over-promise and under-deliver. So, the parameters they use in making their promises are conservative. The pickup time by carrier, transit times, and estimates about how many items a warehouse can pick per shift, are conservative to ensure that promises can be kept. But there is a cost to this. It can lead a retailer to promise something will be delivered in four days when it really could have reliably been delivered in two. This can lead customers to abandon their carts and go elsewhere. Among a sample of Manhattan Associates’ omni retail customers, 85% of the time the orders could have been accurately promised 1.5 to 2 days earlier.
Manhattan Associates is using machine learning to accurately parametrize the order promise. By looking at the historical data on an ongoing basis on what is achieved – by carrier, by lane, by mode, by pick rate at a fulfillment location, by day of the week or season of the year, and so forth – the system can be autoconfigured to make accurate promises for earlier deliveries.
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