Current business scenarios are proving that companies that plan for both best- and worst-case circumstances are the real winners.
In recent times, we’ve seen that well-prepared companies were the ones able to ride out the troughs and crests caused by the pandemic, an unprecedented geopolitical climate, and booms and busts of the stock market.
These companies were able to do so because they had a strong and efficient supply chain in place, one that could adapt to any kind of scenario and handle seemingly insurmountable challenges. They couldn’t have done it without careful planning and disaster preparedness in the supply chain and retail logistics.
What happened to those companies that weren’t well-prepared? Take the biggest disruption that happened in 2020 — that of the COVID-19 pandemic. The fallout was the huge impact on supply chain planning and scheduling, which up to then were well-oiled functions for companies.
In the aftermath of the pandemic, companies found themselves grappling with disruptions in the movement of goods and availability of raw materials, making it difficult to meet production demands. Most were compelled to move from the brick-and-mortar model to online platforms.
Suddenly, all companies, especially retailers, had to stop relying on historical data to engage in supply chain forecasting. Instead, they had to align themselves with changing customer demand. That’s when they realized that flexible supply chain planning was key to responding to business demands brought on by uncertain times.
This lesson has led businesses to focus on reviewing and revamping their supply chain planning capabilities to do contextual commerce. An example is autonomous supply chain planning enabled by artificial intelligence, which helps companies to correct situations on their own by managing fluctuating demand and realizing maximum value from the data provided by digital analytics tools.
Autonomous supply chain planning draws on advanced technologies such as machine learning and AI, wiring together huge amounts of data points to generate useful insights. It ties in historical data with current supply and demand to arrive at a better understanding of market needs. And it allows companies to fulfill dynamic customer requests resulting from fluctuating market conditions.
Apart from inventory forecasting, the system also enables companies to benefit from time and cost savings. Data-based reports and planning can help track supply chain performance over time, and find ways to enhance supply chain operations and keep associated costs as low as possible.
AI has several use cases and applications in the retail supply chain:
Recognize and understand all opportunities and threats across the supply chain. This can be done by having an end-to-end supply chain perspective that’s not just objective-oriented, but also gives an appropriate response to the situation.
Gather and interpret data in real time so that stock replenishments become timely.
Employ the data to mitigate service-level failures and tackle challenges before they snowball into big issues.
Efficiently use the autonomous supply chain, which offers a single and updated forecast. This eliminates inconsistencies between supply and demand, as well as the standard “out-of-stock” answer that eats into sales.
Apply AI and ML to the company’s most critical supply chain functions, such as forecasting and inventory management, to realize higher and faster return on investment.
Supply chain autonomy is a catalyst for the retail sector to achieve cost controls and realize higher profit margins. This involves a gradual adoption process and isn’t always an overnight transformation. What’s crucial here is partnering with a company that can provide the full range of supply chain automation services, and support retail companies in accessing the power of autonomy in today’s digital-first world, to achieve last-mile efficiencies and delight customers.
This article was first published on SupplyChainBrain.