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These are the top ways leading retailers mitigate supply chain tariff disruptions to optimize performance, drive shopper satisfaction and grow sales
Supply chain disruption is a fact of life in the retail industry, with the most recent example involving the frenzied situation surrounding tariffs. The imposition of tariffs by the Trump administration and reciprocal actions by key U.S. trading partners have roiled global markets and fueled speculation about a recession and renewed inflation.
While the severity and duration of tariff-induced disruption are unknown, supply chain professionals are no strangers to major disruptions that have occurred with increasing frequency over the past several decades. There were the U.S. terror attacks of Sept. 11, the global financial crisis and Great Recession, the global COVID-19 pandemic, followed by rampant inflation and the ongoing war in Ukraine. Each development created new challenges and required retailers to adjust and rethink product sourcing, demand forecasting, replenishment, inventory accuracy, and overall supply chain efficiency.
Tariffs may be the newest development, but to address the current situation retailers can use many of the supply chain muscles developed through coping with prior disruptions combined with rapid advances in AI-powered retail supply.Here’s a look at the top ways supply chain leaders in the retail industry achieve greater supply chain agility.
Optimization and simulation
Scenario planning is essential in the modern supply chain, a lesson retailers learn, some the hard way, every time there is a significant disruption. It’s why retailers who use AI-driven optimization and simulation models to fine-tune supply chain processes and improve decision-making outperform those caught flat-footed by marketplace disruptions. These models can optimize inventory accuracy, production schedules, transportation routes, and other variables to minimize costs, improve service levels, and enhance overall supply chain efficiency.
Leverage the power of predictive analytics
Retailers can secure a relatively easy win with demand forecasting by immediately using AI algorithms to analyze historical data and identify patterns, trends, and seasonality in customer demand. By doing so, retailers can predict future demand more accurately to enable better inventory planning and ensure in-store inventory levels align with shopper demand.
Make better use of machine learning algorithms
A subset of AI, machine learning algorithms are the perfect application for data-rich supply chain environments. Retailers can employ ML algorithms to continuously learn from new data, thus improving forecasting models and demand planning. The algorithms adapt to changing market conditions, such as those caused by tariffs, and evolving customer behaviors. The end result is that retailers can incorporate countless variables that enhance the accuracy of demand forecasting in a way that non-AI-powered software cannot.
Real-time data integration
The retail industry is inundated with data and savvy retailers take advantage of as many sources as possible to improve supply chain efficiency. However, it requires unifying and integrating data in real time from various sources such as point-of-sale systems, social media, online marketplaces, weather forecasts, market research, supplier partners, and third party sources to create a powerful cumulative effect. By analyzing this up-to-date information, AI models can generate more accurate demand forecasts and enable proactive inventory replenishment that aligns with retailers’ in-stock goals and service levels.
Demand sensing
Forecast accuracy benefits greatly when retailers use AI-powered demand sensing techniques to capture real-time demand signals and market insights. For example, monitoring consumer behavior, competitor activities, economic indicators, and other data sources for specific categories helps retailers make better-informed decisions and respond more quickly to changes in demand.
Collaborative forecasting
In times of tariff uncertainty, collaboration with trading partners and suppliers takes on heightened importance. Collaboration and data sharing between suppliers, retailers, and other partners in the supply chain are essential for efficiency and forecast accuracy. AI algorithms play a key role and can facilitate the integration of multiple forecasts, identify discrepancies, and generate a consensus forecast that reflects collective intelligence. It is a great way to reduce forecasting errors and improve inventory management during times of profound uncertainty.
Increased visibility of shelf conditions
AI-powered computer vision combined with optical character recognition helps retailers see what is really going on in stores. Gaining an accurate view of shelf conditions enables retailers to solve on-shelf availability issues and gain a clearer picture of true inventory levels to inform demand forecasts.
These are proven strategies for managing the increasing complexity of retail supply chains and they have become even more effective in recent years due to advances in AI-powered supply chain technology. So whether it is tariffs or an unknown disruption lurking around the corner, retailers have never been better equipped to cope with the uncertainty and disruptions common in supply chains.
