Covid-19 lockdowns accelerated a shift to online shopping that was already well under way. According to the Adobe Digital Economy Index, consumers now spend an average of $6.7 billion online each month on groceries, up from $3.1 billion pre-pandemic. Adobe expects the category to reach an annual figure of $85 billion in 2022.
This doubling in demand coincides with supply chain turbulence due to labor supply issues, rising inflation and geopolitical crises such as the war in Ukraine. It’s a challenging landscape for retailers and suppliers alike.
Retailers had to revolutionize inventory and planning when the pandemic hit, and many businesses are still finding their feet in the post-pandemic economy. Some analysts have predicted that it could take until mid-2024 for supply chains to return to relative normal. But that period of ‘normal’ could be short-lived: McKinsey reports that significant disruptions to manufacturing production now occur every 3.7 years, on average.
With so many factors impacting fragile, interconnected supply chains, how can retailers use data to react effectively to disruptions?
Adapt or lose customers
54% of company executives acknowledge that they don’t have clear visibility of their supply chain data past tier 1. Disruption at the early stages goes unseen, and businesses don’t feel the consequences until there are empty spaces where products should be. By then, it’s too late.
Consumers encountered 60 billion out-of-stock messages from online retailers between March 2020 and February 2022, according to Adobe. Shoppers are now likely to see an out-of-stock message on one out of every 59 product pages, a 235% increase from pre-pandemic levels.
Having insufficient stock to fulfil demand results in missed sales, causes reputational damage and sends customers to competitors.
As manufacturing and supply chain disruption increases, retailers must optimize inventory management to give customers a better experience. Businesses that employ data-led solutions to streamline supply chain efficiency will reap rewards.
Common inventory management headaches
Siloed data systems: Huge spreadsheets don’t cut it any more. With inventory data scattered across internal and external siloed systems, teams can’t find what they need, when they need it.
Inability to process data effectively: Data is no good if you can’t interpret it. Retailers need tools that can connect and correlate all inventory data with the impact of external events. Without them, retailers can’t proactively address inbound disruptions, accurately calculate inventory turnover velocity and reduce discounting and overstocking.
Inefficient processes: Manual, error-prone processes – like phone calls and email chains with teams across your ecosystem – are inefficient when you need to make sound inventory-related decisions quickly. Valuable employees waste time tracking down available inventory rather than closing a sale or managing a customer relationship.
How can retailers manage supply chains to minimize disruptions?
Since disruptions can cause inventory shortages or excesses that are discovered long after they’ve taken place, businesses need to get ahead of supply chain issues by collecting, classifying and structuring data. Most companies understand the need to harness their data, with 89% of companies stating that they require data management software in their end-to-end supply chain.
AI and machine learning will increasingly play a role in supply chain management. They can help retailers to become more sophisticated in how they deal with disruption, automating reactions and predicting potential challenges before they happen.
Retail planning software enables companies to orchestrate an integrated process from planning and development to delivery and omni-channel sales. With the right software, retailers can plan, visualize and execute based on real-time plans vs actual feedback throughout the entire product lifecycle.
How will data shape the future of inventory management?
Accurate, up-to-date and relevant data is the lifeblood of successful inventory management. Next-generation analytical and planning tools interpret vast volumes of data so that retailers can predict and react to disruptions before they affect inventory levels.
Digitization reduces manual tasks, freeing up employee time to focus on innovating, managing relationships and improving output. Even better, AI and machine learning can help to make decisions that reduce safety stock levels and carrying costs, and increase inventory turns. This will be aided by more accurate and real-time forecasting, in turn supported by predictive models.
All in all, we can expect to see smarter data use leading to faster and better-founded decision-making processes. Ultimately, the challenges presented by the pandemic and concurrent supply chain issues may be seen as a turning point for the retail sector – a point at which inventory management changed for the better.
About the author
As a business solution expert for the global consumer goods industry, Paula Biste has been advising companies on software technology applications for the last 15 years. Her career has given her uncommon insight into product development, manufacturing and merchandising operations. She is currently advising retailers and brands worldwide on how to digitalize their end-to-end merchandising processes as a business consultant at Centric Software.
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