Mastering the Retail Supply Chain to Drive Intelligent Outcomes
Retail has been ramping up digital transformation for a few years now, in an attempt to keep pace with consumer expectations for frictionless, more personalized, omnichannel experiences. This transformation has been further accelerated by current external factors like the pandemic, global conflict and economic headwinds. As part of this effort, reimagining supply chain practices has become a top focus for many retailers, not only to better handle fluctuating consumer demand, but also to navigate future uncertainty, build permanent risk resilience, and drive intelligent outcomes.
Gartner reported that the #1 supply chain technology trend of 2023 is “actionable AI.” Retailers are adopting machine learning (ML) and artificial intelligence (AI) technologies, which are helping them use data in a streamlined way for end-to-end optimization of the product journey, from the supplier to the consumer.
Intelligent inventory management: control of your own destinies
Supply chain leaders alike, are also taking control of their own destinies by deploying AI/ML to reimagine the product journey, track assets, and monitor their locations in real-time, optimizing routes for faster last mile deliveries, planning the workforce in warehouses, predicting machinery failures before they happen, and more. Since most businesses are still relying on manual methods like offline spreadsheets to monitor supply chains, retailers that leverage digital technology will gain significant differentiation.
No matter if they are talking about planning, sourcing, making, or delivering, every supply chain manager’s main objective is to give the customer what they want when they want it. AI solutions can increase visibility into inventories, reduce inventory holding costs, reduce non-moving inventory, trim fulfillment costs, and cut transit times.
Whether facing a supply crunch or selling excess inventory, a lack of visibility into data will lead to customer experience misses and overall dissatisfaction. Retailers that fail to get closer to a demand-supply balance using manual processes will continue to experience expensive spoilage and out of stock scenarios, as well as profit margin losses and customer frustration.
Predicting the unpredictable: consumer demand
Retailers and economic forecasters have been surprised again and again over the past few years by fluctuating consumer demand. The factors that inform consumer demand for retail goods are as numerous as those that inform retail product supply. McKinsey’s 2022 supply chain survey revealed that demand planning was the #1 priority for digitization among global leaders, among whom 80% intend to or are already leveraging AI/ML for planning. AI can synthesize insights from massive amounts of data, from weather and perishable items to historical data and promotions, to prevent waste and inefficiencies and keep pace with unpredictable external factors, leading to a more accurate picture of future demand.
Ethical implications of using AI in supply chain management
The use of AI in supply chain management brings significant benefits, but it is essential to address the ethical implications. Responsible AI practices must be incorporated to mitigate potential negative effects. Data privacy should be prioritized, ensuring the secure and transparent collection, storage, and use of sensitive customer and supplier information. Algorithm biases should be regularly monitored and audited to avoid perpetuating discrimination or unfair practices. Retailers should strive for diverse and representative datasets, implementing bias-checking mechanisms to ensure fairness. By adopting responsible AI practices, retailers can build trust, respect data privacy, address biases, and consider the impact on human labor, thereby ensuring ethical and sustainable AI adoption in supply chain management.
Solving long-term data visibility and management problems: driving change
Wrangling data and insights from one organizational arm into an actionable dataset is tough enough for retailers. So doing so across a globally dispersed supply chain and partnership network has proven to be a nightmare. This supply chain sector has done an admirable job trying to fill the gaps in their data capabilities thrust forward as a result of the pandemic. Yet, while supply chain organizations are now generating and capturing more data than ever before, many still lack the necessary tools to help them clean and process this vast data lake into actionable insights. And unfortunately, age-old data management processes, like spreadsheets, are too labor intensive to deliver the timely and actionable insights that organizations need.
This has brought upon a further need to embrace and adapt AI tools not only to help optimize various components like inventory management or shipping, but also to bring all of this data together. Moreover, technology is not only helping supply chain leaders get a clear and consolidated view of the insights that matter but is also allowing them to identify anomalies early so that they can avoid disruptions and bolster their resilience.
Recent history has amplified and accelerated the need for a reimagined retail supply chain and the alignment of operational functions towards a common goal. The complexity of today’s shifting retail ecosystem combined with the enormous aggregation of data encourages the use of ML/AI to drive better business outcomes.
In conclusion, by leveraging actionable AI technologies, retailers can master the retail supply chain, achieve intelligent outcomes, and drive positive change. Through intelligent inventory management, accurate demand forecasting, and data-driven decision-making, retailers can optimize their operations, enhance customer satisfaction, and build resilience in the face of uncertainties. Embracing AI tools and adopting a holistic approach to data consolidation and analysis will empower retailers to navigate ethical challenges and drive positive change while ensuring a responsible and inclusive future for retail supply chain management.
