Retail supply chain analytics: technologies, applications, and benefits

According to McKinsey, the global retail industry loses an estimated $1.1T annually due to overstocking or stockouts. Partnering with an experienced retail software development services provider can help businesses implement tailored solutions to overcome this and enhance their supply chain management capabilities.

Traditional retail supply chain management (SCM) often relied on historical data and intuition, resulting in slower and less precise decision-making. Today, the shift towards data-driven analytics offers a more accurate, proactive approach. By embracing AI, machine learning, and big data analytics, retailers can optimize their supply chains to be more responsive, efficient, and resilient in an increasingly dynamic market.

We'll delve into the key benefits, challenges, and emerging technologies transforming retail supply chain analytics.

Key benefits of supply chain analytics

Gartner outlines four key benefits of supply chain analytics that help leaders demonstrate its return on investment (ROI):

1. Financial improvement: Analytics contribute to achieving financial goals such as revenue growth, increased market share, reduced working capital, and higher return on assets (ROA). Although attributing financial gains directly to analytics can be challenging, they often contribute to these improvements.
2. Supply chain improvement: Analytics can improve specific supply chain performance metrics such as order cycle times, supplier lead times, and forecast accuracy. While contributing to overall financial success, these metrics are critical to optimizing supply chain performance.
3. Process improvement: Analytics enable faster, fact-based decision-making, increasing team productivity and improving customer service by reducing the time required for tasks like reporting, business reviews, and order updates.
4. Stakeholder feedback: Positive feedback from stakeholders, such as improved work processes, satisfaction with reporting tools, and better customer collaboration, reinforces the value of analytics. Leaders should also consider constructive criticism to enhance analytics adoption and effectiveness across the organization.

Retail supply chain analytics technologies

With various advanced technologies available, the key question for retail businesses is determining the right combination to optimize their supply chain analytics. Selecting the ideal mix of tools, guided by a trusted tech partner, can significantly boost efficiency, enhance forecasting accuracy, and enable agile responses to market shifts. The wide array of tools available includes:

1. AI/ML: Facilitates advanced data analysis, pattern recognition, and decision-making automation to optimize supply chain operations, improve demand forecasts, and enable real-time adjustments. Over 40% of supply chain organizations report heavily investing in it, reports EY.
2. Big Data analytics: Leverages large datasets from various sources like IoT sensors, transactional data, and market trends to gain insights into supply chain performance, customer behavior, and operational efficiencies.
3. Cloud-based analytics: Provides scalable computing power and storage for analyzing large volumes of data, enabling real-time visibility into supply chain operations and allowing remote access to analytics tools.
4. Internet of Things (IoT): Collects real-time data from sensors and connected devices across the supply chain. This data is then analyzed to optimize logistics, track inventory, and improve asset management.
5. Digital Twin technology: Builds a virtual model of the entire supply chain to simulate real-world scenarios, enabling companies to run "what-if" analyses and optimize processes based on predictive insights.
6. Geospatial analytics: Uses location-based data to optimize logistics, improve route planning, and analyze regional demand patterns for better distribution network management.
7. Blockchain analytics: Offers transparency and traceability in the supply chain by analyzing data stored in blockchain ledgers, providing insights into product provenance, supplier reliability, and transaction records.
8. Data visualization tools: Tools like Tableau, Power BI, and custom dashboards enable businesses to visually explore data, identify trends, and communicate analytics insights across the organization.
9. Robotic process automation (RPA): Automates repetitive data entry and analysis tasks within the supply chain, freeing human resources to focus on higher-value analytics activities.
10. Sentiment analysis: Uses natural language processing (NLP) to analyze customer feedback and market sentiment from social media and other sources, helping businesses to align supply chain strategies with consumer demand trends.

Partnering with an experienced technology provider can help businesses select and implement the ideal toolkit, offering comprehensive insights into supply chain operations. This approach enables data-driven decision-making and allows for proactive responses to shifts in demand or supply conditions.

Read more: How to mitigate supply chain risks with Data Analytics

Applications of retail supply chain analytics

Retail supply chain analytics technologies encompass various advanced applications that streamline and enhance supply chain operations. Key applications include:

1. Scheduling: Enhanced visibility into inventory levels, demand patterns, and manufacturing capacities enables more precise production and distribution scheduling. Thus, resources are allocated more efficiently, reducing bottlenecks and delays.
2. Warehousing: Real-time big data analysis within warehouse ERP systems helps identify inventory levels, track delivery mismatches, and monitor incoming shipments. This capability ensures that warehousing operations remain efficient and responsive to changes.
3. Demand forecasting: By leveraging sales data, market trends, competitor insights, and economic indicators, technologies provide more accurate demand forecasts. This helps retailers better anticipate shifts in demand, leading to improved planning and resource allocation.
4. Distributing: The optimization of distribution networks is achieved through real-time data analysis, which allows companies to streamline operations across distribution hubs, warehouses, and plants. This data-driven approach ensures smooth material flow and reduces distribution delays.
5. Inventory planning: With complete SKU-level transparency and automated replenishment systems, retailers can manage inventory more effectively. Coupled with demand forecasting data, this ensures that stock levels remain balanced, avoiding the pitfalls of overstocking or understocking.
6. Delivery: Technologies enable the monitoring of delivery routes, traffic conditions, and weather in real time, allowing for dynamic rerouting when necessary. This enhances delivery efficiency and optimizes capacity and asset utilization across the supply chain.

These applications collectively improve retail supply chains' efficiency, agility, and resilience, allowing businesses to better meet consumer demand while maintaining operational cost-effectiveness.

Retail supply chain challenges

Retail businesses face many factors that can significantly impact their profitability and competitiveness. Fortunately, advanced analytics solutions can provide valuable insights and tools to help retailers overcome these obstacles and optimize their supply chains. Retail supply chain analytics helps retail businesses address several key supply chain challenges, including:

• Managing margins and assets: Difficulty in understanding the true costs and value drivers within complex supply chains, leading to inefficiencies in managing commodities, inventory, working capital, and logistics networks.
• Event-driven risk management: Struggling to anticipate and respond effectively to risks and disruptions in the supply chain, which can cause delays and increased costs without proper planning and automated response mechanisms.
• Sensing and planning for demand: Inaccurate forecasting that fails to keep up with fast-changing product lifecycles and shifting customer demands, resulting in stockouts or overstock situations.
• Managing business relationships: Challenges in managing and optimizing relationships with trading partners, maintaining performance standards, ensuring compliance, and facilitating smooth information flow across the supply chain.

According to the Economist Intelligence Unit survey, the top internal risks to retail supply chains include a lack of forecasting accuracy, a lack of seamless information flow, and insufficient stock planning.

A strong focus on analytics is one of the most effective ways to address retail supply chains' interconnected challenges. Supply chain analytics is essential to improving resilience, efficiency, and competitiveness in the retail sector, from optimizing supplier management and forecasting accuracy to ensuring seamless information flow and proactive quality control.

Wrap-up

The increasing unpredictability in global politics, industrial policies, financial markets, and climate patterns has amplified the importance of understanding supply chain complexities and identifying potential risks. Retail supply chain analytics software aims to enhance forecasting, efficiency, and responsiveness to customer needs. To achieve comprehensive supply chain visibility, organizations must integrate various systems involved in procurement, production, distribution, and aftermarket services. This integration allows for real-time tracking of goods throughout the supply chain, enabling better decision-making and improved efficiency. Businesses can harness retail supply chain analytics to make decisions faster, raise forecast accuracy, improve customer service, and increase revenue by partnering with a trusted tech partner.