Staying ahead of game with prediction-driven supply chain management

Amazon patented a system called ‘anticipatory shipping’, which predicts and begins moving the products that are expected to be bought by its customers to a hub near them, even before an actual order is placed. Business Intelligence algorithms make the call based upon consumer shopping patterns, past purchases, browsing patterns and intent mapping—all harvested using machine learning and data analytics.

Same-day or next-day delivery which is almost a de-facto standard for fast and reliable delivery today is in itself a revolutionary trend. Shortened delivery times delights customers, expand the loyalty base and improve lifetime value (CLV). Given that this is the new normal, businesses face compounded challenges in managing inventory, production and logistics to meet their customer’s expectations.

In this context, prediction-driven supply chain management as an approach has emerged as a powerful tool. It leverages data and analytics to make accurate predictions about future supply and demand trends, enabling companies to make informed decisions well in advance on production, inventory and distribution.

Descriptive and diagnostic analytics works with historical data to highlight key metrics that help understand what is happening with the business and to deep-dive into it to identify root causes. Predictive analytics takes a step forward, combining historical with real-time data and using statistical methods to discover patterns and trends to forecast the future. This type of mature analytical framework is very useful as decision support inputs for CXOs.

There are several applications of predictive analytics including anticipating demand to adjust inventory, optimizing shipping and routes, minimizing stockout risks, reducing waste, improving delivery times and achieving an overall cost reduction to stay ahead of the competition.

Here are some reasons why prediction-driven supply chain management is not just good-to-have but an essential need of the times:

1. Demand Forecasting
Supply chains today are complex, global and with multiple moving parts connected together on a data backbone. Traditional demand forecasting methods that rely on historical sales data, seasonal trends and market intelligence have their limitations in their ability to work with large datasets in real-time, complex relationships for discovering hidden patterns in the data.

Business analytics algorithms based on machine learning (ML) offer a powerful tool for demand forecasting by analyzing volumes, velocity and variety of data for detecting patterns and trends that may not be readily apparent. ML techniques can be trained to identify correlations in a wide range of data types, including sales data, customer demographics, social media sentiment and even weather patterns. By leveraging ML algorithms for demand forecasting, businesses can gain a more accurate understanding of their customers' purchasing behavior and predict future demand with greater precision and make data-driven decisions to optimize their inventory and production planning.

2. Inventory Management
Effective inventory management is critical to the success of any supply chain operation. By analyzing demand patterns and lead times, supply chain managers can optimize their inventory levels to ensure that products are available when and where they are needed. It also helps reduce excess inventory that ties up resources and increases holding costs. By analyzing lead times, managers make adjustments to account for items with longer lead times and minimize the risk of stockouts.

3. Managing Risks
Using prediction-driven supply chain management, companies can proactively manage their supply chains and ensure business continuity by identifying potential risks and opportunities. By analyzing data and predicting potential supply chain disruptions, developing contingency plans, diversifying suppliers or optimizing inventory levels, companies can take steps to mitigate impacts and ensure continuity of operations, executing better risk management.

4. Enhanced Customer Service
An end-to-end view of the supply chain enables companies to accurately forecast demand to ensure that products are available when and where they are needed, resulting in higher levels of customer satisfaction. Maintaining optimal inventory levels and streamlined operations results in reduced channel logistics costs that can be passed onto the customers, delighting them by providing higher value on their spending. Happier customers come back for more, and also make positive recommendations ensuing higher volumes, market share and profitability.

5. Better Supplier Management
Data analytics and BI are powerful tools that assist in analyzing historical data for supplier performance metrics and optimising supplier management processes. It enables supply chain managers to identify the most reliable and cost-effective suppliers. Further, managers can proactively identify potential issues and take appropriate measures to abate risks and avoid supply chain disruptions.

6. Sustainability
Data analytics and ML can play a critical role in optimizing supply chains for sustainability. Analyzing data on energy usage, emissions, and waste can help supply chain managers identify areas where sustainability improvements can be made. It is already an important consideration for businesses and can have a significant impact on the environment as well as the bottom line.

7. Agility
Companies use prediction-driven supply chain management to respond quickly to changes in demand, supply and market conditions. By continually monitoring and analyzing data, companies adjust their operations in real-time to meet changing customer needs and trends, staying ahead of the curve with quicker lead times and delivering improved customer satisfaction.

8. Higher Efficiency
Businesses achieve greater operational efficiency, reduce costs and improve customer satisfaction by leveraging the insights provided by predictive analytics. With enhanced visibility across the supply chain, they can anticipate potential disruptions before they occur. It also helps businesses in identifying opportunities for process improvements, such as optimizing delivery routes and scheduling that minimizes lead times resulting in higher profitability.

Prediction-driven supply chain management is a key driver of sustainable business growth, helping companies to gain a competitive edge and succeed in today's dynamic marketplace.

Companies that embrace prediction-driven supply chain management stand to gain by being better equipped to meet customer demands and adapt to market changes. With real-time visibility of their supply chains, companies proactively address potential disruptions, enabling them to operate with greater agility and resilience. With decision intelligence tools they make informed decisions, reduce costs, and optimize inventory levels. This improves bottom lines and also enhances customer satisfaction and builds a positive brand value.

The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of Indian Transport & Logistics News.