An Introduction to Demand-Driven Inventory Replenishment Planning in 2023
In the dynamic world of supply chain management, demand planning is a critical component for achieving holistic supply planning. It involves constructing an accurate forecast of future demand based on supply drivers, aiming to meet customer needs as precisely as possible. With the unpredictable levels of demand fluctuations in today’s market, it’s crucial to effectively navigate the path to replenishment management in supply chains.
What is Demand-driven Inventory Replenishment?
Demand-driven replenishment is a strategic approach to inventory management that prioritizes aligning inventory levels with actual customer demand rather than relying solely on historical data or forecasts. This method ensures inventory optimization, reduces rework, and eliminates bottlenecks in the supply chain, ultimately enhancing resilience.
Imagine a river flowing downstream, where the water represents your inventory, and the riverbed is your supply chain. Traditional systems maintain the water level (inventory) based on past rainfall data (historical demand). However, in a demand-driven system, the water level is adjusted in real time based on the current weather conditions (actual demand), ensuring a smooth and efficient flow.
Why Use Demand-driven Inventory Replenishment?
A supply chain risk management study by Deloitte revealed that companies best equipped to respond to disruptions, such as the COVID-19 pandemic, had robust relationships with their suppliers and partners and the necessary tools to offer visibility into their broader supply networks.
However, those needing more supply chain visibility or the capacity to handle inventory fluctuations due to demand irregularities were left scrambling to implement systems that should have been in place.
Therefore, demand forecasting is one of the most critical aspects of the replenishment components. Massive disruptions demand a proportionally rapid and innovative response. Organizations that must see the demand-supply paradigm shift and respond appropriately and on time will likely stay caught up.
What Factors Influence Demand-driven Inventory Replenishment?
Almost 70-90% of stockouts are caused by poor replenishment, implying that just 10-30% occur due to inventory shortages or other supply chain challenges. When inventory plans fail, this can impact warehouse processes, sales cycles, and product mix strategies.
Therefore, it’s essential to have robust inventory replenishment planning strategies in place that can cope with any unforeseen circumstances impacting day-to-day operations.
The factors that significantly impact inventory replenishment include:
- Poor visibility of the end-to-end supply chain
- Sub-optimal warehouse space planning
- Lack of reliable forecasting and demand planning methods
- Absence of optimal stocking plans
- Insufficient warehouse order fulfilment processes
What is a Demand-driven Inventory Replenishment Strategy?
A demand-driven replenishment strategy is an approach to inventory management that focuses on aligning inventory levels with actual customer demand.
This strategy involves optimizing warehouse management, implementing process-oriented replenishment, enhancing supply chain visibility, leveraging machine learning and artificial intelligence (AI), and utilizing real-time forecasting.
What are Demand-driven Inventory Replenishment Planning Best Practices?
Effective replenishment planning and fulfillment strategies can drive sales and considerably amplify profits. Demand-driven replenishment planning with automatic replenishment can be effective by enhancing internal supply chain touchpoints.
Here are some best practices:
Optimized Warehouse Management
Warehouse management is integral to all supply chains, especially at the replenishment level. Hyper-automation across warehouse workflows, floor layouts, and inventory can significantly optimize warehouse management.
Accounting early for vendor delays can ensure rapid back-order fulfilment and reduce time-lapses in inventory replenishment planning. Determining lead times is a crucial component of process-oriented supply chains.
Enhancing Supply Chain Visibility
Enhanced supply chain visibility can highlight gaps in the supply chain where time and resource leakages might occur. Transparency in supply chain operations leads to efficiency in order fulfillment, demand forecasting, projecting warehouse levels, and internal stock levels.
Leveraging Machine Learning and AI
AI in the Supply Chain can create feedback loops between demand, inventory, and sourcing, enhancing supply chain operations. Machine learning can group data about sales orders, seasonality peaks, and demand history into a comprehensive format.
Forecasting demand in real-time can lead to an updated and automated supply chain, eliminating bottlenecks and unnecessary delays or halts.
Accurate Replenishment Planning with AI-powered Insights
ThroughPut’s AI-enabled replenishment planning solution has been designed to analyze massive volumes of data and enables predictive replenishment across complex supply cycles. ThroughPut provides accurate real-time insights into operations, to accelerate accurate decision making.
With ThroughPut, several growing organizations have benefited with holistic supply chain optimization and achieved the below outcomes:
- Achieve forecast accuracy to align with existing sales & operations planning objectives
- Manage real-time demand shifts for prompt replenishment planning
- Boost material flow and inventory levels for guaranteed OTIF performance
- Optimize and improve product and customer mix based on profit margins to generate free cash flow
ThroughPut’s AI solution can ensure optimized replenishment, based on a robust demand-forecasting model. To access ThroughPut’s AI expertise, book a demo here.