Inventory reordering is a crucial supply chain process that keeps production flowing and prevents costly stockouts. However, traditionally reordering has been manual, tedious, and reactive – often leading to excess stocks or shortages. The rise of automation and AI is transforming inventory management, enabling data-driven, precise reordering.
In this comprehensive guide, we’ll explore what automated reordering entails, key enabling technologies, top benefits for businesses, use cases, best practices for implementation, and how to track performance.
What is Automated Reordering?
Automated reordering leverages software systems to monitor inventory levels and automatically generate purchase orders when stocks hit predefined reorder points. This removes the need for human interaction to submit replenishment orders.
Key enablers include:
- Integrated inventory management systems gathering real-time data on stock levels, demand, and other variables from sensors, ERPs, and other sources
- Analytics engines applying machine learning to forecast usage and calculate dynamic reorder points based on seasonal, historical, and contextual data
- Trigger-based architecture that automatically generates purchase orders when stocks hit minimum thresholds
- RPA bots that seamlessly interface with order management systems to execute reorder transactions
Automated reorder rules can also specify preferred vendors, bundles, delivery modes, and other purchasing specifics to optimize the entire replenishment process.
Industry Adoption
Automated reordering has seen rapid adoption, with Mordor Intelligence predicting the automated inventory management software market to grow 13% CAGR to reach $4.6 billion by 2030 [1]. Top drivers include omnichannel retail expansion, demand volatility and supply uncertainty post-covid.
A Material Handling Institute survey found 87% of supply chain leaders ranked inventory visibility as critical or highly important, underlying the growing data connectivity and intelligence needs for resilient operations [2].
The Business Case
The business case backing investments into reordering automation is compelling:
- 75% of cross-industry businesses suffer stock-outs an average 2.8% of the time, costing 4% of revenue [3]
- Up to 30% of healthcare inventory costs stem from overstocks, expiration and obsolescence [4]
- 20% of industrial supply chain managers report disrupted production due to raw material shortages in the past 3 years [5]
Intelligent inventory management technology provides the answer. Aberdeen Research found that best-in-class automated replenishment adopters enjoyed 98.6% order fill rates and near 99% inventory accuracy [6].
Benefits of Automated Reordering
Implementing automated inventory reordering delivers multifaceted benefits:
1. Dramatic Time and Cost Savings
Automating the traditionally tedious and manual inventory reorder process saves significant human capital and expenses. Analytics and RPA bots handle the legwork of tracking usage, forecasting demand, and filling orders 24/7. This allows procurement teams to focus on more strategic tasks.
2. Reduced Stockouts and Improved Service Levels
Automated systems reorder at precisely calculated points to prevent inventories from dropping to critical levels. This reduces stockouts, production bottlenecks, and rush orders which disrupt operations. Customers also benefit from consistent product availability.
3. Superior Inventory Visibility and Control
Integrated inventory management systems provide real-time visibility into stock levels across facilities while analytics translate data into actionable insights. Teams can track performance metrics and configurable alerts provide warnings to prevent potential problems before they occur.
4. Compliance and Auditability
Rule-based architecture enforces ordering policies, budget controls, and documented approvals, supporting compliance objectives. All transactions are digitally logged for auditing and historical analytics.
5. Decentralized Management
Automation enables reliable reorder execution by local facility-level users through a unified view of network-wide inventories while retaining centralized vendor agreements and commodity code structures.
6. Enhanced Demand Forecasting
By analyzing historical order data, seasonal variables, promotions, economic factors and more, machine learning algorithms can more accurately predict future demand. This fine-tunes reorder points and quantities to align with consumption.
7. Leaner Inventories & Reduced Waste
Calculating precise safety stocks and dynamically adjusting reorder parameters minimizes the risk of excess inventory. This also reduces spoilage of perishable products. Intelligent reordering minimizes the working capital tied up in inventories.
Use Cases
Automated reordering delivers value across industries including:
Manufacturing
Automating reorder processes for raw materials and machine components optimizes throughput in just-in-time production environments while minimizing buffer stock requirements. IoT enabled equipment can also automatically request replacements for worn components.
Retail & Distribution
Retailers must manage thousands of rapidly moving products across locations and volatility from promotions and seasonality. Automated reordering is crucial for maintaining availability despite complexity at scale.
Healthcare Systems
To save lives, hospitals and medical facilities cannot risk running out of critical supplies like pharmaceuticals or blood banks. Automation keeps inventories sufficiently replenished considering lengthy lead times and demand spikes.
Automotive Manufacturing
Automakers orchestrate thousands of supplied parts crossing international borders while battling industry disruptions. Automation coordinates this complexity, providing component availability resilience.
Food & Beverage
Fluctuating crop yields, along with perishability factors create immense challenges forecasting and shipping foodstuffs where margins are razor thin. Automated reordering preserves freshness and mitigates waste.
Service Supply Chains
Aftermarket parts distributors must guarantee near 100% uptime and availability across millions of SKUs and outlets. Automation handles scale while optimizing regional inventory distribution strategies.
Technology Architecture
Automated reordering leverages a technology stack including:
Sensors and Auto Identification
Automatic identification technologies like RFID and barcode scanners capture granular, real-time data on inventories as items move through facilities. IoT sensors can also wirelessly monitor stock levels.
Business Systems Integration
ERP, MRP, WMS and other backend systems integrate into a unified platform providing end-to-end visibility and executing reorder transactions.
Analytics Engine
Replenishment algorithms crunch massive datasets using machine learning to model, predict and optimize complex demand patterns, accounting for all internal and external variables.
Blockchain
Emerging applications of blockchain provide trusted data sharing across multi-party supply chains, improving end-to-end visibility for decentralized decision making.
Process Automation
RPA bots seamlessly interface with ordering systems to raise and process purchase orders automatically based on coded logic and workflows.
Digital Twin Modeling
Virtual simulations of inventory flows, order processes and shop floor operations enables testing replenishment strategies in silico before real-world implementation.
User Interface & Alerting
Dashboards, reporting tools and customizable notifications keep humans in the loop, providing visibility and supporting configuration changes.
Best Practices for Implementation
Follow these vital steps when deploying automated reordering:
Integrate Software Systems
Create unified data flows across inventory management software, ERPs, order management tools and all other relevant platforms to provide the complete visibility needed. Utilize middleware as needed.
Analyze Historical Data
Aggregate at least 12+ months of past demand data. Cleanse data, identify patterns and outliers, and build forecasting models. Externalize key variables.
Define & Code Logic Rules
Specify reorder points and quantities based on safety stock buffers, lead times and more. Script rules for regenerating purchase orders and integrate with procurement systems.
Simulate & Iterate
Run simulations using real projected data to validate system performance before go-live. Continually tune and enhance algorithms post-implementation.
Monitor KPIs
Establish processes for tracking key metrics like service levels, inventory costs, and reorder cycle times to measure operational impact over time.
Conduct Pilots
Prove automated reorder value through limited pilots focused on high-impact SKUs before wider rollouts. Measure before-and-after performance deltas.
Phase Implementations
Stage introductions across groups of products, facilities and regions over 12-18 months for controlled adoption. Adjust strategies dynamically based on learnings.
The Future of Automated Reordering
While automation has delivered immense progress optimizing inventories, innovations on the horizon promise even greater benefits through connectivity and intelligence.
Gartner predicts that by 2026, 50% of global product manufacturers will orchestrate real-time reorder processing between smart machines, customers and multiple tiers across their supply chain network [7].
Dubbed "vendor-managed inventory" or VMI 2.0, this strategy goes beyond traditional past-looking reorder automation. Instead AI and prescriptive analytics will dynamically coordinate machine-to-machine inventory planning, anticipating future demand changes in real-time while intelligently routing resources across the ecosystem to achieve target service levels.
Emerging digital twin simulations of entire vertically integrated supply chains will also help evaluate the system-wide impacts of automated ordering policies before real-world implementation. By first replicating live operations data in accelerated virtual environments, data scientists can formulate optimal strategies.
Blockchain enabled "distributed inventory" models allowing partners across a decentralized supply network to securely share stock level transparency are also gaining traction. This visibility aids collaborative decision making, with downstream signals helping optimize upstream planning.
As the pace of business accelerates, agility and precision empowered by smart automation will define the new era of inventory replenishment excellence.
The Bottom Line
Automating the once manual inventory reorder process is a new imperative for competing in today’s demand-driven economy. AI and analytics radically transform legacy reordering activities from reactive to predictive – squeezing latency out of the replenishment cycle while optimizing flexibility and control. By implementing purpose-built automation, enterprises can manage volatile demand, maximize service levels, minimize write-offs and free up working capital.
