If a business’s supply chain challenges are a door, consider its inventory replenishment strategy as the hinge. This strategy determines how businesses reorder items from suppliers to meet demand. When the supply orders meet the pace of demand, storage costs can be kept down and customers can be kept satisfied.
The problem facing any business with a supply chain is that replenishment strategies, even effective ones, stop working when internal or external factors change demand suddenly.
To meet changes in demand caused by anything from global market changes to seasonal consumer tastes, many businesses burn through their reserves paying for overstock and dealing with stock outs. Advanced analytics provide a modern solution to runaway warehouse costs by automating basic reordering strategies while customizing data collection methods for each business’s needs.
The importance of replenishment strategies
Commerce and manufacturing businesses of all sizes and in all industries rely on efficient inventory replenishment to meet demand while keeping costs low. The factors that businesses try to account for when forming their replenishment strategies include:
- Market conditions and other external factors
- Consumer demand
- Available space in their warehouse system
- Current stock levels
- Supplier behavior
Challenges can arise from any of these factors. For instance, inaccurate or sluggish demand forecasts make it more likely that businesses will be behind when demand increases or decreases, depleting their safety stock or causing costly overstock. Or consider that out-of-date inventory numbers make setting the right reorder values difficult, particularly when managing multiple storage locations.
Roles for data analytics in replenishment
Real-time data analytics have applications that go beyond e-commerce SEO. For businesses that manage inventories, versatile management software includes in-depth reports on trends and sales data based on real-time inventory tracking. This data produces more accurate forecasts, leading to better insights.
For instance, by automating stock counts, businesses no longer need to rely on manual inventory tracking to make important decisions. With all the records in one system, businesses can also eliminate the storage costs of keeping paper records.
Calculating an accurate reorder point or ROP is another significant challenge. The ROP is the inventory level at which a business should replenish its stock. The factors that contribute to the ROP include the demand forecast, current and safety stock levels, the supplier’s reliability, and the business’s lead times.
Data analytics offer solutions to these basic challenges of inventory optimization. However, modern inventory management systems equipped with advanced analytics can take a business’s strategies to the next level with these advanced features:
Real-time inventory tracking
Inventory management systems use real-time inventory tracking to help businesses strategize their reordering schedule. Various technology solutions can take this tracking to the next level, including:
IoT (Internet of Things): The IoT connects different types of devices such as phones and computers to enable management software solutions to operate on a wider scale. The IoT can collect performance data and automate functions vital to supply chain maintenance, provided the inventory management software has been implemented to utilize it.
RFID (Radio frequency identification): RFID tags can be scanned like barcodes, but they do not require the line of sight of a scanner. The process of collecting item check-ins, inventory counts, and shipment confirmations can be done at a distance Asset-tracking is smoother, and even returnable merchandise and company vehicles will always be in the system.
Remote monitoring: Cameras connected to the internet via a business’s inventory management system allow for remote monitoring. This means that supervisors can monitor the floor without being on-site – they simply log into the management system from supported devices such as their phone or computer and keep tabs on their operations from there.
These tools represent ways to acquire data, but the real question becomes how businesses can turn that data into actionable insights. The cost benefits of more accurate inventory tracking stem from how businesses utilize their demand forecasts to make changes.
Demand forecasting models
Inventory management systems identify trends in seasonal demand, analyze historical data, and predict market changes to help businesses get a clear picture of their customers’ future demand trends. By predicting the market direction, businesses can eliminate costly overstocks and stock outs resulting from non-optimized stock levels.
Reliable demand forecasting boosts sales, improves customer relations, and reduces costs by ensuring that popular items are always in stock and items that shift downward in demand due to seasonal market changes are not taking up valuable space in your warehouses.
Forecasting models that utilize machine learning algorithms can take this process a step further by improving the system’s ability to predict seasonal changes and recognize external factors utilizing AI.
For example, businesses whose supply chains were taken by surprise by global market shifts during the pandemic lacked the advantage of a system equipped with machine learning. Such a system could have recognized an external factor like a global market shift and calculated shifts in stock levels to meet the changing demand forecast faster than human managers ever could.
These models can also employ customer segmentation to organize customers into groups based on preferences or demographics. This allows businesses to make smarter decisions tailored to the groups they hope to attract.
Lead time analysis
Lead time analysis is used to calculate the ideal inventory levels your business should keep at a given time. The analysis includes a breakdown of the timeline for receiving, processing, selling, and shipping your inventory with the goal of cutting fulfillment costs by reducing overstocks and stock outs.
Analytics improve lead time calculations in a number of ways, including:
Accounting for supplier behavior: Manual tracking methods often fail to account for the supplier’s historical performance when timing reorders. Automated systems use analytics to factor in your vendors’ behavior.
Automating reorder points: Each item in your warehouse sells differently at different times. Advanced analytics can customize the reorder point of each item individually. Your supervisors will be able to see when an item reaches its reorder point, but the system will report the stock levels required to meet demand.
Time tracking and task scheduling: By more efficiently tracking employee and equipment tasks, advanced analytics allows the system to report to accounting, scheduling, and other systems to keep the combined workflow optimized.
The result of these systems working in tandem is a better relationship with suppliers since a smooth operation is profitable for them too. Lower costs and increased automation mean more resources to devote to other sectors.
Safety stock calculation
Safety stock, or the extra stock you keep on hand for in-demand items to prevent stock outs, is not as unpredictable when employing advanced analytics. The system can predict and automatically reorder based on more accurate predictions of market shifts, seasonal demand, external factors, and more.
However, safety stock is still complicated to calculate since no system is perfect. Optimizing these calculations leads to less labor for your team since they don’t have to calculate the safety stock levels for every product. As demand changes, businesses find that the ideal safety stock level is like a bullseye that never stops moving. Analytics offer the equivalent of a laser sight.
A system equipped with predictive analytics can optimize calculations for dynamic safety stock management, which accounts for variations in demand, including uncertainty in the forecast, so your business can always hit its target.
Implementing analytics for inventory replenishment
While these features offer versatile tools, they must be properly implemented to make a tangible difference to a business’s inventory management strategies. Consider the following qualities that inventory management software needs to effectively integrate with a business’s existing systems:
Data collection and preparation
Meaningful analysis can only lead to actionable strategies if it starts with clean and accurate data, including relevant historical sales data and inventory data. Systems cannot accurately create reordering strategies without real-time updates to numerous factors, including:
- Product IDs
- Product names
- Product locations
- Product measurements
- Minimum inventory
- Reorder points
- Order details
- Supplier IDs
To diagnose inventory issues, inventory control management systems need to have complete records of this information. On top of these basic identifiers, historical sales data, supplier behavior, external factors, consumer demand trends, seasonal changes, and more all have to be processed by the system to accurately predict your inventory needs.
Cross-functional collaboration
Your workflow likely relies on multiple systems working in tandem. These include production scheduling, capacity planning, work order management, sales, manufacturing, and more. Shared insights generated by advanced analytics lead to effective reordering strategies.
Imagine this scenario: an item that is normally a low-seller at a certain time of year unexpectedly increases in demand. Your production schedule is now behind where it should be to prevent stock outs, forcing you to dip into safety stock or even cancel outstanding orders.
With analytics capable of cross-functional collaboration, a more accurate demand forecast could predict this demand shift and key the production schedule ahead of time to keep your supply chain ahead of your customer’s needs instead of struggling to catch up to them.
Continuous monitoring and adaptation
The data points and strategies your business uses to optimize its replenishment strategy one minute may be outdated the next. Demand is always changing – sometimes predictably and other times due to unforeseen factors. The goal of management has become prediction and response. The faster the response, the better equipped to stay ahead of the competition a business will be.
Analytics-driven reordering strategies iterate on changing conditions based on the numerous factors they monitor in real-time, allowing businesses to automate their workloads for continuous adaptation. In short, optimizing reordering strategies is an ongoing process. It stands to reason that businesses need an ongoing solution as well.