Knowing the right reorder points is an important factor for companies. Balancing inventory must be strategic because buying too early may result in unnecessary expenses and buying too late may lead to a stockout

With that in mind, companies must know their reorder point, also known as ROP, to avoid situations like that. 

Reorder point in inventory policies 

For instance, when using a continuous review policy, the ROP is the moment where the inventory reaches or goes below the threshold. Opposite to that, in a periodic review, ROP is pre-determined and what changes is the quantity of items ordered. 

In the first one, an example may be: when the stock level reaches 3 pieces, I order 10. In other words, with a fixed reorder point, the quantity is the same every cycle, even if your stock goes below the pre-determined value of reorder. 

On the other hand, a periodic review would work like that: we make an order every friday evening to our supplier. That means that the day is fixed, but the quantity will vary every cycle. 

Combining policies 

Although the most common inventory policies have fixed points, being the moment or the quantity. It’s possible to combine two of them to make the most of an operational convenience. 

In this option we would have a fixed quantity and a fixed schedule. The reorder point, then, would be like the example: every Saturday, if I have less than 4 items on my stock, I order 6 more. 

This way, the order quantity stays the same, which makes transportation and packaging much easier. At the same time, allows operations to group order with a supplier. 

Every policy has it risks, the same happens with the hybrid. With fixed order and schedule, the risk is overcompensating the stock and having a higher inventory level and cost.  

Also, the hybrid policy is much more complex to optimize mathematically, resulting in less discussions about it and therefore, less optimized models. 

The costs of reorder points 

When working with a fixed reorder point, two natural questions are: What is the right quantity? What is the best moment to order?  

The specialist in supply chain Nicolas Vandeput recommends the Model-Optimize-Apply-Learn framework. 

This means that the first step is modeling the costs of the supply chain based on the order quantity, then optimize the model, which means finding the optimal order quantity that minimizes costs. 

The maximum level of inventory we can have is “Q” and the lowest is 0. In a deterministic context, the demand is constant, so the stock will drop in a regular way. That’s a simple case and will result in two costs: Holding costs: related to warehouse and possessing items and keeping them. 

  1. Transaction costs: the costs involved in making an order. 

Considering those two types of costs, we get to the formula of total costs: 

Total Costs = Holding Costs + Transaction Costs. 

The holding costs are proportional to the order quantity, so you need to optimize the order quantity to avoid expending more than you need on stock. On other words, the more product you order, the more you will have to stock. 

Replenishment models 

The objective of replenishment models is to find an optimal policy management for inventory. Which consists in figuring out how much to order and when to order. 

When to order usually is fixed or quantity based when an inventory reach a certain level. As we’ve seen, there’s still ways to merge those two and have a fixed schedule with fixed quantity. 

But a couple of aspects must be considered when deciding how to define a replenishment model. For example, there’s different characteristics that will impact demand: 

  • Constant or Variable: does the consumption of your company stays the same or it’s highly mutable? 
  • Known vs Random: do you know what the demand is going to be or it’s stochastic? 
  • Continuous vs Discrete: it spreads through the cycle or it concentrates in a specific moment? 

Different kinds of demand result in a various of time leads that will also impact the decision. It may be instantaneous, which will be easy to replenish or take a long time to get to your stock.  

Not only that, the time spent on lead time may be very strict and precise or also be variable. Another thing is the necessity to order only one kind of item or items that complement themselves. If your production needs more than one raw material, for instance, not necessarily all of them will arrive at the same time. 

The importance of Reorder Points 

Every business that wants to have an effective inventory management, reorder points are crucial. With right decisions, it will help save money by holding costs and preventing stockouts. 

A good reorder point will also make sure that there is enough stock available to satisfy the customers. So, when calculating a good ROP for two different scenarios, being with safety stock and without safety stock, you should consider three factors: 

  1. Lead time: the time spent to fulfill your order 
  1. Safety stock: the amount of stock in warehouse that can help avoid stockouts. 
  1. Daily average usage: number of sales made in a day. 

Then, the formula to calculate ROP for stock purposes, when you order consider the number of items in inventory, is: 

ROP = (daily average usage x lead time) + safety stock. 

If your business doesn’t use safety stock, you just don’t consider it in the equation. 

Following a couple of rules to determine ROP and having a well-defined inventory policy will help your company improve efficiency and be more productive, reducing costs and consequently losing less stock. 

Supply Brain can help you define when to buy and what the ideal quantity is, taking into account the economic order. In the tool you can also get product insights, with recommendations and prioritizations. Would you like to schedule a demonstration? Get in touch!

    Carol Gameleira

    Carol Gameleira

    Graduated in Public Relations and post graduated in Marketing by ESPM, Carol possess 7 years of experience in the area of Comunications and Digital Marketing, acting in the Artificial Inteligence and Supply Chain realm since 2020.