What is Inventory Planning? A Brief Dictionary of Inventory-Related Terms

Inventory Control concerns the management of physical goods, focusing on an accurate and up-to-the-minute count of every item in inventory and where it is located, as well as efficient retrieval of items. Relevant technologies include computer databases, barcoding, Radio Frequency Identification (RFID), and the use of robots for retrieval.

Inventory Management aims to execute the inventory policy defined by the company. Inventory Management is often accomplished using Enterprise Resource Planning (ERP) systems, which generate purchase orders, production orders, and reporting that details current inventory on hand, incoming, and up for order.

Inventory Planning sets operational policy details, such as item-specific reorder points and order quantities, and predicts future demand and supplier lead times. Important components of an inventory planning process include what-if scenarios for netting out on-hand inventory, analyzing how changes to demand, lead times, and stocking policies will impact ordering, as well as managing exceptions and contingencies.

Inventory Optimization utilizes an analytical process that computes values for inventory planning parameters (e.g., reorder points and order quantities) that optimize a numerical goal or “objective function” without violating a numerical constraint. For instance, an objective function might be to achieve the lowest possible inventory operating cost (defined as the sum of inventory holding costs, ordering costs, and shortage costs), and the constraint might be to achieve a fill rate of at least 90%. Using a mathematical model of the inventory system and probability forecasts of item demand, inventory optimization can quickly and automatically suggest how to best manage thousands of inventory items.

Explaining What “Service Level” Means in Your Inventory Optimization Software

Customers often ask us why a stocking recommendation is “so high.” Here is a question we received recently:

During our last team meeting, we found a few items with abnormal gaps between our current ROP and the Smart-suggested ROP at a 99% service level. The concern is that the system indicates that the reorder point will have to increase substantially to achieve a 99% service level. Would you please help us understand the calculation?

When we reviewed the data, it was clear to the customer that the Smart-calculated ROP was indeed correct.  We concluded (1) what they really wanted was a much lower service level target and (2) we had not done a good explaining what was really meant by “service level.” 

So, what does a “99% service level” really mean? 

When it pertains to the target that you enter in your inventory optimization software, it means that the stocking level for the item in question will have a 99% chance of being able to fill whatever the customer needs right away.  For instance, if you have 50 units in stock, there is a 99% chance that the next demand will fall somewhere in the range of 0 to 50 units.

What our customer meant was that 99% of the time a customer placed an order, it was delivered in full within whatever lead time the customer was quoted.  In other words, not necessarily right away but when promised.  

Obviously, the more time you give yourself to deliver to a customer the higher your service level will be. But that distinction is often not explicitly understood when new users of inventory optimization software are conducting what-if scenarios at different service levels.  And that can lead to considerable confusion.  Computing service levels based on immediate stock availability is a higher standard: harder to meet but much more competitive.

Our manufacturing customers often quote service levels based on lead times to their customers, so it isn’t essential for them to deliver immediately from the shelf. In contrast, our customers in the distribution, Maintenance Repair and Operations (MRO), and spare parts spaces, must normally ship same day or within 24 hours.  For them it is a competitive necessity to ship right away and do so in full.

When inputting target service levels using your inventory optimization software, keep this distinction in mind.  Choose the service level based on the percentage of the time you want to ship inventory in full, right away from the shelf.  

Don’t blame shortages on problematic lead times.

Lead time delays and supply variability are supply chain facts of life, yet inventory-carrying organizations are often caught by surprise when a supplier is late. An effective inventory planning process embraces this fact of life and develops policies that effectively account for this uncertainty. Sure, there will be times when lead time delays come out of nowhere and cause a shortage. But most often, the shortages result from:

  1. Not computing stocking policies (e.g., reorder points, safety stocks, and Min/Max levels) often enough to catch changes in the lead time. 
  2. Using poor estimates of actual lead time such as using only averages of historical receipts or relying on a supplier quote.

Instead, recalibrate policies across every single part during every planning cycle to catch changes in demand and lead times.  Rather than assuming only an average lead time, simulate the lead times using scenarios.  This way, recommended stocking policies account for the probabilities of lead times being high and adjust accordingly.  When you do this, you’ll identify needed inventory increases before it is too late. You’ll capture more sales and drive significant improvements in customer satisfaction.

Top 4 Moves When You Suspect Software is Inflating Inventory

We often are asked, “Why is the software driving up the inventory?” The answer is that Smart isn’t driving it in either direction – the inputs are driving it, and those inputs are controlled by the users (or admins). Here are four things you can do to get the results you expect.

1. Confirm that your service level targets are commensurate with what you want for that item or group of items. Setting very high targets (95% or more) will likely drive inventory up if you have been coasting along at a lower level and are OK with being there. It’s possible you’ve never achieved the new higher service level but customers have not complained.  Figure out what service level has worked by evaluating historical reports on performance and set your targets accordingly. But keep in mind that competitors may beat you on item availability if you keep using your father’s service level targets.

2. Make sure your understanding of “service level” aligns with the software system’s definition. You may be measuring performance based on how often you ship within one week from receipt of the customer order, whereas the software is targeting reorder points based on your ability to ship right away, not within a week. Clearly the latter will require more inventory to hit the same “service level.” For instance, a 75% same-day service level may correspond to a 90% same-week service level. In this case, you are really comparing apples to oranges. If this is the reason for the excess stock, then determine what “same day” service level is needed to get you to your desired “same week” service level and enter that into the software. Using the less-stringent same-day target will drop the inventory, sometimes very significantly.

3. Evaluate the lead time inputs. We’ve seen instances in which lead times had been inflated to trick old software into producing desired results. Modern software tracks suppliers’ performance by recording their actual lead times over multiple orders, then it takes account of lead time variability in its simulations of daily operations. Watch out if your lead times are fixed at one value that was decided on in the distant past and isn’t current.

4. Check your demand signal. You have lots of historical transactions in your ERP system that can be used in many ways to determine the demand history. If you are using signals such as transfers, or you are not excluding returns, then you may be overstating demand. Spend a little time on defining “demand” in the way that makes most sense for your situation.

Uncover data facts and improve inventory performance

The best inventory planning processes rely on statistical analysis to uncover relevant facts about the data. For instance:

  1. The range of demand values and supplier lead times to expect.
  2. The most likely values of item demand and supplier lead time.
  3. The full probability distributions of item demand and supplier lead time.

If you reach the third level, you have the facts required to answer important operational questions, additional questions such as:

  1. Exactly how much extra stock is needed to improve service levels by 5%?
  2. What will happen to on-time-delivery if inventory is reduced by 5%?
  3. Will either of the above changes generate a positive financial return?
  4. More generally, what service level target and associated inventory level is most profitable?

When you have the facts and add your business knowledge, you can make more informed stocking decisions that will generate significant returns. You’ll also set proper expectations with internal and external stakeholders, ensuring there are fewer unwelcome surprises.

Four Common Mistakes when Planning Replenishment Targets

Whether you are using ‘Min/Max’ or ‘reorder point’ and ‘order quantity’ to determine when and how much to restock, your approach might deliver or deny huge efficiencies. Key mistakes to avoid:

 

  1. Not recalibrating regularly
  2. Only reviewing Min/Max when there is a problem
  3. Using Forecasting methods not up to the task
  4. Assuming data is too slow moving or unpredictable for it to matter

 

We have over 150,000 SKU x Location combinations. Our demand is intermittent. Since it’s slow moving, we don’t need to recalculate our reorder points often. We do so maybe once annually but review the reorder points whenever there is a problem.” – Materials Manager.

 

This reactive approach will lead to millions in excess stock, stock outs, and lots of wasted time reviewing data when “something goes wrong.” Yet, I’ve heard this same refrain from so many inventory professionals over the years. Clearly, we need to do more to share why this thinking is so problematic.

It is true that for many parts, a recalculation of the reorder points with up-to-date historical data and lead times might not change much, especially if patterns such as trend or seasonality aren’t present. However, many parts will benefit from a recalculation, especially if lead times or recent demand has changed. Plus, the likelihood of significant change that necessitates a recalculation increases the longer you wait. Finally, those months with zero demands also influence the probabilities and shouldn’t be ignored outright. The key point though is that it is impossible to know what will change or won’t change in your forecast, so it’s better to recalibrate regularly.

 

  Planning Replenishment Targets Software calculate

This standout case from real world data illustrates a scenario where regular and automated recalibration shines—the benefits from quick responses to changing demand patterns like these add up quickly. In the above example, the X axis represents days, and the Y axis represents demand. If you were to wait several months between recalibrating your reorder points, you’d undoubtedly order far too soon. By recalibrating your reorder point far more often, you’ll catch the change in demand enabling much more accurate orders.

 

Rather than wait until you have a problem, recalibrate all parts every planning cycle at least once monthly. Doing so takes advantage of the latest data and proactively adjusts the stocking policy, thus avoiding problems that would cause manual reviews and inventory shortages or excess.

The nature of your (potentially varied) data also needs to be matched with the right forecasting tools. If records for some parts show trend or seasonal patterns, using targeting forecasting methods to accommodate these patterns can make a big difference. Similarly, if the data show frequent zero values (intermittent demand), forecasting methods not built around this special case can easily deliver unreliable results.

Automate, recalibrate and review exceptions. Purpose built software will do this automatically. Think of it another way: is it better to dump a bunch of money into your 401K once per year or “dollar cost average” by depositing smaller, equally sized amounts throughout the year. Recalibrating policies regularly will yield maximized returns over time, just as dollar cost averaging will do for your investment portfolio.

How often do you recalibrate your stocking policies? Why?