I can’t imagine being an inventory planner in spare parts, distribution, or manufacturing and having to create safety stock levels, reorder points, and order suggestions without using key performance predictions of service levels, fill rates, and inventory costs:

Smart’s Inventory Optimization solution generates out-of-the-box key performance predictions that dynamically simulate how your current stocking policies will perform against possible future demands.  It reports on how often you’ll stock out, the size of the stockouts, the value of your inventory, holding costs, and more.  It lets you proactively identify problems before they occur so you can take corrective action in the short term. You can create what-if scenarios by setting targeted service levels and modifying lead times so you an see the predicted impact of these changes before committing to it.

For example,

• You can see if a proposed move from the current service level of 90% to a targeted service level of 97% is financially advantageous
• You can automatically identify if a different service level target is even more profitable to your business that the proposed target.
• You can see exactly how much you’ll need to increase your reorder points to accommodate a longer lead time.

If you aren’t equipping planners with the right tools, they’ll be forced to set stocking policies, safety stock levels, and create demand forecasts in Excel or with outdated ERP functionality.   Not knowing how policies are predicted to perform will leave your company ill equipped to properly allocate inventory.  Contact us today to learn how we can help!

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.

Just as the renowned astronomer Copernicus transformed our understanding of astronomy by placing the sun at the center of our universe, today, we invite you to re-center your approach to inventory management. And while not quite as enlightening, this advice will help your company avoid being caught in the gravitational pull of inventory woes—constantly orbiting between stockouts, surplus gravity, and the unexpected cosmic expenses of expediting?

In this article, we’ll walk you through the process of crafting a spare parts inventory plan that prioritizes availability metrics such as service levels and fill rates while ensuring cost efficiency. We’ll focus on an approach to inventory planning called Service Level-Driven Inventory Optimization. Next, we’ll discuss how to determine what parts you should include in your inventory and those that might not be necessary. Lastly, we’ll explore ways to enhance your service-level-driven inventory plan consistently.

In service-oriented businesses, the consequences of stockouts are often very significant.  Achieving high service levels depends on having the right parts at the right time. However, having the right parts isn’t the only factor. Your Supply Chain Team must develop a consensus inventory plan for every part, then continuously update it to reflect real-time changes in demand, supply, and financial priorities.

Managing inventory with Service-level-driven planning combines the ability to plan thousands of items with high-level strategic modeling. This requires addressing core issues facing inventory executives:

• Lack of control over supply and associated lead times.
• Unpredictable intermittent demand.
• Conflicting priorities between maintenance/mechanical teams and Materials Management.
• Reactive “wait and see” approach to planning.
• Misallocated inventory, causing stockouts and excess.
• Lack of trust in systems and processes.

The key to optimal service parts management is to grasp the balance between providing excellent service and controlling costs. To do this, we must compare the costs of stockout with the cost of carrying additional spare parts inventory. The costs of a stockout will be higher for critical or emergency spares, when there is a service level agreement with external customers, for parts used in multiple assets, for parts with longer supplier lead times, and for parts with a single supplier. The cost of inventory may be assessed by considering the unit costs, interest rates, warehouse space that will be consumed, and potential for obsolescence (parts used on a soon-to-be-retired fleet have a higher obsolescence risk, for example).

To arbitrate how much stock should be put on the shelf for each part, it is critical to establish consensus on the desired key metrics that expose the tradeoffs the business must make to achieve the desired KPIs. These KPIs will include Service Levels that tell you how often you meet usage needs without falling short on stock, Fill Rates that tell you what percentage of demand is filled, and Ordering costs detail the expenses incurred when you place and receive replenishment orders. You also have Holding costs, which encompass expenses like obsolescence, taxes, and warehousing, and Shortage costs that pertain to expenses incurred when stockouts happen.

An MRO business or Aftermarket Parts Planning team might desire a 99% service level across all parts – i.e., the minimum stockout risk that they are willing to accept is 1%. But what if the amount of inventory needed to support that service level is too expensive? To make an informed decision on whether there is going to be a return on that additional inventory investment, you’ll need to know the stockout costs and compare that to the inventory costs. To get stockout costs, multiply two key elements: the cost per stockout and the projected number of stockouts. To get inventory value, multiply the units required by the unit cost of each part. Then determine the annual holding costs (typically 25-35% of the unit cost). Choose the option that yields a total lower cost. In other words, if the benefit associated with adding more stock (reduced shortage costs) outweighs the cost (higher inventory holding costs), then go for it. A thorough understanding of these metrics and the associated tradeoffs serves as the compass for decision-making.

Modern software aids in this process by allowing you to simulate a multitude of future scenarios. By doing so, you can assess how well your current inventory stocking strategies are likely to perform in the face of different demand and supply patterns. If anything falls short or goes awry, it’s time to recalibrate your approach, factoring in current data on usage history, supplier lead times, and costs to prevent both stockouts and overstock situations.

Enhance your service-level-driven inventory plan consistently.

In conclusion, it’s crucial to assess your service-level-driven plan continuously. By systematically constructing and refining performance scenarios, you can define key metrics and goals, benchmark expected performance, and automate the calculation of stocking policies for all items. This iterative process involves monitoring, revising, and repeating each planning cycle.

The depth of your analysis within these stocking policies relies on the data at your disposal and the configuration capabilities of your planning system. To achieve optimal outcomes, it’s imperative to maintain ongoing data analysis. This implies that a manual approach to data examination is typically insufficient for the needs of most organizations.

For information on how Smart Software can help you meet your service supply chain goals with service-driven planning and more, visit the following blogs.

–   “Explaining What  Service-Level Means in Your Inventory Optimization Software”  Stocking recommendations can be puzzling, especially when they clash with real-world needs.  In this post, we’ll break down what that 99% service level means and why it’s crucial for managing inventory effectively and keeping customers satisfied in today’s competitive landscape.

–  “Service-Level-Driven Planning for Service Parts Businesses” Service-Level-Driven Service Parts Planning is a four-step process that extends beyond simplified forecasting and rule-of-thumb safety stocks. It provides service parts planners with data-driven, risk-adjusted decision support.

–   “How to Choose a Target Service Level.” This is a strategic decision about inventory risk management, considering current service levels and fill rates, replenishment lead times, and trade-offs between capital, stocking and opportunity costs.  Learn approaches that can help.

–   “The Right Forecast Accuracy Metric for Inventory Planning.”  Just because you set a service level target doesn’t mean you’ll actually achieve it. If you are interested in optimizing stock levels, focus on the accuracy of the service level projection. Learn how.

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.  

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.

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.