Reveal Your Real Inventory Planning and Forecasting Policy by Answering These 9 Questions

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

In our last blog we posed the question:  How can you be sure that you really have a policy for inventory planning and demand forecasting? We explained how an organization’s lack of understanding on the basics (how a forecast is created, how safety stock buffers are determined, and how/why these values are adjusted) contributes to poor forecast accuracy, misallocated inventory, and lack of trust in the whole process.

In this blog, we review 9 specific questions you can ask to uncover what’s really happening at your company. We detail the typical answers provided when a forecasting/inventory planning policy doesn’t really exist, explain how to interpret these answers, and offer some clear advice on what to do about it.

Always start with a simple hypothetical example. Focusing on a specific problem you just experienced is bound to provoke defensive answers that hide the full story. The goal is to uncover the actual approach used to plan inventory and forecasts that has been baked into the mental math or spreadsheets.   Here is an example:

Suppose you have 100 units on hand, the lead time to replenish is 3 months, and the average monthly demand is 20 units?   When should you order more?  How much would you order? How will your answer change if expected receipts of 10 per month were scheduled to arrive?  How will your answer change if the item is the item is an A, B, or C item, the cost of the item is high or low, lead time of the item is long or short?  Simply put, when you schedule a production job or place a new order with a supplier, why did you do it? What triggered the decision to get more?  What planning inputs were considered?

When getting answers to the above question, focus on uncovering answers to the following questions:

1. What is the underlying replenishment approach? This will typically be one of Min/Max, forecast/safety stock, Reorder Point/Order Quantity, Periodic Review/Order Up To or even some odd combination

2. How are the planning parameters, such as demand forecasts, reorder points, or Min/Max, actually calculated? It’s not enough to know that you use Min/Max.  You have to know exactly how these values are calculated. Answers such as “We use history” or “We use an average” are not specific enough.   You’ll need answers that clearly outline how history is used.  For example, “We take an average of the last 6 months, divide that by 30 to get a daily average, and then multiply that by the lead time in days.  For ‘A’ items we then multiply the lead time average by 2 and for ‘B’ items we use a multiplier of 1.5.” (While that is not an especially good technical approach, at least it has a clear logic.)

Once you have a policy well-defined, you can identify its weaknesses in order to improve it.  But if the answer provided doesn’t get much further past “We use history”, then you don’t have a policy to start with.   Answers will often reveal that different planners use history in different ways.  Some may only consider the most recent demand, others might stock according to the average of the highest demand periods, etc.  In other words, you may find that you actually have multiple ill-conceived “policies”.

3. Are forecasts used to drive replenishment planning and if so, how? Many companies will say they forecast, but their forecasts are calculated and used differently. Is the forecast used to predict what on hand inventory will be in the future, resulting in an order being triggered?  Or is it used to derive a reorder point but not to predict when to order (i.e. I predict we’ll sell 10 a week so to help protect against stock out, I’ll order more when on hand gets to 15)? Is it used as a guide for the planner to help subjectively determine when they should order more?  Is it used to set up blanket orders with suppliers?  Some use it to drive MRP. You’ll need to know these specifics.  A thorough answer to this question might look like this: “My forecast is 10 per week and my lead time is 3 weeks so I make my reorder point a multiple of that forecast, typically 2 x lead time demand or 60 unit for important items and I use a smaller multiple for less important items.  (Again, not a great technical approach, but clear.)

4.  What technique is actually used to generate the forecast? Is it an average, a trending model such as double exponential smoothing, a seasonal model? Does the choice of technique change depend on the type of demand data or when new demand data is available? (Spare parts and high-volume items have very different demand patterns.) How do you go about selecting the forecast model? Is this process automated?  How often is the choice of model reconsidered?  How often are the model parameters recomputed? What is the process used to reconsider your approach?  The answer here documents how the baseline forecasts are produced.  Once determined, you can conduct an analysis to identify whether other forecasting methods would improve forecast accuracy.  If you aren’t documenting forecast accuracy and conducting “forecast value add” analysis then you aren’t in a position to properly assess whether the forecasts being produced are the best that they can be.  You’ll miss out on opportunities to improve the process, increase forecast accuracy, and educate the business on what type of forecast error is normal and should be expected.

5. How do you use safety stock? Notice the question was not “Do you use safety stock?” In this context, and to keep it simple, the term “safety stock” means stock used to buffer inventory against supply and demand variability.  All companies use buffering approaches in some way.  There are some exceptions though.  Maybe you are a job shop manufacturer that procures all parts to order and your customers are completely fine waiting weeks or months for you to source material, manufacture, QA, and ship.  Or maybe you are high-volume manufacturer with tons of buying power so your suppliers set up local warehouses that are stocked full and ready to provide inventory to you almost immediately.  If these descriptions don’t describe your company, you will definitely have some sort of buffer to protect against demand and supply variability.  You may not use the “safety stock” field in your ERP but you are definitely buffering.

Answers might be provided such as “We don’t use safety stock because we forecast.”  Unfortunately, a good forecast will have a 50/50 chance of being over/under the actual demand.  This means you’ll incur a stock out 50% of the time without a safety stock buffer added to the forecast.  Forecasts are only perfect when there is no randomness. Since there is always randomness, you’ll need to buffer if you don’t want to have abysmal service levels.

If the answer isn’t revealed, you can probe a bit more into how the varying replenishment levers are used to add possible buffers which leads to questions 6 & 7.

6. Do you ever increase the lead time or order earlier than you truly need to?
In our hypothetical example, your supplier typically takes 4 weeks to deliver and is pretty consistent. But to protect against stockouts your buyer routinely orders 6 weeks out instead of 4 weeks.  The safety stock field in your ERP system might be set to zero because “we don’t use safety stock”, but in reality, the buyer’s ordering approach just added 2 weeks of buffer stock.

7. Do you pad the demand forecast?
In our example, the planner expects to consume 10 units per month but “just in case” enters a forecast of 20 per month.  The safety stock field in the MRP system is left blank but the now disguised buffer stock has been smuggled into the demand forecast.  This is a mistake that introduces “forecast bias.”  Not only will your forecasts be less accurate but if the bias isn’t accounted for and safety stock is added by other departments, you will overstock.

The ad-hoc nature of the above approaches compounds the problems by not considering the actual demand or supply variability of the item. For example, the planner might simply make a rule of thumb that doubles the lead time forecast for important items.  One-size doesn’t fit all when it comes to inventory management.  This approach will substantially overstock the predictable items while substantially understocking the intermittently demanded items. You can read “Beware of Simple Rules of Thumb for Managing Inventory” to learn more about why this type of approach is so costly.

The ad-hoc nature of the approaches also ignores what happens the company is faced with a huge overstock or stock out. When trying to understand what happened, the stated policies will be examined. In the case of an overstock, the system will show zero safety stock.  The business leaders will assume they aren’t carrying any safety stock, scratch their heads, and eventually just blame the forecast, declare “Our business can’t be forecasted” and stumble on. They may even blame the supplier for shipping too early and making them hold more than needed. In the case of a stock out, they will think they aren’t carrying enough and arbitrarily add more stock across many items not realizing there is in fact lots of extra safety stock baked into process.  This makes it more likely inventory will need to be written off in the future.

8. What is the exact inventory terminology used? Define what you mean by safety stock, Min, reorder point, EOQ, etc.  While there are standard technical definitions it’s possible that something differs, and miscommunication here will be problematic.  For example, some companies refer to Min as the amount of inventory needed to satisfy lead time demand while some may define Min as inclusive of both lead time demand and safety stock to buffer against demand variability. Others may mean the minimum order quantity.

9. Is on hand inventory consistent with the policy? When your detective work is done and everything is documented, open your spreadsheet or ERP system and look at the on-hand quantity. It should be more or less in line with your planning parameters (i.e. if Min/Max is 20/40 and typical lead time demand is 10, then you should have roughly 10 to 40 units on hand at any given point in time.  Surprisingly, for many companies there is often a huge inconsistency. We have observed situations where the Min/Max setting is 20/40 but the on-hand inventory is 300+.  This indicates that whatever policy has been prescribed just isn’t being followed.   That’s a bigger problem.

Going Forward

Demand forecasting and inventory stocking policy need to be well-defined processes that are understood and accepted by everybody involved.  There should be zero mystery.

To do this right, the demand and supply variability must be analyzed and used to compute the proper levels of safety stock.   Adding buffers without an implicit understanding of what each additional unit of buffer stock is buying you in terms of service is like arbitrarily throwing a handful of ingredients into a cake recipe.  A small change in ingredients can have a huge impact on what comes out of the oven – one bite too sweet but the next too sour.  It is the same with inventory management.  A little extra here, a little less there, and pretty soon you find yourself with costly excess inventory in some areas, painful shortages in others, no idea how you got there, and with little guidance on how to make things better.

Modern inventory optimization and demand planning software with its advanced analytics and strong basis in forecast analysis can help a good deal with this problem. But even the best software won’t help if it is used inconsistently.

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Reveal Your Real Inventory Planning and Forecasting Policy by Answering These 9 Questions

Reveal Your Real Inventory Planning and Forecasting Policy by Answering These 9 Questions

In this blog, we review 9 specific questions you can ask to uncover what’s really happening with the inventory planning and demand forecasting policy at your company. We detail the typical answers provided when a forecasting/inventory planning policy doesn’t really exist, explain how to interpret these answers, and offer some clear advice on what to do about it.

How to Tell You Don’t Really Have an Inventory Planning and Forecasting Policy

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You can’t properly manage your inventory levels, let alone optimize them, if you don’t have a handle on exactly how demand forecasts and stocking parameters (such as Min/Max, safety stocks, and reorder points, and order quantities) are determined. Many organizations cannot specify how policy inputs are calculated or identify situations calling for management overrides to the policy. If you have these problems, you may be wasting hundreds of thousands to millions of dollars each year in unnecessary shortage costs, holding costs, and ordering costs.

How to Tell You Don’t Really Have an Inventory Planning and Forecasting Policy

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

You can’t properly manage your inventory levels, let alone optimize them, if you don’t have a handle on exactly how demand forecasts and stocking parameters (such as Min/Max, safety stocks, and reorder points, and order quantities) are determined.

Many organizations cannot specify how policy inputs are calculated or identify situations calling for management overrides to the policy.   For example, many people can say they rely on a particular planning method such as Min/Max, reorder point, or forecast with safety stock, but they can’t say exactly how these planning inputs are calculated.  More fundamentally, they may not understand what would happen to their KPI’s if they were to change Min,Max, or Safety Stock. They may know that the forecast relies on “averages” or “history” or “sales input”, but specific details about how the final forecast is arrived at are unclear.

Often enough, a company’s inventory planning and forecasting logic was developed by a former employee or vanished consultant and entombed in a spreadsheet.  It otherwise may rely on outdated ERP functionality or ERP customization by an IT organization that incorrectly assumed that ERP software can and should do everything. (Read this great and, as they say, “funny because it’s true,” blog by Shaun Snapp about ERP Centric Strategies.)  The policy may not have been properly documented, and no one currently on the job can improve it or use it to best advantage.

This unhappy situation leads to another, in which buyers and inventory planners flat out ignore the output from the ERP system, forcing reliance on Microsoft Excel to determine order schedules.  Ad hoc methods are developed that impede cohesive responses to operational issues and aren’t visible to the rest of the organization (unless you want your CFO to learn the complex and finicky spreadsheet).  These methods often rely on rules of thumb, averaging techniques, or textbook statistics without a full understanding of their shortcomings or applicability.  And even when documented, most companies often discover that actual ordering strays from the documented policy.  One company we consulted for had on hand inventory levels that were routinely 2 x’s the Max quantity!  In other words, there isn’t really a policy at all.

In summary, many current inventory and demand forecast “systems” were developed out of distrust for the previous system’s suggestions but don’t actually improve KPI’s.  They also force the organization to rely on a few employees to manage demand forecasting, daily ordering, and inventory replenishment.

And when there is a problem, it is impossible for the executive team to unwind how you got there, because there are too many moving parts.  For example, was the excess stock the fault of an inaccurate demand forecast that relied on an averaging method that didn’t account for a declining demand?  Or was it due to an outdated lead time setting that was higher than it should’ve been?  Or was it due to a forecast override a planner made to account for an order that just never happened?  And who gave the feedback to make that override?  A customer? Salesperson?

Do you have any of these problems?  If so, you are wasting hundreds of thousands to millions of dollars each year in unnecessary shortage costs, holding costs, and ordering costs.  What would you be able to do with that extra cash?  Imagine the impact that this would have on your business.

In our next blog, we’ll review specific questions you can ask to uncover what’s really happening at your company, detail the typical answers provided when a forecasting/inventory planning policy doesn’t really exist, explain how to interpret these answers, and offer some clear advice on what to do about it.

 

Leave a Comment

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Don’t Become a Victim of Your Forecast Models

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Generally, the supply chain field has lagged behind finance in terms of the use of statistical models. My university colleagues and I are chipping away at that, but we have a long way to go. Some supply chains are quite technically sophisticated, but many, perhaps more, are essentially managed as much by gut instinct as by the numbers. Is this avoidance of analytics safer than relying on models?

Reveal Your Real Inventory Planning and Forecasting Policy by Answering These 9 Questions

Reveal Your Real Inventory Planning and Forecasting Policy by Answering These 9 Questions

In this blog, we review 9 specific questions you can ask to uncover what’s really happening with the inventory planning and demand forecasting policy at your company. We detail the typical answers provided when a forecasting/inventory planning policy doesn’t really exist, explain how to interpret these answers, and offer some clear advice on what to do about it.

How to Tell You Don’t Really Have an Inventory Planning and Forecasting Policy

How to Tell You Don’t Really Have an Inventory Planning and Forecasting Policy

You can’t properly manage your inventory levels, let alone optimize them, if you don’t have a handle on exactly how demand forecasts and stocking parameters (such as Min/Max, safety stocks, and reorder points, and order quantities) are determined. Many organizations cannot specify how policy inputs are calculated or identify situations calling for management overrides to the policy. If you have these problems, you may be wasting hundreds of thousands to millions of dollars each year in unnecessary shortage costs, holding costs, and ordering costs.

The 3 levels of forecasting: Point forecasts, Interval forecasts, Probability forecasts
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The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

Most demand forecasts are partial or incomplete: They provide only one single number: the most likely value of future demand. This is called a point forecast. Usually, the point forecast estimates the average value of future demand.  Interval forecasts provide an estimate of the possible future range of demand (i.e. demand has a 90% chance of being between 50 – 100 units).  Probabilistic forecasts take it a step further and provide additional information.  Knowing more is always better than knowing less and the probabilistic forecast provides that extra information so crucial for inventory management. This video blog by Dr. Thomas Willemain explains each type of forecast and the advantages of probabilistic forecasting.

 

Watch Now

 

 

Point forecast (green) shows what is most likely to happen.  The Interval Forecast shows the range (blue) of possibilities.

 

Probability Forecast shows the probability of each value occurring

 

 

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Related Posts

Don’t Become a Victim of Your Forecast Models

Don’t Become a Victim of Your Forecast Models

Generally, the supply chain field has lagged behind finance in terms of the use of statistical models. My university colleagues and I are chipping away at that, but we have a long way to go. Some supply chains are quite technically sophisticated, but many, perhaps more, are essentially managed as much by gut instinct as by the numbers. Is this avoidance of analytics safer than relying on models?

Reveal Your Real Inventory Planning and Forecasting Policy by Answering These 9 Questions

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In this blog, we review 9 specific questions you can ask to uncover what’s really happening with the inventory planning and demand forecasting policy at your company. We detail the typical answers provided when a forecasting/inventory planning policy doesn’t really exist, explain how to interpret these answers, and offer some clear advice on what to do about it.

Undershoot is Sabotaging your Service Level!

Undershoot is Sabotaging your Service Level!

Undershoot means that the lead time begins not at the reorder point but below it. Undershoot happens every time the demand that breached the reorder point took the stock down below (not down to) the reorder point. Undershoot picks your pocket before you even begin to roll the dice. It deludes the inventory professional into thinking his or her reorder points are sufficient to achieve their targets, whereas actual performance will not make the grade.

Smart Software and ArcherPoint Team Up to Launch Smart IP&O for NAV

Collaboration Provides Smart Inventory Planning, Forecasting, & Optimization for Microsoft NAV

Boston MA., June 5, 2018 – Smart Software, Inc. is excited to announce the successful integration of its cloud-based Inventory Planning and Optimization software with Microsoft Dynamics NAV to create Smart IP&O for NAV.  Smart Software partnered with ArcherPoint Inc., a Microsoft Dynamics ERP Gold Partner and full-service provider for Dynamics NAV and Dynamics 365 to build the connector.

Smart Software is a global provider of next generation 100% web-based demand planning, forecasting, and inventory optimization solutions. ArcherPoint created the connector to integrate Smart Software’s tools with Microsoft Dynamics NAV. The new integration brings the cloud-based Smart IP&O (Inventory Planning and Optimization) into the latest version of Microsoft’s ERP solution. By seamlessly integrating strategic planning in Smart IP&O with operational execution in Dynamics NAV, business users can continuously predict, respond, and plan more effectively in today’s uncertain business environment.

Jim Benson, sales executive from ArcherPoint says, Smart Software helps our customers by delivering insightful business analytics for inventory modeling and forecasting that drive ordering and replenishment in the latest version of Microsoft NAV. With Smart IP&O, our customers gain a means to shape inventory strategy to align with the business objectives, while empowering their planning teams to reduce inventory and improve service. In today’s supply chain, it is no longer enough to simply manage inventory. It must be optimized.”

The Smart/NAV integration makes all transactional data in NAV, such as shipments, sales orders, receipts, inventory on hand, and more, available in Smart IP&O’s data model. Smart IP&O brings this data to life leveraging field-proven analytics and forecasting methods. This enables executives and their planning team to identify operational inefficiencies, accurately forecast demand, model the financial and customer impact of current and proposed inventory policies, and return optimal planning parameters and forecasts to drive replenishment.

Greg Hartunian, CEO of Smart Software stated, “Businesses that leverage inventory optimization and forecasting technology are able to better understand their operations, lower costs, improve customer service, and outperform the competition. We look forward to working closely with ArcherPoint to help our joint customers achieve these key benefits.”

To learn more about the Smart IP&O for NAV and how it can help your business, please join us for a free webinar, Wednesday, June 27 at 2pm ET. We will provide a demo on the software, uses, and benefits of the product.  To register for the webinar please visit: https://www.archerpoint.com/events/lunch-and-learn-archerpoint-smart-inventory-planning-and-optimization

About Smart Software
Smart Software, a leading innovator in demand planning and inventory optimization software, offers Smart IP&O, an integrated suite of web-based demand planning, inventory optimization and supply chain analytics applications.  Smart Software has collaborated with ArcherPoint to develop an automated integration with Microsoft Dynamics NAV, enabling the transparent flow of data and results to drive Sales, Inventory and Operations Planning.  Founded in 1984, Smart serves a wide range of manufacturing, distribution, and transportation organizations including The Home Depot, FedEx, SCIEX, DisneyLand Resorts, MARS, BC Transit, Metro-North Railroad and many more.  Learn more at www.smartcorp.com.

About ArcherPoint
ArcherPoint has built a business around adaptive innovation. Regardless of industry, companies look to ArcherPoint as a business solution provider and partner they can depend on to deliver results. Our history with Microsoft Dynamics NAV dates back to the product’s beginnings. Today, our team includes experts all over the world, not only in Dynamics NAV solution designdevelopment, 24/7 support, and upgrades, but also in accounting, manufacturingretaildistribution, and other key areas of business. With a commitment to quality service, ArcherPoint is dedicated to helping companies realize true business value by giving them access to world-class ERP solutions that will grow with them to meet their needs now and in their future.


For more information, please contact Smart Software, Inc., Four Hill Road, Belmont, MA 02478.
Phone: 1-800-SMART-99 (800-762-7899); FAX: 1-617-489-2748; E-mail: info@smartcorp.com

The Real Culprits of Stockouts and Excess

The Smart Forecaster

Pursuing best practices in demand planning, forecasting and inventory optimization

What is to blame for having too much of the stuff you don’t need and not enough of the stuff you do need?  Demand and supply variability are often blamed.  These problems are significant and seems impossible to overcome leaving many organizations to simply accept misallocated stock as a cost of doing business.  However, the real problem it isn’t simply late supplier deliveries and unpredictable demand.  These are supply chain planning “facts of life” and it’s how your company addresses them that counts.  Watch Greg Hartunian’s vlog to hear his thoughts and what you can do about it.

 

 

Smart Inventory Planning and Optimization automatically calculates the stocking policy that yields the best return for your business considering holding costs, ordering costs, and stock outs.  To see it in action, register below to watch a 12 minute demonstration.

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Related Posts

Don’t Become a Victim of Your Forecast Models

Don’t Become a Victim of Your Forecast Models

Generally, the supply chain field has lagged behind finance in terms of the use of statistical models. My university colleagues and I are chipping away at that, but we have a long way to go. Some supply chains are quite technically sophisticated, but many, perhaps more, are essentially managed as much by gut instinct as by the numbers. Is this avoidance of analytics safer than relying on models?

Reveal Your Real Inventory Planning and Forecasting Policy by Answering These 9 Questions

Reveal Your Real Inventory Planning and Forecasting Policy by Answering These 9 Questions

In this blog, we review 9 specific questions you can ask to uncover what’s really happening with the inventory planning and demand forecasting policy at your company. We detail the typical answers provided when a forecasting/inventory planning policy doesn’t really exist, explain how to interpret these answers, and offer some clear advice on what to do about it.

How to Tell You Don’t Really Have an Inventory Planning and Forecasting Policy

How to Tell You Don’t Really Have an Inventory Planning and Forecasting Policy

You can’t properly manage your inventory levels, let alone optimize them, if you don’t have a handle on exactly how demand forecasts and stocking parameters (such as Min/Max, safety stocks, and reorder points, and order quantities) are determined. Many organizations cannot specify how policy inputs are calculated or identify situations calling for management overrides to the policy. If you have these problems, you may be wasting hundreds of thousands to millions of dollars each year in unnecessary shortage costs, holding costs, and ordering costs.