1. The setup will be straightforward.

We just need to feed our demand histories into our new statistical methods, and we can start planning more effectively.  Not quite: it’s about the technology and the process. You are investing in a new business process to develop forecasts for driving business strategy and inventory planning decisions. It will take time to get all stakeholders involved: sales, marketing, procurement, operations, and maintenance/technicians (for spare parts inventory).  Who owns the forecast? What will your items’ forecast hierarchy look like?  Where will the most business knowledge come from?  Is there a consensus process that will use the business knowledge to customize the forecasts to your particular situation? Does everyone understand the statistical methods?   Is there agreement on the underlying values that balance holding, ordering and (especially) shortage costs? Are you prepared to make choices along the crucial tradeoff curve relating inventory costs to customer service levels?  How do you plan on measuring forecasting accuracy/error? Does management understand the concept of “forecast value add” whereby you track the error with each version of the forecast (statistical error vs. sales forecast error vs. consensus error).  Without this context and agreed upon participation from key stake holders, the system will still be implemented but used in silo

2. All I need is historical demand data, and then I can start forecasting.

Almost.  Getting good data isn’t easy.  Are your demand history data complete and correct? Are your supplier data (e.g., lead times) also complete and correct? Have you recognized the special needs of new and end-of-life items? Sure, IT could export a file of aggregated demand data (weekly or monthly), but how do you know it is correct?  When orders and shipments are booked, they fall under a variety of different transactions codes.  You have to be able to know how to compose your demand signal.  Orders or shipments? Include or exclude returns? What about warehouse transfers?  What about returns that occur many periods after initial shipment?  How will my ERP interpret the forecast?  But wait…we are using a solution with an ERP connector that promises data will flow back and forth seamlessly.  An ERP connector will certainly cover the transfer of historical data and forecast results between systems but it won’t improve bad data quality.  You also have to make sure the ERP connector has the flexibility of determining how to compose your demand history.  For example, if it is hard coded to pull certain transactions types that you may not want or require different transactions it doesn’t include, you’ll need customizations.  There is also the problem of product supersession and/or location changes – i.e., Product A gets phased out and becomes Product B, or now Product A ships from a different warehouse.   Sounds simple, but if this happens often across thousands of items then it must be accounted for as part of an automatic forecasting process.  Otherwise, your users are required to manually manage this constant updating. Then you lose economies of scale. More “data wrangling” means more hassle, more errors, and missed decision deadlines. Less frequent updates can mean less accurate forecasts, which leads to excess inventory for some items and insufficient inventory for others

3. If we get a better forecast, we’ll have the right inventory, reduce stockouts, and increase service.

The demand forecast is one component of a larger process.  If you have another department that applies incorrect buffers (too much or too little safety stock), then a lot of the benefit of a more accurate forecast goes out the window.  You have to look holistically at forecasting within the context of inventory management. You can’t get maximum benefit (and in some cases, any benefit) unless you account for all components including buffer levels such as safety stock and reorder points, ordering rules, and managing supplier/internal lead times. It is not uncommon for buyers to implement rules of thumb inventory policies such as ordering early or inflating the forecast to reduce the risk of running out.  The opposite behavior where an order signal triggered by the forecast is deferred to a later date to prevent an order from being placed “too early” is equally prevalent.  This type of behavior is based on a pain avoidance response that occurs within companies that have an ad-hoc inventory planning process that doesn’t holistically connect the forecast to inventory strategy.  

4. The more forecasting models the better.

 This is true in some cases. In an ironic twist, the more models to choose from sometimes means you’ll have a greater chance of picking the wrong one.  This occurs even when there is an automated system selecting the right method.  This is because most automated forecasting systems still make the mistake of selecting methods based on best fit to past demand. This backward-looking approach usually results in poor performance when looking forward in time; this can be tested by waiting a bit and then comparing forecasted versus actual demand (or, if you don’t want to wait, by hiding some of the recent data and forecasting it, in which case the actuals are already in hand). In principle, having more models might be useful, but what is important is understanding the approach for model selection.  Furthermore, most forecasting models produce a single-number forecast (“Demand for Product A will be 17 units next month”) without any indication of the forecast uncertainty or margin of error. Without knowing the margin of error, you cannot appreciate and rationally manage forecast risk.

In our software, we offer automated time series selection that chooses from dozens of proven techniques on the basis of estimated future performance, not fit to past data.  We also go beyond single-number forecasting using probabilistic methods to generate thousands of forecast scenarios to assess forecast uncertainty.  We’ve found that this approach is considerably more accurate for certain types of data than the traditional tournament selection.  So, in these situations the number of models we’d recommend using is “One!”  Does that it make inferior?  Of course not.  Take the time to fine-tune your models in order to see what works best for your business.

5. With the right software, anybody can do the job well.

Would that it were so. However, after our involvement in decades of implementations, it is clear that not everybody should be at the demand planning keyboard. The job doesn’t need a super-hero, but certain traits make for success:

• Having a company-wide perspective. So many problems in demand planning stem from stove-piped thinking. A proper planning process surfaces the need for all stakeholders’ involvement, so a user unable to think beyond his or her previous fiefdom can be a liability.
• Being innumerate. A user who is not comfortable with numbers will struggle.
• Appreciating randomness. This is similar to innumeracy but goes beyond. Most of the friction in demand planning and inventory optimization derives from randomness: in product demand, in supplier lead time, etc. Without a good feel for how randomness causes trouble, a user will often be puzzled at how poorly his or her decisions turn out
• Being incurious. Top-flight software encourages users to game out “what if?” scenarios to see how to tweak automatically computed solutions to get even better results. If the user never gets into a “what if?” mentality, they will under perform. Furthermore, playing with alternative scenarios is one of the best ways to build an instinctive feel for the randomness in the system.

Conclusion

The five reasons outlined here show why implementing a forecasting, demand planning, or inventory optimization system isn’t as simple as turning on the software, importing your historical data, and getting some user training on how to operate the software.  You are implementing a new process for planning your business and determining stocking policy that will drive spending on inventory and impact your ability to capture sales.  However, the effort is well worth it.  Per an Institute of Business Forecasting (IBF) blog, a 1% reduction in under-forecast error at a $50 Million company yields a savings as much as $1.52M.  Conversely, the benefits of a 1% reduction in over-forecast error were $1.28M yielding an average benefit of $1.4M.  This means you stand to save your business $260,000 annually for every $10 Million in revenue! 

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 10 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.

10. What are you going to do next?

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.

There’s a stale old joke: “There are two types of people – those who believe there are two types of people, and those who don’t.” We can modify that joke: “There are two types of people – those who know there are three types of supply chain analytics, and those who haven’t yet read this blog.”

The three types of supply chain analytics are “descriptive”, “predictive”, and “prescriptive.” Each plays a different role in helping you manage your inventory. Modern supply chain software lets you exploit all three.

Descriptive Analytics

Descriptive Analytics are the stuff of dashboards. They tell you “what’s happenin’ now.” Included in this category are such summary numbers as dollars currently invested in inventory, current customer service level and fill rate, and average supplier lead times. These statistics are useful for keeping track of your operations, especially when you track changes in them from month to month. You will rely on them every day. They require accurate corporate databases, processed statistically.

Predictive Analytics

Predictive Analytics most commonly manifest as forecasts of demand, often broken down by product and location and sometimes also by customer. These statistics provide early warning so you can gear up production, staffing and raw material procurement to satisfy demand. They also provide predictions of the effect of changes in operating policies, e.g., what happens if we increase our order quantity for Product X from 20 to 25 units? You might rely on Predictive Analytics periodically, perhaps weekly or monthly, when you look up from what’s happening now to see what will happen next. Predictive Analytics uses Descriptive Analytics as a foundation but adds more capability. Predictive Analytics for demand forecasting requires advanced statistical processing to detect and estimate such features of product demand as trend, seasonality and regime change.  Predictive Analytics for inventory management uses forecasts of demand as inputs into models of the operation of inventory policies, which in turn provide estimates of key performance metrics such as service levels, fill rates, and operating costs.

Prescriptive Analytics

Prescriptive Analytics are not about what is happening now, or what will happen next, but about what you should do next, i.e., they recommend decisions aimed at maximizing inventory system performance. You might rely on Prescriptive Analytics to best posture your entire inventory policy. Prescriptive Analytics uses Predictive Analytics as a foundation then adds optimization capability. For instance, Prescriptive Analytics software can automatically work out the best choices for future values of Min’s and Max’s for thousands of inventory items. Here, “best” might mean the values of Min and Max for each item that minimize operating cost (the sum of holding, ordering, and shortage costs) while maintaining a 90% floor on item fill rate.

Example

The figure below shows how supply chain analytics can help the inventory manager. The columns show three predicted Key Performance Indicators (KPI’s): service level, inventory investment, and operating costs (holding costs + ordering costs + shortage costs).

 Figure 1: The three types of analytics used to evaluate planning scenarios

The rows show four alternative inventory policies, expressed as scenarios. The “Live” scenario reports on the values of the KPI’s on July 1, 2018. The “99% All” scenario changes the current policy by raising the service level of all items to 99%. The “75 floor/99 ceiling” scenario raises service levels that are too low up to 75% and lowers very high (i.e., expensive) service levels down to 95%. The “Optimization” scenario prescribes item specific service levels that minimizes total operating costs.

The “Live 07-01-2018” scenario is an example of Descriptive Analytics. It shows the current baseline performance. The software then allows the user to try out changes in inventory policy by creating new “What If” scenarios that might then be converted to named scenarios for further consideration. The next two scenarios are examples of Predictive Analytics. They both assess the consequences of their recommended inventory control policies, i.e., recommended values of Min and Max for all items. The “Optimization” scenario is an example of Prescriptive Analytics because it recommends a best compromise policy.

Consider how the three alternative scenarios compare to the baseline “Live” scenario. The “99% All” scenario raises the item availability metrics, increasing service level from 88% to 99%. However, doing so increases the total inventory investment from $3 million to about $4 million. In contrast, the “75 floor/99 ceiling” scenario increases both service level and reduces the cash tied up in inventory by about $300,000. Finally, the “Optimization” scenario achieves an 80% service level, a reduction from the current 88%, but it cuts more than $2 million from the inventory value and reduces operating costs by more than $400,000 annually. From here, managers could try further options, such as giving back some of the $2 million savings to achieve a higher average service level.

Summary

Modern software packages for inventory planning and inventory optimization should offer three kinds of supply chain analytics: Descriptive, Predictive, and Prescriptive. Their combination lets inventory managers track their operations (Descriptive), forecast where their operations will be in the future (Predictive), and optimize their inventory policies in response in anticipation of future conditions (Prescriptive).