In this article, we will review the “suggested orders” functionality in Epicor BisTrack, explain its limitations, and summarize how Smart Inventory Planning & Optimization (Smart IP&O) can help reduce inventory & minimize stock-outs by accurately assessing the tradeoffs between stockout risks and inventory costs.

Automating Replenishment in Epicor BisTrack
Epicor BisTrack’s “Suggested Ordering” can manage replenishment by suggesting what to order and when via reorder point-based policies such as min-max and/or manually specified weeks of supply. BisTrack contains some basic functionality to compute these parameters based on average usage or sales, supplier lead time, and/or user-defined seasonal adjustments. Alternatively, reorder points can be specified completely manually. BisTrack will then present the user with a list of suggested orders by reconciling incoming supply, current on hand, outgoing demand, and stocking policies.

How Epicor BisTrack “Suggested Ordering” Works
To get a list of suggested orders, users specify the methods behind the suggestions, including locations for which to place orders and how to determine the inventory policies that govern when a suggestion is made and in what quantity.

First, the “method” field is specified from the following options to determine what kind of suggestion is generated and for which location(s):

Purchase – Generate purchase order recommendations.

1. Centralized for all branches – Generates suggestions for a single location that buys for all other locations.
2. By individual branch – Generates suggestions for multiple locations (vendors would ship directly to each branch).
3. By source branch – Generates suggestions for a source branch that will transfer material to branches that it services (“hub and spoke”).
4. Individual branches with transfers – Generates suggestions for an individual branch that will transfer material to branches that it services (“hub and spoke”, where the “hub” does not need to be a source branch).

Manufacture – Generate work order suggestions for manufactured goods.

1. By manufacture branch.
2. By individual branch.

Transfer from source branch – Generate transfer suggestions from a given branch to other branches.

Next, the “suggest order to” is specified from the following options:

1. Minimum – Suggests orders “up to” the minimum on hand quantity (“min”). For any item where supply is less than the min, BisTrack will suggest an order suggestion to replenish up to this quantity.
2. Maximum when less than min – Suggests orders “up to” a maximum on-hand quantity when the minimum on-hand quantity is breached (e.g. a min-max inventory policy).

3. Based on cover (usage) – Suggests orders based on coverage for a user-defined number of weeks of supply with respect to a specified lead time. Given internal usage as demand, BisTrack will recommend orders where supply is less than the desired coverage to cover the difference.

4. Based on over (sales) – Suggests orders based on coverage for a user-defined number of weeks of supply with respect to a specified lead time. Given sales orders as demand, BisTrack will recommend orders where supply is less than the desired coverage to cover the difference.

5. Maximum only – Suggests orders “up to” a maximum on-hand quantity where supply is less than this max.

Finally, if allowing BisTrack to determine the reorder thresholds, users can specify additional inventory coverage as buffer stock, lead times, how many months of historical demand to consider, and can also manually define period-by-period weighting schemes to approximate seasonality. The user will be handed a list of suggested orders based on the defined criteria. A buyer can then generate POs for suppliers with the click of a button.

Limitations

Rule-of-thumb Methods

While BisTrack enables organizations to generate reorder points automatically, these methods rely on simple averages that do not capture seasonality, trends, or the volatility in an item’s demand. Averages will always lag behind these patterns and are unable to pick up on trends. Consider a highly seasonal product like a snow shovel—if we take an average of Summer/Fall demand as we approach the Winter season instead of looking ahead, then the recommendations will be based on the slower periods instead of anticipating upcoming demand. Even if we consider an entire years’ worth of history or more, the recommendations will overcompensate during the slower months and underestimate the busy season without manual intervention.

Rule of thumb methods also fail when used to buffer against supply and demand variability.  For example, the average demand over the lead time might be 20 units.  However, a planner would often want to stock more than 20 units to avoid stocking out if lead times are longer than expected or demand is higher than the average.  BisTrack allows users to specify the reorder points based on multiples of the averages.  However, because the multiples don’t account for the level of predictability and variability in the demand, you’ll always overstock predictable items and understock unpredictable ones.   Read this article to learn more about why multiples of the average fail when it comes to developing the right reorder point.

Manual Entry
Speaking of seasonality referenced earlier, BisTrack does allow the user to approximate it through the use of manually entered “weights” for each period. This forces the user to have to decide what that seasonal pattern looks like—for every item. Even beyond that, the user must dictate how many extra weeks of supply to carry to buffer against stockouts, and must specify what lead time to plan around. Is 2 weeks extra supply enough? Is 3 enough? Or is that too much? There is no way to know without guessing, and what makes sense for one item might not be the right approach for all items.

Intermittent Demand
Many BisTrack customers may consider certain items “unforecastable” because of the intermittent or “lumpy” nature of their demand. In other words, items that are characterized by sporadic demand, large spikes in demand, and periods of little or no demand at all. Traditional methods—and rule-of-thumb approaches especially—won’t work for these kinds of items. For example, 2 extra weeks of supply for a highly predictable, stable item might be way too much; for an item with highly volatile demand, this same rule might not be enough. Without a reliable way to objectively assess this volatility for each item, buyers are left guessing when to buy and how much.

Reverting to Spreadsheets
The reality is most BisTrack users tend to do the bulk of their planning off-line, in Excel. Spreadsheets aren’t purpose-built for forecasting and inventory optimization. Users will often bake in user-defined rule of thumb methods that often do more harm than good.  Once calculated, users must input the information back into BisTrack manually. The time consuming nature of the process leads companies to infrequently compute their inventory policies – Many months and on occasion years go by in between mass updates leading to a “set it and forget it” reactive approach, where the only time a buyer/planner reviews inventory policy is at the time of order.  When policies are reviewed after the order point is already breached, it is too late.  When the order point is deemed too high, manual interrogation is required to review history, calculate forecasts, assess buffer positions, and to recalibrate.  The sheer volume of orders means that buyers will just release orders rather than take the painstaking time to review everything, leading to significant excess stock.  If the reorder point is too low, it’s already too late.  An expedite may now be required, driving up costs, assuming the customer doesn’t simply go elsewhere.

Epicor is Smarter
Epicor has partnered with Smart Software and offers Smart IP&O as a cross platform add-on to its ERP solutions including BisTrack, a speciality ERP for the Lumber, hardware, and building material industry.  The Smart IP&O solution comes complete with a bidirectional integration to BisTrack.  This enables Epicor customers to leverage built-for-purpose best of breed inventory optimization applications.  With Epicor Smart IP&O you can generate forecasts that capture trend and seasonality without manual configurations.  You will be able to automatically recalibrate inventory policies using field proven, cutting-edge statistical and probabilistic models that were engineered to accurately plan for intermittent demand.   Safety stocks will accurately account for demand and supply variability, business conditions, and priorities.  You can leverage service level driven planning so you have just enough stock or turn on optimization methods that prescribe the most profitable stocking policies and service levels that consider the real cost of carrying inventory. You can support commodity buys with accurate demand forecasting over longer horizons, and run “what-if” scenarios to assess alternative strategies before execution of the plan.

Smart IP&O customers routinely realize 7 figure annual returns from reduced expedites, increased sales, and less excess stock, all the while gaining a competitive edge by differentiating themselves on improved customer service. To see a recorded webinar hosted by the Epicor Users Group that profiles Smart’s Demand Planning and Inventory Optimization platform, please register here.

Service-Level-Driven Service Parts Planning for Microsoft Dynamics BC or F&SC 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.

The math to determine this level of planning simply does not exist in D365 functionality.  It requires math and AI that passes thousands of times through calculations for each part and part center (locations).  Math and AI like this are unique to Smart.  To understand more, please read on. 

Step 1. Ensure that all stakeholders agree on the metrics that matter. 

All participants in the service parts inventory planning process must agree on the definitions and what metrics matter most to the organization. Service Levels detail the percentage of time you can completely satisfy required usage without stocking out. Fill Rates detail the percentage of the requested usage that is immediately filled from stock. (To learn more about the differences between service levels and fill rate, watch this 4-minute lesson here.) Availability details the percentage of active spare parts with an on-hand inventory of at least one unit. Holding costs are the annualized costs of holding stock accounting for obsolescence, taxes, interest, warehousing, and other expenses. Shortage costs are the cost of running out of stock, including vehicle/equipment downtime, expedites, lost sales, and more. Ordering costs are the costs associated with placing and receiving replenishment orders.

Step 2. Benchmark historical and predicted current service level performance.

All participants in the service parts inventory planning process must hold a common understanding of predicted future service levels, fill rates, and costs and their implications for your service parts operations. It is critical to measure both historical Key Performance Indicators (KPIs) and their predictive equivalents, Key Performance Predictions (KPPs).  Leveraging modern software, you can benchmark past performance and leverage probabilistic forecasting methods to simulate future performance.  Virtually every Demand Planning solution stops here.  Smart goes further by stress-testing your current inventory stocking policies against all plausible future demand scenarios.  It is these thousands of calculations that build our KPPs.  The accuracy of this improves D365’s ability to balance the costs of holding too much with the costs of not having enough. You will know ahead of time how current and proposed stocking policies are likely to perform.

Step 3. Agree on targeted service levels for each spare part and take proactive corrective action when targets are predicted to miss. 

Parts planners, supply chain leadership, and the mechanical/maintenance teams should agree on the desired service level targets with a full understanding of the tradeoffs between stockout risk and inventory cost.  A call out here is that our D365 customers are almost always stunned by the stocking levels difference between 100% and 99.5% availability.   With the logic for nearly 10,000 scenarios that half a percent outage is almost never hit.   You achieve full stocking policy with much lower costs.   You find the parts that are understocked and correct those.  The balancing point is often a 7-12% reduction in inventory costs. 

This leveraging of what-if scenarios in our parts planning software gives management and buyers the ability to easily compare alternative stocking policies and identify those that best meet business objectives.  For some parts, a small stock out is okay.  For others, we need that 99.5% parts availability.  Once these limits are agreed upon, we use the Power of D365 to optimize inventory using D365 core ERP as it should be.   The planning is automatically uploaded to engage Dynamics with modified reorder points, safety stock levels, and/or Min/Max parameters.  This supports a single Enterprise center point, and people are not using multiple systems for their daily parts management and purchasing.

Step 4. Make it so and keep it so. 

Empower the planning team with the knowledge and tools it needs to ensure that you strike agreed-upon balance between service levels and costs.  This is critical and important.  Using Dynamics F&SC or BC to execute your ERP transactions is also important.  These two Dynamics ERPs have the highest level of new ERP growth on the planet.  Using them as they are intended to be used makes sense.   Filling the white space for the math and AI calculations for Maintenance and Parts management also makes sense.  This requires a more complex and targeted solution to help.  Smart Software Inventory Optimization for EAM and Dynamics ERPs holds the answer.    

Remember: Recalibration of your service parts inventory policy is preventive maintenance against both stockouts and excess stock.  It helps costs, frees capital for other uses, and supports best practices for your team. 

Extend Microsoft 365 F&SC and AX with Smart IP&O

To see a recording of the Microsoft Dynamics Communities Webinar showcasing Smart IP&O, register here:

https://smartcorp.com/inventory-planning-with-microsoft-365-fsc-and-ax/

“Regardless of how much effort is poured into training forecasters and developing elaborate forecast support systems, decision-makers will either modify or discard the predictions if they do not trust them.”  — Dilek Onkal, International Journal of Forecasting 38:3 (July-September 2022), p.802.

The words quoted above grabbed my attention and prompted this post. Those of a geekly persuasion, like your blogger, are inclined to think of forecasting as a statistical problem. While that is obviously true, those of a certain age, like your blogger, understand that forecasting is also a social activity and therefore has a large human component.

Who Do You Trust?

Trust is always a two-way street, but let’s stay on the demand forecaster’s side. What characteristics of and actions by forecasters and demand planners build trust in their work? The above quoted Professor Onkal reviewed academic research on this topic going back to 2006. She summarized results from practitioner surveys that identified key trust factors related to forecaster characteristics, forecasting process, and forecasting communication.

Forecaster characteristics

Key to building trust among the users of forecasts are perceptions of forecaster and demand planner competence and objectivity. Competence has a mathematical component, but many managers confuse computer skills with analytic skills, so users of forecasting software can usually clear this hurdle. However, since the two are not the same, it pays dividends to absorb your vendor’s training and learn not just the math but the lingo of your forecasting software. In my observation, trust can also be increased by showing knowledge of the company’s business.

Objectivity is also a key to trustworthiness. It may be uncomfortable for the forecaster to be put in the middle of occasional departmental squabbles, but those will come up and must be handled with tact. Squabbles? Well, silos exist and tilt in different directions. Sales departments favor higher demand forecasts that drive production increases, so that they never have to say “Sorry, we are fresh out of that.” Inventory managers are wary of high demand forecasts, because “excess enthusiasm” can leave them holding the bag, sitting on bloated inventory.

Sometimes the forecaster becomes a de facto referee, and in this role must display overt signs of objectivity. That can mean first recognizing that every management decision involves tradeoffs of good things against other good things, e.g., product availability versus lean operations, and then helping the parties strike a painful but tolerable balance by surfacing the links between operational decisions and the key performance metrics that matter to folks like Chief Financial Officers.

The Forecasting process

The forecasting process can be thought of as having three phases: data inputs, calculations, and outputs. Actions can be taken to increase trust in each phase.

Regarding inputs:

Trust can be increased if obviously relevant inputs are at least acknowledged if not directly used in calculations. Thus, factors like social media sentiment and regional sales managers’ gut instincts can be legitimate parts of a forecast consensus process. However, objectivity requires that these putative predictors of profit be tested objectively. For instance, a professional-grade forecasting process may well include subjective adjustment to statistical forecasts but must then also assess whether the adjustments actually end up improving accuracy, not just making some people feel listened to.

Regarding the second phase, calculations:

The forecaster will be trusted to the extent that they are able to deploy more than one way to calculate forecasts and then articulate a good reason why they chose the method eventually used. In addition, the forecaster should be able to explain in accessible language how even complicated techniques do their job. It is difficult to put trust in a “black box” method that is so opaque as to be inscrutable. The importance of explainability is amplified by the fact of life that the forecaster’s superior must themselves in turn be able to justify the choice of technique to their supervisor.

For instance, exponential smoothing uses this equation: S(t) = αX(t)+(1-α)S(t-1). Many forecasters are familiar with this equation, but many forecast users are not. There is a story that explains the equation in terms of averaging irrelevant “noise” in an item’s demand history and the need to strike a balance between smoothing out noise and being able to react to sudden shifts in the level of demand. The forecaster who can tell that story will be more credible. (My own version of that story uses phrases from sports, i.e., “head fakes” and “jukes”. Finding folksy analogs appropriate to your specific audience always pays dividends.)

A final point: best practice demands that any forecast be accompanied by an honest assessment of its uncertainty. A forecaster who tries to build trust by being overly specific (“Sales next quarter will be 12,184 units”) will always fail. A forecaster who says “Sales next quarter will have a 90% chance of falling between 12,000 and 12,300 units” will be both correct more often and  also more helpful to decision makers. After all, forecasting is essentially a job of risk management, so the decision maker is best served by knowing the risks.

Forecasting communication:

Finally, consider the third phase, communication of forecast results. Research suggests that continual communication with forecast users builds trust. It avoids those horrible, deflating moments when a nicely formatted report is shot down because of some fatal flaw that could have been foreseen: “This is no good because you didn’t take account of X, Y or Z” or “We really wanted you to present results rolled up to the top of the product hierarchies (or by sales region or by product line or…)”.

Even when everybody is aligned as to what is expected, trust is enhanced by presenting results using well-crafted graphics, with massive numerical tables provided for backup but not as the main way of communicating results. My experience has been that, just as a meeting-control device, a graph is usually much better than a large numerical table. With a graph, everybody’s attention is focused on the same thing and many aspects of the analysis are immediately (and literally) visible. With a table of results, the table of participants often splinters into side conversations in which each voice is focused on different pieces of the table.

Onkal summarizes the research this way: “Take-aways for those who make forecasts and those who use them converge around clarity of communication as well as perceptions of competence and integrity.”

What Do You Trust?

There is a related dimension of trust: not who do you trust but what do you trust? By this I mean both data and software….  Read the 2nd part of this Blog “What do you Trust” here  https://smartcorp.com/forecasting/the-role-of-trust-in-the-demand-forecasting-process-part-2-what/

How long should it take for a demand forecast to be computed using statistical methods?  This question is often asked by customers and prospects.  The answer truly depends.  Forecast results for a single item can be computed in the blink of an eye, in as little as a few hundredths of a second, but sometimes they may require as much as five seconds.  To understand the differences, it’s important to understand that there is more involved than grinding through the forecast arithmetic itself.   Here are six factors that influence the speed of your forecast engine.

1) Forecasting method.  Traditional time-series extrapolative techniques (such as exponential smoothing and moving average methods), when cleverly coded, are lighting fast.  For example, the Smart Forecast automatic forecasting engine that leverages these techniques and powers our demand planning and inventory optimization software can crank out statistical forecasts on 1,000 items in 1 second!  Extrapolative methods produce an expected forecast and a summary measure of forecast uncertainty. However, more complex models in our platform that generate probabilistic demand scenarios take much longer given the same computing resources.  This is partly because they create a much larger volume of output, usually thousands of plausible future demand sequences. More time, yes, but not time wasted, since these results are much more complete and form the basis for downstream optimization of inventory control parameters.

2) Computing resources.  The more resources you throw at the computation, the faster it will be.  However, resources cost money and it may not be economical to invest in these resources.  For example, to make certain types of machine learning-based forecasts work, the system will need to multi-thread computations across multiple servers to deliver results quickly.  So, make sure you understand the assumed compute resources and associated costs. Our computations happen on the Amazon Web Services cloud, so it is possible to pay for a great deal of parallel computation if desired.

3) Number of time-series.  Do you have to forecast only a few hundred items in a single location or many thousands of items across dozens of locations?  The greater the number of SKU x Location combinations, the greater the time required.  However, it is possible to trim the time to get demand forecasts by better demand classification.  For example, it is not important to forecast every single SKU x Location combination. Modern Demand Planning Software can first subset the data based on volume/frequency classifications before running the forecast engine.  We’ve observed situations where over one million SKU x Location combinations existed, but only ten percent had demand in the preceding twelve months.

4) Historical Bucketing.  Are you forecasting using daily, weekly, or monthly time buckets?  The more granular the bucketing, the more time it is going to take to compute statistical forecasts.  Many companies will wonder, “Why would anyone want to forecast on a daily basis?” However, state-of-the-art demand forecasting software can leverage daily data to detect simultaneous day-of-week and week-of-month patterns that would otherwise be obscured with traditional monthly demand buckets. And the speed of business continues to accelerate, threatening the competitive viability of the traditional monthly planning tempo.

5) Amount of History.  Are you limiting the model by only feeding it the most recent demand history, or are you feeding all available history to the demand forecasting software? The more history you feed the model, the more data must be analyzed and the longer it is going to take.

6) Additional analytical processing.  So far, we’ve imagined feeding items’ demand history in and getting forecasts out. But the process can also involve additional analytical steps that can improve results. Examples include:

a) Outlier detection and removal to minimize the distortion caused by one-off events like storm damage.

b) Machine learning that decides how much history should be used for each item by detecting regime change.

c) Causal modeling that identifies how changes in demand drivers (such as price, interest rate, customer sentiment, etc.) impact future demand.

d) Exception reporting that uses data analytics to identify unusual situations that merit further management review.

The Rest of the Story. It’s also critical to understand that the time to get an answer involves more than the speed of forecasting computations per se.  Data must be loaded into memory before computing can begin. Once the forecasts are computed, your browser must load the results so that they may be rendered on screen for you to interact with.  If you re-forecast a product, you may choose to save the results.  If you are working with product hierarchies (aggregating item forecasts up to product families, families up to product lines, etc.), the new forecast is going to impact the hierarchy, and everything must be reconciled.   All of this takes time.

Fast Enough for You? When you are evaluating software to see whether your need for speed will be satisfied, all of this can be tested as part of a proof of concept or trial offered by demand planning software solution providers.  Test it out, and make sure that the compute, load, and save times are acceptable given the volume of data and forecasting methods you want to use to support your process.

 What is the wiggle effect? 

It’s when your statistical forecast incorrectly predicts the ups and downs observed in your demand history when there really isn’t a pattern.  It’s important to make sure your forecasts don’t wiggle unless there is a real pattern.

Here is a transcript from a recent customer where this issue was discussed:

Customer: “The forecast isn’t picking up on the patterns I see in the history.  Why not?” 

Smart:  “If you look closely, the ups and downs you see aren’t patterns.  It’s really noise.”  

Customer:  “But if we don’t predict the highs, we’ll stock out.”

Smart: “If the forecast were to ‘wiggle’ it would be much less accurate.  The system will forecast whatever pattern is evident, in this case a very slight uptrend.  We’ll buffer against the noise with safety stocks. The wiggles are used to set the safety stocks.”

Customer: “Ok. Makes sense now.” 

The wiggle looks reassuring but, in this case, it is resulting in an incorrect demand forecast. The ups and downs aren’t really occurring at the same times each month.  A better statistical forecast is shown in light green.