For some of our customers, weather has a significant influence on demand. Extreme short-term weather events like fires, droughts, hot spells, and so forth can have a significant near-term influence on demand.

There are two ways to factor weather into a demand forecast: indirectly and directly. The indirect route is easier using the scenario-based approach of Smart Demand Planner. The direct approach requires a tailored special project requiring additional data and hand-crafted modeling.

Indirect Accounting for Weather

The standard model built into Smart Demand Planner (SDP) accommodates weather effects in four ways:

1. If the world is steadily getting warmer/colder/drier/wetter in ways that impact your sales, SDP detects these trends automatically and incorporates them into the demand scenarios it generates.
2. If your business has a regular rhythm in which certain days of the week or certain months of the year have consistently higher or lower-than-average demand, SDP also automatically detects this seasonality and incorporates it into its demand scenarios.
3. Often it is the cussed randomness of weather that interferes with forecast accuracy. We often refer to this effect as “noise”. Noise is a catch-all term that incorporates all kinds of random trouble. Besides weather, a geopolitical flareup, the surprise failure of a regional bank, or a ship getting stuck in the Suez Canal can and have added surprises to product demand. SDP assesses the volatility of demand and reproduces it in its demand scenarios.
4. Management overrides. Most of the time, customers let SDP churn away to automatically generate tens of thousands of demand scenarios. But if users feel the need to touch up specific forecasts using their insider knowledge, SDP can make that happen through management overrides.

Direct Accounting for Weather

Sometimes a user will be able to articulate subject matter expertise linking factors outside their company (such as interest rates or raw materials costs or technology trends) to their own aggregate sales. In these situations, Smart Software can arrange for one-off special projects that provide alternative (“causal”) models to supplement our standard statistical forecasting models. Contact your Smart Software representative to discuss a possible causal modeling project.

Meanwhile, don’t forget your umbrella.

In almost every business and industry, decision-makers need reliable forecasts of critical variables, such as sales, revenues, product demand, inventory levels, market share, expenses, and industry trends.

Many kinds of people make these forecasts. Some are sophisticated technical analysts, such as business economists and statisticians. Many others regard forecasting as an important part of their overall work: general managers, production planners, inventory control specialists, financial analysts, strategic planners, market researchers, and product and sales managers. Still, others seldom think of themselves as forecasters but often have to make forecasts on an intuitive, judgmental basis.

Because of the way we designed Smart Demand Planner, it has something to offer all types of forecasters. This design grows out of several observations about the forecasting process. Because we designed Smart Demand Planner with these observations in mind, we believe it has a style and content uniquely suited for turning your browser into an effective forecasting and planning tool:

Forecasting is an art that requires a mix of professional judgment and objective, statistical analysis.

It is often effective to begin with an objective statistical forecast that automatically accounts for trends, seasonality, and other patterns.  Then, apply adjustments or forecast overrides based on your business judgment. Smart Demand Planner makes it easy to execute graphical and tabular adjustments to statistical forecasts.

The forecasting process is usually iterative.

You will likely decide to make several refinements of your initial forecast before you are satisfied. You may want to exclude older historical data that you find to no longer be relevant.  You could apply different weights to the forecast model that put varying emphases on the most recent data. You could apply trend dampening to increase or decrease aggressively trending statistical forecasts.  You could allow the Machine Learning models to fine-tune the forecast selection for you and select the winning model automatically.  Smart Demand Planner’s processing speed gives you plenty of time to make several passes and saves multiple versions of the forecasts as “snapshots” so you can compare forecast accuracy later.

Forecasting requires graphical support.

The patterns evident in data can be seen by a discerning eye. The credibility of your forecasts will often depend heavily on graphical comparisons other business stakeholders make when they assess the historical data and forecasts. Smart Demand Planner provides graphical displays of forecasts, history, and forecast vs. actuals reporting.

Forecasts are never exactly correct.

Because some error always creeps into even the best forecasting process, one of the most useful supplements to a forecast is an honest estimate of its margin of error.

Smart Demand Planner presents both graphical and tabular summaries of forecast accuracy based on the acid test of predicting data held back from development of the forecasting model. 

Forecast intervals or confidence intervals are also very useful.  They detail the likely range of possible demand that is expected to occur.  For example, if actual demand falls outside of the 90% confidence interval more than 10% of the time then there is reason to investigate further.  

Forecasting requires a match of method to data.

One of the major technical tasks in forecasting is to match the choice of forecasting technique to the nature of the data. Features of a data series like trend, seasonality or abrupt shifts in level suggest certain techniques instead of others.

Smart Demand Planner’ Automatic forecasting feature makes this match quickly, accurately and automatically.

Forecasting is often a part of a larger process of planning or control.

For example, forecasting can be a powerful complement to spreadsheet-based financial analysis, extending rows of figures off into the future. In addition, accurate sales and product demand forecasts are fundamental inputs to a manufacturer’s production planning and inventory control processes. An objective statistical forecast of future sales will always help identify when the budget (or sales plan) may be too unrealistic. Gap analysis enables the business to take corrective action to their demand and marketing plans to ensure they do not miss the budgeted plan.

Forecasts need to be integrated into ERP systems
Smart Demand Planner can quickly and easily transfer its results to other applications, such as spreadsheets, databases and planning systems including ERP applications.  Users are able to export forecasts in a variety of file formats either via download or to secure FTP file locations.  Smart Demand Planner includes API based integrations to a variety of ERP and EAM systems including Epicor Kinetic and Epicor Prophet 21, Sage X3 and Sage 300, Oracle NetSuite, and each of Microsoft’s Dynamics 365 ERP systems. API based integrations enable customers to push forecast results directly back to the ERP system on demand.

The result is more efficient sales planning, budgeting, production scheduling, ordering, and inventory planning.

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.

​When you should use traditional extrapolative forecasting techniques.

With so much hype around new Machine Learning (ML) and probabilistic forecasting methods, the traditional “extrapolative” or “time series” statistical forecasting methods seem to be getting the cold shoulder.  However, it is worth remembering that these traditional techniques (such as single and double exponential smoothing, linear and simple moving averaging, and Winters models for seasonal items) often work quite well for higher volume data. Every method is good for what it was designed to do.  Just apply each appropriately, as in don’t bring a knife to a gunfight and don’t use a jackhammer when a simple hand hammer will do. 

Extrapolative methods perform well when demand has high volume and is not too granular (i.e., demand is bucketed monthly or quarterly). They are also very fast and do not use as many computing resources as probabilistic and ML methods. This makes them very accessible.

Are the traditional methods as accurate as newer forecasting methods?  Smart has found that extrapolative methods do very poorly when demand is intermittent. However, when demand is higher volume, they only do slightly worse than our new probabilistic methods when demand is bucketed monthly.  Given their accessibility, speed, and the fact you are going to apply forecast overrides based on business knowledge, the baseline accuracy difference here will not be material.

The advantage of more advanced models like Smart’s GEN2 probabilistic methods is when you need to predict patterns using more granular buckets like daily (or even weekly) data.  This is because probabilistic models can simulate day of the week, week of the month, and month of the year patterns that are going to be lost with simpler techniques.  Have you ever tried to predict daily seasonality with a Winter’s model? Here is a hint: It’s not going to work and requires lots of engineering.

Probabilistic methods also provide value beyond the baseline forecast because they generate scenarios to use in stress-testing inventory control models. This makes them more appropriate for assessing, say, how a change in reorder point will impact stockout probabilities, fill rates, and other KPIs. By simulating thousands of possible demands over many lead times (which are themselves presented in scenario form), you’ll have a much better idea of how your current and proposed stocking policies will perform. You can make better decisions on where to make targeted stock increases and decreases.

So, don’t throw out the old for the new just yet. Just know when you need a hammer and when you need a jackhammer.