There’s one thing I’m reminded of almost every day at Smart Software that puzzle me: most companies do not understand how forecasts are created, and stocking policies are determined.  It’s an organizational black box. Here is an example from a recent sales call:

How do you forecast?
We use history.

How do you use history?
What do you mean?

Well, you can take an average of the last year, last two years, average the most recent periods, or use some other type of formula to generate the forecast.
I’m pretty sure we use an average of the last 12 months.

Why 12 months instead of a different amount of history?
12 months is a good amount of time to use because it doesn’t get skewed by older data but it’s recent enough

How do you know it’s more accurate than using 18 months or some other length of history?
We don’t know. We do adjust the forecasts based on feedback from sales.  

Do you know if the adjustments make things more accurate or less than if you just used the average?
We don’t know but are confident that forecasts are inflated

What do the inventory buyers do then if they think the numbers are inflated?
They have lots of business knowledge and adjust their buys accordingly

So, is it fair to say they would ignore the forecasts at least some of the time?
Yes, some of the time.

How do the buyers decide when to order more? Do you have a reorder point or safety stock specified in your ERP system that helps guide these decisions?
Yes, we use a safety stock field.

How is safety stock calculated?
Buyers determine this based on the importance of the item, lead times, and other considerations such as how many customers purchase the item, the velocity of the item, it’s cost.  They’ll carry different amounts of safety stock depending on this.

The discussion continued. The main takeaway here is that when you scratch just below the surface, far more questions are revealed than answers.  This often means that the inventory planning and demand forecast process is highly subjective, varies from planner to planner, is not well understood by the rest of the organization, and likely to be reactive.  As Tom Willemain has described it’s “chaos masked by improvisation.”   The “as-is” process needs to be fully identified and documented.  Only then can gaps be exposed, and improvements can be made.   Here is a list of 10 questions  you can ask that will reveal your organization’s true forecasting, demand planning, and inventory planning process.

“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.

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.

Trust in Data

Trust in data underpins trust in the forecaster using the data. Most of our customers have their data in an ERP system. This data must be understood as a key corporate asset. For the data to be trustworthy, it must have the “three C’s”, i.e., it must be correct, complete, and current.

Correctness is obviously fundamental. We once had a customer who was implementing a new, strong forecasting process, but found the results completely at odds with their sense of what was happening in the business. It turned out that several of their data streams were incorrect by a factor of two, which is a huge error. Of course, this set back the implementation process until they could identify and correct all the gross errors in their demand data.

There is a less obvious point to be made about correctness. That is, data are random, so what you see now is not likely to be what you see next. Planning production based on the assumption that next week’s demand will be exactly the same as this week’s demand is clearly foolish, but classical formula-based forecasting models like the exponential smoothing mentioned above will project the same number throughout the forecast horizon. This is where scenario-based planning is essential for coping with the inevitable fluctuations in key variables such as customers’ demands and suppliers’ replenishment lead times.

Completeness is the second requirement for data to be trusted. Our software ultimately gets much of its value from exposing the links between operational decisions (e.g., selecting the reorder points governing replenishment of stock) and business-related metrics like inventory costs. Yet often implementation of forecasting software is delayed because item demand information is available someplace, but holding, ordering and/or shortage costs are not.  Or, to cite another recent example, a customer was able to properly size only half their inventory of spares for reparable parts because nobody had been tracking when the other half was breaking down, meaning there was no information on mean time before failure (MTBF), meaning it was not possible to model the breakdown behavior of half the fleet of reparable spares.

Finally, the currency of data matters. As the speed of business increases and company planning cycles drop from a quarterly or monthly tempo to a weekly or daily tempo, it becomes desirable to exploit the agility provided by overnight uploads of daily transactional data into the cloud. This allows high-frequency adjustments of forecasts and/or inventory control parameters for items that experience high volatility and sudden shifts in demand. The fresher the data, the more trustworthy the analysis.

Trust in Demand Forecasting Software

Even with high-quality data, forecasters must still trust the analytical software that processes the data. This trust must extend to both the software itself and to the computational environment in which it functions.

If forecasters used on-premises software, they must rely on their own IT departments to safeguard the data and keep it available for use. If they wish instead to exploit the power of cloud-based analytics, customers must trust their confidential information to their software vendors. Professional-level software, such as ours, justifies customers’ trust through SOC 2 certification. SOC 2 certification was developed by the American Institute of CPAs and defines criteria for managing customer data based on five “trust service principles”—security, availability, processing integrity, confidentiality, and privacy.

What about the software itself? What is needed to make it trustworthy? The main criteria here are the correctness of algorithms and functional reliability. If the vendor has a professional program development process, there will be little chance that the software ends up computing the wrong numbers because of a programming error. And if the vendor has a rigorous quality assurance process, there will be little chance that the software will crash just when the forecaster is on deadline or must deal with a pop-up analysis for a special situation.

Summary

To be useful, forecasters and their forecasts must be trusted by decision-makers. That trust depends on characteristics of forecasters and their processes and communication. It also depends on the quality of the data and software used in creating the forecasts.

Read the 1st part of this Blog “Who do you Trust” here: https://smartcorp.com/forecasting/the-role-of-trust-in-the-demand-forecasting-process-part-1-who/

“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/

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

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 that have 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 down time, 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. By stress testing your current inventory stocking policies against all plausible scenarios of future demand, 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. By leveraging what-if scenarios in modern parts planning software, it is possible to compare alternative stocking policies and identify those that best meets business objectives. Agree on what degree of stockout risk is acceptable for each part or class of parts. Likewise, determine inventory budgets and other cost constraints. Once these limits are agreed, take immediate action to avoid stockouts and excess inventory before they occur. Use your software to automatically upload modified reorder points, safety stock levels, and/or Min/Max parameters to your Enterprise Resource Planning (ERP) or Enterprise Asset Management (EAM) system to adjust daily parts 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 by driving your ordering process using optimized inputs (forecasts, reorder points, order quantities, safety stocks). Track your KPI’s and use your software to identify and address exceptions. Don’t let reorder points grow stale and outdated.  Recalibrate the stocking policies each planning cycle (at least once monthly) using up-to-date usage history, supplier lead times, and costs. Remember: Recalibration of your service parts inventory policy is preventive maintenance against both stockouts and excess stock.

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5 Steps to Consensus Demand Planning

The whole point of demand forecasting is to establish the best possible view of future demand.  This requires that we draw upon the best data and inputs we can get, leverage statistics to capture underlying patterns, put our heads together to apply overrides based on business knowledge, and agree on a consensus demand plan that serves as cornerstone to the company’s overall demand plan.

Step 1: Develop an accurate demand signal.   What constitutes demand?  Consider how  your organization defines demand – say, confirmed sales orders net of cancellations or shipment data adjusted to remove the impact of historical stockouts  – and use this consistently.  This is your measure of what the market is requesting you to deliver.  Don’t confuse this with your ability to deliver – that should be reflected in the revenue plan.

Step 2: Generate a statistical forecast.  Plan for thousands of items, using a proven forecasting application that automatically pulls in your data and reliably produces accurate forecasts for all of your items.  Review the first pass of your forecast, then make adjustments.  A strike or train wreck may have interrupted shipping last month – don’t let that wag your forecast.  Adjust for these and reforecast.  Do the best you can, then invite others to weigh in.

Step 3: Bring on the experts.  Product line managers, sales leaders, key distribution partners know their markets.  Share your forecast with them.  Smart uses the concept of a “Snapshot” to share a facsimile of your forecast – at any level, for any product line – with people who may know better.  There could be an enormous order that hasn’t hit the pipeline, or a channel partner is about to run their annual promotion.  Give them an easy way to take their portion of the forecast and change it.  Drag this month up, that one down …

Step 4:  Measure Accuracy and Forecast Value Add.  Some of your contributors may be right on the money, other tend to be biased high or low.  Use forecast vs. actuals reporting and measure forecast value add analysis to measure forecast errors and whether changes to the forecast are hurting or helping.  By informing the process with this information, your company will improve it’s ability to forecast more accurately.

Step 5: Agree on the Consensus Forecast.  You can do this one product line or geography at a time, or business by  business.  Convene the team, graphically stack up their inputs, review past accuracy performance, discuss their reasons for increasing or reducing the forecast, and agree on whose inputs to use.  This becomes your consensus plan.  Finalize the plan and send it off – upload forecasts to MRP, send to finance and manufacturing.  You have just kicked off your Sales, Inventory and Operational Planning process.

You can do this.  And we can help.  If you have any questions about collaborative demand planning please reply to this blog, we will follow up.

In Smart Software’s forty-plus years of providing forecasting software, we’ve met many people who find themselves, perhaps surprisingly, becoming demand forecasters. This blog is aimed primarily at those fortunate individuals who are about to start this adventure (though seasoned pros may enjoy the refresher).

Welcome to the field! Good forecasting can make a big difference to your company’s performance, whether you are forecasting to support sales, marketing, production, inventory, or finance.

There is a lot of math and statistics underlying demand forecasting methods, so your assignment suggests that you are not one of those math-phobic people who would rather be poets. Luckily, if you are feeling a bit shaky and not yet healed from your high school geometry class, a lot of the math is built into forecasting software, so your first job is to leave the math for later while you get a view of the big picture. It is indeed a big picture, but let’s isolate few of the ideas that will most help you succeed.

1. Demand Forecasting is a team sport. Even in a small company, the demand planner is part of a team, with some folks bringing the data, some bringing the tech, and some bringing the business judgment. In a well-run business, your job will never be to simply feed some data into a program and send out a forecast report. Many companies have adopted a process called Sales and Operations Planning (S&OP) in which your forecast will be used to kick off a meeting to make certain judgments (e.g., Should we assume this trend will continue? Will it be worse to under-forecast or over-forecast?) and to blend extra information into the final forecast (e.g., sales force input, business intelligence on competitors’ moves, promotions). The implication for you is that your skills at listening and communicating will be important to your success.

2. Statistical Forecasting engines need good fuel. Historical data is the fuel used by statistical forecasting programs, so bad or missing or delayed data can degrade your work product. Your job will implicitly include a quality control aspect, and you must keep a keen eye on the data that are supplied to you. Along the way, it is a good idea to make the IT people your friends.

3. Your name is on your forecasts. Like it or not, if I send forecasts up the chain of command, they get labeled as “Tom’s forecasts.” I must be prepared to own those numbers. To earn my seat at the table, I must be able to explain what data my forecasts were based on, how they were calculated, why I used Method A instead of Method B to do the calculations, and especially how firm or squishy they are. Here honesty is important. No forecast can reasonably be expected to be perfectly accurate, but not all managers can be expected to be perfectly reasonable. If you’re unlucky, your management will think that your reports of forecast uncertainty suggest either ignorance or incompetence. In truth, they indicate professionalism. I have no useful advice about how best to manage such managers, but I can warn you about them. It’s up to you to educate those who use your forecasts. The best managers will appreciate that.

4. Leave your spreadsheets behind. It’s not uncommon for someone to be promoted to forecaster because they were great with Excel. Unless you are with an unusually small company, the scale of modern corporate forecasting overwhelms what you can handle with spreadsheets. The increasing speed of business compounds the problem: the sleepy tempo of annual and quarterly planning meetings is rapidly giving way to weekly or even daily re-forecasts as conditions change. So, be prepared to lean on a professional vendor of modern, scalable cloud-based demand planning and statistical forecasting software for training and support.

5. Think visually. It will be very useful, both in deciding how to generate demand forecasts and in presenting them to management, so take advantage of the visualization capabilities built into forecasting software. As I noted above, in today’s high-frequency business world, the data you work with can change rapidly, so what you did last month may not be the right thing to do this month. Literally keep an eye on your data by making simple plots, like “timeplots” that show things like trend or seasonality or (especially) changes in trend or seasonality or anomalies that must be dealt with. Similarly, supplementing tables of forecasts with graphs comparing current forecasts to prior forecasts to actuals can be very helpful in an S&OP process. For example, timeplots showing past values, forecasted values, and “forecast intervals” indicating the objective uncertainty in the forecasts provide a solid basis for your team to fully appreciate the message in your forecasts.

That’s enough for now. As a person who’s taught in universities for half a century, I’m inclined to start into the statistical side of forecasting, but I’ll save that for another time. The five tips above should be helpful to you as you grow into a key part of your corporate planning team. Welcome to the game!