Scenario-based Forecasting vs. Equations

Why Scenario-based planning helps planners better manage risk and create better outcomes.

If you are reading this, you are probably a supply chain professional with responsibilities for demand forecasting, inventory management or both. If you live in the 21st century, you use software of some kind to help you do your job. But what, fundamentally, does your software do for you?

Traditionally, software has served as a delivery vehicle for equations. Even if you decided early on in life that you and equations don’t get along, they can still do something for you, and you can live with them—provided some software keeps all that math at a safe distance away.

This is fine, as far as it goes. But we at Smart Software think you would do better by trading in your equations for scenarios. Most often, the point of an equation is to give “the answer”, typically in the form of a number, as in “next month’s demand for SKUxxx will be 105 units.” Results like these are helpful, but incomplete.

Forecasting can be thought of as a computing problem, but it is more helpful to think of it as an exercise in risk management. The equation’s forecast of 105 units does not include any indication of the uncertainty in the forecast, though there is always some. It does not help you think about plausible contingencies: what if demand is for more than 105 units? What if it’s for fewer than 105? Could it get as high as 130 or as low as 80? Is 80 even remotely likely?

This is where scenario-based analysis shows its advantage. One definition of “scenario” is “a postulated sequence of events.” Our definition is more extensive: a scenario is “a postulated sequence of events and their associated probabilities of happening.” Scenarios are the ultimate what-if planning tool. Forecasting by equation will predict a demand for 105 units. Scenario forecasting produces a bundle of possible demand figures, some more likely and others less so. If there are few or no scenarios as low as 80, you can let that contingency go.

Plus-or-Minus How Much?

Those who are better versed in equation-based forecasting might protest that equation-based software sometimes provides indications of the “plus or minus” of a forecast, complete with a bell-shaped curve indicating the relative likelihood of various contingencies. However, when you see a perfect bell-shaped distribution, you know you are being asked to rely on a theoretical assumption that is only sometimes valid.

Scenario forecasts do not rely on that assumption.  In fact, they need not rely on any pre-conceived mathematical assumption whose main selling point is that it simplifies analysis. You don’t need a simplified analysis–you need a realistic analysis based on facts.

Cutting-edge software produces scenario forecasts, not just for demand planning but also for inventory management. Demand is a key input to inventory software, along with supplier behavior as reflected in replenishment lead times. Both demand and supply need to be forecasted if you want to see the consequences of, for instance, choosing a reorder point of 15 and an order quantity of 25.

Inventory systems are what is called “path sensitive”, meaning that any particular sequence of demand values will yield different performance than the same demand values in a different order. For example, if all your highest demand periods come bunched up, one after another, you’ll have much more difficulty keeping stocked than if the same high demand periods are spaced apart with time to restock in between. Scenarios reflect these differences in sufficient detail to yield average performance metrics reflective of the various contingencies inherent in uncertain demand.

Figure 1 illustrates the difference between an equation-based forecast and forecast scenarios.  The green cells hold 10 months of demand for a spare part. The blue cells hold an equation-based forecast that calls for average demand of 1.5 units in months 11, 12 and 13. The pistachio-colored cells hold eight scenario forecasts, though in practice our software would generate tens of thousands of scenarios. Now, the scenarios also average out to 1.5 units per month, but they go further and display the wide variety of ways that the next three months could play out. For instance, reading vertically, the monthly demand could range from 0 to 3. Reading horizontally, the three-month totals could range from 0 to 6, compared to the equation-based estimate of 4.5. Continuing with this toy example, if you have 5 units on hand and the replenishment lead time is greater than 3 months, the equation-based model says you will be ok over the next 3 months, but the scenario-based results say you have 1 chance in 8 (12.5%) chance of stocking out. Equivalently, you have an 87.5% service level. If the part is critical and you are aiming for a 95% service level, you are at risk of missing your item availability goal.

Scenario based Forecasting vs Equations hd2

Figure 1: Comparing equation-based and scenario-based forecasts

 

Summary

Remember, equation-based forecasting gives you information, but shallow information. Scenario-based forecasting can tell you not just what result is most likely but also how reliable any of a variety of predictions are—and this allows you to bring your judgment to bear on balancing risk and stocking expenses—all automated to scale to a vast catalog of items.

 

Top Five Tips for New Demand Planners and Forecasters

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.

 

  1. 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.

 

  1. 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.

 

  1. 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.

 

  1. 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!

 

 

 

Managing Inventory amid Regime Change

​If you hear the phrase “regime change” on the news, you immediately think of some fraught geopolitical event. Statisticians use the phrase differently, in a way that has high relevance for demand planning and inventory optimization. This blog is about “regime change” in the statistical sense, meaning a major change in the character of the demand for an inventory item.

An item’s demand history is the fuel that powers demand planners’ forecasting machines. In general, the more fuel the better, giving us a better fix on the average level, the volatility, the size and frequency of any spikes, the shape of any seasonality pattern, and the size and direction of any trend.

But there is one big exception to the rule that “more data is better data.” If there is a major shift in your world and new demand doesn’t look like old demand, then old data become dangerous.

Modern software can make accurate forecasts of item demand and suggest wise choices for inventory parameters like reorder points and order quantities. But the validity of these calculations depends on the relevance of the data used in their calculation. Old data from an old regime no longer reflect current reality, so including them in calculations creates forecast error for demand planners and either excess stock or unacceptable stockout rates for inventory planners.

That said, if you were to endure a recent regime change and throw out the obsolete data, you would have a lot less data to work with. This has its own costs, because all the estimates computed from the data would have greater statistical uncertainty even though they would be less biased. In this case, your calculations would have to rely more heavily on a blend of statistical analysis and your own expert judgement.

At this point, you may ask “How can I know if and when there has been a regime change?” If you’ve been on the job for a while and are comfortable looking at timeplots of item demand, you will generally recognize regime change when you see it, at least if it’s not too subtle. Figure 1 shows some real-world examples that are obvious.

Figure 1 Four examples of regime change in real-world item demand

Figure 1: Four examples of regime change in real-world item demand

 

Unfortunately, less obvious changes can still have significant effects. Moreover, most of our customers are too busy to manually review all the items they manage even once per quarter. When you get beyond, say, 100 items, the task of eyeballing all those time series becomes onerous. Fortunately, software can do a good job of continuously monitoring demand for tens of thousands of items and alerting you to any items that may need your attention. Then too, you can arrange for the software to not only detect regime change but also automatically exclude from its calculations all data collected before the most recent regime change, if any. In other words, you can get both automatic warning of regime change and automatic protection from regime change.

For more on the basics of regime change, see our previous blog on the topic: https://smartcorp.com/blog/demandplanningregimechange/  

 

An Example with Numbers in It

If you would like to learn more, read on to see a numerical example of how much regime change can alter the calculation of a reorder point for a critical spare part. Here is a scenario to illustrate the point.

Scenario

  • Goal: calculate the reorder point needed to control the risk of stockout while waiting for replenishment. Assume the target stockout risk is 5%.
  • Assume the item has intermittent daily demand, with many days of zero demand.
  • Assume daily demand has a Poisson distribution with an average of 1.0 units per day.
  • Assume the replenishment lead time is always 30 days.
  • The lead time demand will be random, so it will have a probability distribution and the reorder point will be the 95th percentile of the distribution.
  • Assume the effect of regime change is to either raise or lower the mean daily demand.
  • Assume there is one year of daily data available for estimating the mean daily unit demand.

 

Figure 2 Example of change in mean demand and sample of random daily demand

Figure 2 Example of change in mean demand and sample of random daily demand

 

Figure 2 shows one form of this scenario. The top panel shows that the average daily demand increases from 1.0 to 1.5 after 270 days. The bottom panel shows one way that a year’s worth of daily demand might appear. (At this point, you may be feeling that calculating all this stuff is complicated, even for what turns out to be a simplified scenario. That is why we have software!)

Analysis

Successful calculation of the proper reorder point will depend on when regime change happens and how big a change occurs. We simulated regime changes of various sizes at various times within a 365 day period. Around a base demand of 1.0 units per day, we studied shifts in demand (“shift”) of ±25% and ±50% as well as a no change reference case. We located the time of the change (“t.break”) at 90, 180, and 270 days. In each case, we computed two estimates of the reorder point: The “ideal” value given perfect knowledge of the average demand in the new regime (“ROP.true”), and the estimated value of mean demand computed by ignoring the regime change and using all the demand data for the past year (“ROP.all”).

Table 1 shows the estimates of the reorder point computed over 100 simulations. The center block is the reference case, in which there is no change in the daily demand, which remains fixed at 1 unit per day. The colored block at the bottom is the most extreme increasing scenario, with demand increasing to 1.5 units/day either one-third, one-half, or two-thirds of the way through the year.

We can draw several conclusions from these simulations.

ROP.true: The correct choice for reorder point increases or decreases according to the change in mean demand after the regime change. The relationship is not a simple linear one: the table spans a 600% range of demand levels (0.25 to 1.50) but a 467% range of reorder points (from 12 to 56).

ROP.all: Ignoring the regime change can lead to gross overestimates of the reorder point when demand drops and gross underestimates when demand increases.  As we would expect, the later the regime change, the worse the error. For example, if demand increases from 1.0 to 1.5 units per day two-thirds of the way through the year without being noticed, the calculated reorder point of 43 units would fall 13 units short of where it should be.

A word of caution: Table 1 shows that basing the calculations of reorder points using only data from after a regime change will usually get the right answer. What it doesn’t show is that the estimates can be unstable if there is very little demand history after the change. Therefore, in practice, you should wait to react to the regime change until a decent number of observations have accumulated in the new regime. This might mean using all the demand history, both pre- and post-change, until, say, 60 or 90 days of history have accumulated before ignoring pre-change data.

 

Table 1 Correct and Estimated Reorder Points for different regime change scenarios

Table 1 Correct and Estimated Reorder Points for different regime change scenarios

Blanket Orders

Customer as Teacher

Our customers are great teachers who have always helped us bridge the gap between textbook theory and practical application. A prime example happened over twenty years ago, when we were introduced to the phenomenon of intermittent demand, which is common among spare parts but rare among the finished goods managed by our original customers working in sales and marketing. This revelation soon led to our preeminent position as vendors of software for managing inventories of spare parts. Our latest bit of schooling concerns “blanket orders.”

Expanding the Inventory Theory Textbook

Textbook inventory theory focuses on the three most used replenishment policies: (1) Periodic review order-up-to policy, designated (T, S) in the books (2) Continuous review policy with fixed order quantity, designated (R, Q) and (3) Continuous review order-up-to policy, designated (s, S) but usually called “Min/Max.” Our customers have pointed out that their actual ordering process often includes frequent use of “blanket orders.” This blog focuses on how to adjust stocking targets when blanket orders are used.

Blanket Orders are Different

Blanket orders are contracts with suppliers for fixed replenishment quantities arriving at fixed intervals. For example, you might agree with your supplier to receive 20 units every 7 days via a blanket order rather than 60 to 90 units every 28 days under the Periodic Review policy. Blanket orders contrast even more with the Continuous Review policies, under which both order schedules and order quantities are random.

In general, it is efficient to build flexibility into the restocking process so that you order only what you need and only order when you need it. By that standard, Min/Max should make the most sense and blanket policies should make the least sense.

The Case for Blanket Policies

However, while efficiency is important, it is never the only consideration. One of our customers, let’s call them Company X, explained the appeal of blanket policies in their circumstances. Company X makes high-performance parts for motorcycles and ATV’s. They turn raw steel into cool things.

But they must deal with the steel. Steel is expensive. Steel is bulky and heavy. Steel is not something conjured overnight on a special-order basis. The inventory manager at Company X does not want to place large but random-sized orders at random times. He does not want to baby-sit a mountain of steel. His suppliers do not want to receive orders for random quantities at random times. And Company X prefers to spread out its payments. The result: Blanket orders.

The Fatal Flaw in Blanket Policies

For Company X, blanket orders are intended to even out replenishment buys and avoid unwieldy buildups of piles of steel before they are ready for use. But the logic behind continuous review inventory policies still applies. Surges in demand, otherwise welcome, will occur and can create stockouts. Likewise, pauses in demand can create excess demand. As time goes on, it becomes clear that a blanket policy has a fatal flaw: only if the blanket orders exactly match the average demand can they avoid runaway inventory in either direction, up or down. In practice, it will be impossible to exactly match average demand. Furthermore, average demand is a moving target and can drift up or down.

Hybrid Blanket Policies to the Rescue

A blanket policy does have advantages, but rigidity is its Achilles heel.  Planners will often improvise by adjusting future orders to handle changes in demand but this doesn’t scale across thousands of items.  To make the replenishment policy robust against randomness in demand, we suggest a hybrid policy that begins with blanket orders but retains flexibility to automatically (not manually) order additional supply on an as-need basis. Supplementing the blanket policy with a Min/Max backup provides for adjustments without manual intervention. This combination will capture some of the advantages of blanket orders while protecting customer service and avoiding runaway inventory.

Designing a hybrid policy requires choice of four control parameters. Two parameters are the fixed size and fixed timing of the blanket policy. Two more are the values of Min and Max. This leaves the inventory manager facing a four-dimensional optimization problem.  Advanced inventory optimization software will make it possible to evaluate choices for the values of the four parameters and to support negotiations with suppliers when crafting blanket orders.

 

 

Coping with Surging Demand During the Rebound

The Smart Forecaster

 Pursuing best practices in demand planning,

forecasting and inventory optimization

Many of our customers that saw demand dry up during the pandemic are now seeing demand return.  Some are seeing a significant demand surge. Other customers in critical industries like plastics, biotech, semiconductors and electronics saw demand surges starting as far back as last April. For suggestions about how to cope with these situations, please read on.

Surging demand usually creates two problems: inability to fill orders and inability to get replenishment due to supplier overload. This situation requires changes in the way you use your advanced planning software. Here are three tips to help you cope.

 

Tip #1: Narrow your temporal focus

 

In normal times (remember those?), more data implied better results. Nowadays, old data poison your calculations, since they represent conditions that no longer apply. You should base forecasts and other calculations on data from the current situation. Where to cut off past data may be obvious from a plot of the data, or you may decide to set a “reasonable” cutoff date based on a consensus of colleagues.  Smart Software has developed machine learning algorithms that automatically identify how much historical data should be optimally fed to the forecast model. Be on the lookout for these enhancements to the software that will be rolling out soon. In the meantime, conduct accuracy tests using held-out actuals using different historical start dates.  Smart’s forecast vs. actual feature will support this automatically.

Smart Demand Planner forecasts vs. actual report

 

Tip #2: Increase your planning tempo

 

When operations are stable, you can set your inventory policies and trust them to be appropriate for a long time. When times are turbulent, it is important to increase the frequency of your planning cycles to keep old policy settings from drifting too far away from optimality.  More frequent recalibration of your stocking policies and forecasts means that you’ll be quicker to catch trends that will surprise your competition and always keep you steps ahead.  With software capable of automatically selecting optimal values, all that work can be done in one shot by the software. You should review those changes and possibly tweak them, but it makes sense to let the software do the bulk of the work.

 

Tip #3: Do more What-If planning

 

In turbulent times, you might expect even more turbulence in the future. Using your software for what-if planning helps you prepare for changes that may be coming. For example, suppose you’ve been in touch with a key supplier who hints that they may be raising prices or may have to slip their delivery schedules. By feeding the software different inputs, you can do contingency planning. If prices go up, you can see how responding by changing order quantities would impact your inventory operating costs and inventory investment. If lead times go up, you can see what the impact would be on item availability. This foreknowledge helps you figure out what your counter-moves would be before the crisis hits.

If there ever was a time when we could cruise on automatic pilot, it’s in the rear-view mirror. Your organization, coping with explosive growth, has many challenges. Old answers are obsolete; new answers have to come from somewhere, fast. Advanced software that leverages probabilistic forecasting can help, along with changes in planning processes.

 

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        Six Steps Up the Learning Curve for New Planners

        The Smart Forecaster

         Pursuing best practices in demand planning,

        forecasting and inventory optimization

        If you are a new professional in the field of inventory management, you face a very steep learning curve. There are many moving parts in the system you manage, and much of the movement is random. You may find it helpful to take a step back from the day-to-day flow to think about what it takes to be successful. Here are six suggestions that you may find useful; they are distilled from working over thirty five years with some very smart practitioners.

         

        1. Know what winning means.

        Inventory management is not a squishy area where success can be described in vague language. Success here is a numbers game. There a number of key performance indicators (KPI’s) available to you, including Service Level, Fill Rate, Inventory Turns, Inventory Investment, and Inventory Operating Cost. Companies differ in the importance they assign to each metric such, but you can’t win without using some or all of these to keep score.

        But “winning” is not as simple as getting the best possible score on each metric. The metric values that are most important vary across companies. Your company may prioritize customer service over cost control, or vice versa, and next year it might have reason to reverse that preference.

        Furthermore, there are linkages among KPI’s that require you to think of them simultaneously rather than as a collection of independent scores. For example, improving Service Level will usually also improve Fill Rate, which is good, but it will also usually increase Operating Cost, which is not good.

        These linkages express themselves as tradeoffs. And while the KPI’s themselves are numbers, the management of the bundle of KPI’s requires some wise subjectivity, because what is needed is a reasonable balance among competing forces. The fundamental tradeoff is to balance the cost of having inventory against the value of having the inventory available to those who need it.

        If you are relatively junior, these tradeoff judgments may be made higher in the organization, but even then you can play a useful role by insuring that the tradeoffs are exposed and appreciated. This means exposed at a quantitative level, e.g., “We can increase Service Level from 85% to 90%, but it will require $100K more stock in the warehouse.” This kind of specific quantitative knowledge can be provided by advanced supply chain analytics.

         

        2. Keep score.

        We’re all a bit squeamish about being measured, but confident professionals insist on keeping score. Enlightened supervisors understand that external forces can ding the performance of your system (e.g., a key supplier disappears), and that always helps. But whether or not you have good top cover, you cannot demonstrate success, nor can you react to problems, without measuring those KPI’s.

        Keeping score is important, but so is understanding what influences score. Suppose your Service Level has dropped from last month’s value. Is that just the usual month-to-month fluctuation or is it something out of the ordinary? If it is problematic, then you need to diagnose the problem. Often there are several possible suspects. For example, Service Level can drop because the sales and marketing folks did something great and demand has spiked, or because a supplier did something not so great and replenishment lead time has tanked. Software can help you track these key inputs to help your detective work, and supply chain analytics can estimate the impacts of changes in these inputs and point you to compensating responses.

         

        3. Be sure your decisions are fact-based.

        Software can guide you to good decisions, but only if you let it. Inputs such as holding costs, ordering costs, and shortage costs need to be well estimated to get accurate assessment of tradeoffs. Especially important is something as apparently simple as using correct values for item demand, since modeling demand is the starting point for simulating the results of any proposed inventory system design. In fact, if we are willing to stretch the meaning of “fact” a bit to include the results of system simulations, you should not commit to major changes without having reliable predictions of what will happen when you commit to those changes.

         

        4. Realize that yesterday’s answer may not be today’s answer.

        Supply chains are collections of parts, all of which are subject to change over time. Demand that is trending up may start to trend down. Replenishment lead times may slip. Supplier order minima may increase. Component prices may increase due to tariffs. Such factors mean that the facts you collected yesterday can be out of date today, making yesterday’s decisions inappropriate for today’s problems. Vigilance. Check out a prior article detailing the adverse financial impact of infrequent updates to planning parameters.

         

        5. Give each item its due.

        If you are responsible for hundreds or thousands of inventory items, you will be tempted to simplify your life by adopting a “one size fits all” approach. Don’t. SKU’s aren’t exactly like snowflakes, but some differentiation is required to do your job well. It’s a good idea to form groups of items based on some salient characteristics. Some items are critical and must (almost) always be available; others can run some reasonable risk of being backordered. Some items are quite unpredictable because they are “intermittent” (i.e., have lots of zero values with nonzero values mixed in at random); others have high volume and are reasonably predictable. Some items can be managed with relatively inexpensive inventory methods that make adjustments every month; some items need methods that continuously monitor and adjust the stock on hand. Some items, such as contractual purchases, may be so predictable that you can treat them as “planned demand” and pull them out from the rest.

        Once you have formed sensible item groups, you still have decisions to make about each item in each group, such as deciding their reorder points and order quantities. Here advanced analytics can take over and automatically compute the best choices based on what winning means in the context of that group.  

         

        6. Get everybody on the same page.

        Being organized is not only pleasing, it’s efficient. If you have a system for inventory management, then everybody on your team shares the same objectives and follows the same processes. If you don’t have a system, then every planner has his or her own way of thinking about the problem and making decisions. Some of those are bound to be better than others. It’s desirable to standardize on the best practices and ban the rest. Besides being more efficient, having a standardized process makes it easier to diagnose problems when things go wrong and to implement fixes.

         

        Volume and color boxes in a warehouese

         

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          Inventory Optimization for Manufacturers, Distributors, and MRO

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              Here are six suggestions that you may find useful; they are distilled from working over thirty five years with some very smart practitioners. Cloud computing companies with unique server and hardware parts, e-commerce, online retailers, home and office supply companies, onsite furniture, power utilities, intensive assets maintenance or warehousing for water supply companies have increased their activity during the pandemic. Garages selling car parts and truck parts, pharmaceuticals, healthcare or medical supply manufacturers and safety product suppliers are dealing with increasing demand. Delivery service companies, cleaning services, liquor stores and canned or jarred goods warehouses, home improvement stores, gardening suppliers, yard care companies, hardware, kitchen and baking supplies stores, home furniture suppliers with high demand are facing stockouts, long lead times, inventory shortage costs, higher operating costs and ordering costs.