Worse, it may cause you to miss the most important opportunities on your horizon. Outliers—variously, wild cards or surprises—are what define this edge. A good boundary is one made up of elements lying on the ragged edge of plausibility. They are outcomes that might conceivably happen but make one uncomfortable even to contemplate.
The most commonly considered outliers are wild cards. My favorite example of a wild card, because its probability is so uncertain and its impact so great, is finding radio evidence of intelligent life somewhere else in the universe. Nobody knows if we will ever receive a message radio astronomers have been listening since the late s , but if we did, it would send a vast and unpredictable tremor through the zeitgeist.
The tricky part about wild cards is that it is difficult to acknowledge sufficiently outlandish possibilities without losing your audience. The problem—and the essence of what makes forecasting hard—is that human nature is hardwired to abhor uncertainty. We are fascinated by change, but in our effort to avoid uncertainty we either dismiss outliers entirely or attempt to turn them into certainties that they are not. This is what happened with the Y2K problem in the final years before January 1, Opinions clustered at the extremes, with one group dismissing the predictions of calamity and another stocking up on survival supplies.
The correct posture toward Y2K was that it was a wild card—an event with high potential impact but very low likelihood of occurrence, thanks to years of hard work by legions of programmers fixing old code. The result of the Y2K nonevent was that many people concluded they had been the victims of someone crying Y2K wolf, and they subsequently rejected the possibility of other wild cards ever coming to pass.
After all, airliners flown into monuments were the stuff of Tom Clancy novels in the s inspired by Clancy, I helped write a scenario for the U. Air Force in that opened with a plane being flown into the Pentagon , and it was widely known that the terrorists had a very personal antipathy toward the World Trade Center. Yet the few people who took this wild card seriously were all but dismissed by those who should have been paying close attention. The result of the Y2K nonevent was that many people subsequently rejected the possibility of other wild cards ever coming to pass.
Human nature being what it is, we are just as likely to overreact to an unexpected wild card by seeing new wild cards everywhere. Above all, ask hard questions about whether a seeming wild card in fact deserves to be moved closer to the center. Change rarely unfolds in a straight line. The most important developments typically follow the S-curve shape of a power law: Change starts slowly and incrementally, putters along quietly, and then suddenly explodes, eventually tapering off and even dropping back down.
But it will flatten eventually, certainly with regard to silicon circuit density. This distinction reveals another important feature of S curves, which is that they are fractal in nature. Very large, broadly defined curves are composed of small, precisely defined and linked S curves.
For a forecaster, the discovery of an emergent S curve should lead you to suspect a larger, more important curve lurking in the background.
Miss the larger curve and your strategy may amount to standing on a whale, fishing for minnows. The art of forecasting is to identify an S-curve pattern as it begins to emerge, well ahead of the inflection point. The tricky part of S curves is that they inevitably invite us to focus on the inflection point, that dramatic moment of takeoff when fortunes are made and revolutions launched.
His discovery falls at the inflection point of Western exploration. Columbus was not the first fifteenth-century explorer to go to the New World—he was the first to make it back, and he did so at a moment when his discovery would land like a spark in the economic tinder of a newly emergent Europe and launch thousands upon thousands of voyages westward.
Ironically, forecasters can do worse than ordinary observers when it comes to anticipating inflection points. Ordinary folks are simply surprised when an inflection point arrives seemingly out of nowhere, but innovators and would-be forecasters who glimpse the flat-line beginnings of the S curve often miscalculate the speed at which the inflection point will arrive.
As futurist Roy Amara pointed out to me three decades ago, there is a tendency to overestimate the short term and underestimate the long term. Our hopes cause us to conclude that the revolution will arrive overnight. Then, when cold reality fails to conform to our inflated expectations, our disappointment leads us to conclude that the hoped-for revolution will never arrive at all—right before it does. One reason for the miscalculations is that the left-hand part of the S curve is much longer than most people imagine.
Television took 20 years, plus time out for a war, to go from invention in the s to takeoff in the early s. Even in that hotbed of rapid change, Silicon Valley, most ideas take 20 years to become an overnight success.
The Internet was almost 20 years old in , the year that it began its dramatic run-up to the s dot-com eruption. So having identified the origins and shape of the left-hand side of the S curve, you are always safer betting that events will unfold slowly than concluding that a sudden shift is in the wind.
Once an inflection point arrives, people commonly underestimate the speed with which change will occur. The fact is, we are all by nature linear thinkers, and phenomena governed by the sudden, exponential growth of power laws catch us by surprise again and again.
Even if we notice the beginning of a change, we instinctively draw a straight line diagonally through the S curve, and although we eventually arrive in the same spot, we miss both the lag at the start and the explosive growth in the middle.
Timing, of course, is everything, and Silicon Valley is littered with the corpses of companies who mistook a clear view for a short distance and others who misjudged the magnitude of the S curve they happened upon. Also expect the opportunities to be very different from those the majority predicts, for even the most expected futures tend to arrive in utterly unexpected ways. In the early s, for example, PC makers predicted that every home would shortly have a PC on which people would do word processing and use spreadsheets or, later, read encyclopedias on CDs.
Internal business demand forecasts review your operations. The internal business forecasting type will uncover limitations that might slow your growth. It can also highlight untapped areas of opportunity within the organization. This forecasting model factors in your business financing, cash on hand, profit margins, supply chain operations, and personnel. Internal business demand forecasting is a helpful tool for making realistic projections. It can also point you toward areas where you need to build capacity in order to meet expansion goals.
There are many different ways to create forecasts. Here are five of the top demand forecasting methods. Trend projection uses your past sales data to project your future sales.
It is the simplest and most straightforward demand forecasting method. For example, perhaps you had a sudden spike in demand last year. However, it happened after your product was featured on a popular television show, so it is unlikely to repeat.
Or your eCommerce site got hacked, causing your sales to plunge. Be sure to note unusual factors in your historical data when you use the trend projection method. Market research demand forecasting is based on data from customer surveys. You can do this research on an ongoing basis or during an intensive research period.
Market research can give you a better picture of your typical customer. Your surveys can collect demographic data that will help you target future marketing efforts. Market research is particularly helpful for young companies that are just getting to know their customers. It uses feedback from the sales group to forecast customer demand. Your salespeople have the closest contact with your customers.
They hear feedback and take requests. As a result, they are a great source of data on customer desires, product trends, and what your competitors are doing. This method gathers the sales division with your managers and executives. The group meets to develop the forecast as a team. The Delphi method, or Delphi technique, leverages expert opinions on your market forecast. This method requires engaging outside experts and a skilled facilitator. You start by sending a questionnaire to a group of demand forecasting experts.
You create a summary of the responses from the first round and share it with your panel. This process is repeated through successive rounds. The answers from each round, shared anonymously, influence the next set of responses. The Delphi method is complete when the group comes to a consensus. This demand forecasting method allows you to draw on the knowledge of people with different areas of expertise.
The fact that the responses are anonymized allows each person to provide frank answers. Because there is no in-person discussion, you can include experts from anywhere in the world on your panel. The end result is an informed consensus. The econometric method requires some number crunching. These factors - known as the sales forecast assumptions - form the basis of your forecast. Wherever possible, put a figure against the change - as shown in the examples below.
You can then get a feel for the impact it will have on your business. Also, give the reasoning behind each figure, so that other people can comment on whether it's realistic. For new businesses , the assumptions need to be based on market research and good judgement. Start by writing down your sales assumptions.
See the page in this guide on your sales assumptions. You can then create your sales forecast. This becomes easy once you've found a way to break the forecast down into individual items. Selling more of your product to an existing customer is far easier than making a first sale to a new customer.
So the conversion rates for existing customers are much higher than those for new customers. You may want to include details of which product each customer is likely to buy. Then you can spot potential problems. One product could sell out, while another might not move at all. By predicting actual sales, you're forecasting what you think will be sold. This is generally far more accurate than forecasting from a target figure and then trying to work out how to achieve it.
The completed sales forecast isn't just used to plan and monitor your sales efforts. It's also a vital part of the cash flow. There is a wide range of sales forecasting software available that can make the whole process much simpler and more accurate.
This software generates forecasts based on historical data. If you are considering buying software, get advice from an IT expert, your trade association, your business advisors and businesses of a similar size and in similar markets. It's all too easy to be over-optimistic. It's a good idea to look back at the previous year's forecast to see if your figures were realistic. New businesses should avoid the mistake of working out the level of sales they need for the business to be viable, then putting this figure in as the forecast.
You also need to consider if it is physically possible to achieve the sales levels you're forecasting. For example:. The definition of full-time may vary by region. For example, in the United States, a full-time employee typically works 40 hours per week. The main purpose of the monthly forecast is to drive hiring and staffing plans. To determine how many FTEs are required, the first step is to forecast the workload.
Workload: The contact or call volume the number of incoming messages or calls multiplied by the average handle time AHT of a call. Average handle time : The average time needed for a call, including hold times and after-call work.
Forecasts for volume and handle time should be built separately, however they should both begin with the historical data collected by your contact center over the past months or years. After creating a forecast based on historical data, the next step is to layer in business intelligence.
Business Intelligence : Any information that can explain why the future will be different from the past e. Once both forecasts are built, multiply the call volume by the handle time to derive your workload forecast.
The workload forecast is then used to calculate the required staff per time period. Start by selecting a staff group and gather as much historical data as you can for that group. Try to gather data from the past 36 months to understand volume trends and seasonality patterns over time.
The easiest way to start is to identify the year-over-year growth to establish what your volume should be for the current year. In this example, the average is 3. Considering this growth rate, the forecast would add 2, calls to the volume from , for a grand total of 56, contacts for the forecast.
To quickly generate a monthly call volume forecast that will provide high-level direction to your operations:. For the forecast, simply multiply the percentage for each month by the total volume for the year.
Using the full year forecast of 56, calls, the monthly volumes would appear as follows:. The AHT forecast is essentially created in the same way as the call volume forecast:. You can take this same process and calculate a weekly, long-term forecast which might be helpful for some long-term staffing decisions, for example, determining the amount of flexibility that will be required of agents when it comes to scheduling.
Short-term forecasting is the input to the scheduling process. Weekly and daily forecasts are typically created weeks in advance.
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