Please read this article :

there are some queestions that i need answer of :

1. Search the internet and find a company that has made a mistake in forecasting.

2. Write a short introduction about the company that has made the mistake.This section should include company name, business/industry, mission, vision, products, and target market.

3. Describe the mistake and explain the nature of the mistake.

4. Analyze the effects of the mistake on inventory control and two other activities of the business.Make sure you write these points in organized paragraphs.

5. Provide three suggestions for fixing such a mistake and avoiding it in the future.

Sometimes its important to take a step back and consider a different view of what we immerse ourselves in day-to-day. Stephen McHenry, Chancellor of SR Engineering for Google, says thats particularly true when it comes to forecasting. He advocates focusing not just on formulas or models, but on the assumptions on which a given model is based, which may render it completely invalid.

In a presentation at the forecasters recent Business Forecasting and Planning Excellence conference in Las Vegas, McHenry outlined the top ten planning mistakes made by companies across industries. And no doubt, most translate well to pharma.

#10 Not understanding the market factors creating adoption of your product McHenry says that although the merits of your product quality, cost or popularity, for instance may drive adoption, less obvious reasons may influence its uptake by consumers. He points to an example of when a second tier manufacturers computer disks experienced high consumer demand, but only because drives from the original equipment manufacturer were unavailable. Demand for the alternate drives fell off as soon as the OEM adjusted its manufacturing capacity to meet demand for original equipment drives.

Fad or viral effects, he says, may also drive an artificial or short-lived demand.

#9 Not responding quickly enough (or being able to respond) to changing market conditions If demand surges unexpectedly, McHenry says, rather than buying land and building more manufacturing facilities (which limits quick flexibility), companies might be better off taking options on future facilities or outsourcing until a better picture of long-term conditions can be discerned.

Dont commit too much, too early irreversibly, he says. He suggests a four-step what if approach for addressing any risks with a greater than 20% chance of happening.

People should at least be asking themselves what is the single biggest risk they face, McHenry advises. Just having contingencies in place for their biggest risk would help measurably.

#8 Insufficient market validation You probably cant ever know too much about your market, he says. But I dont mean do marketing studies forever and dont ever introduce a product.

He offers an example of a database vendor building a replication product. Using a focus group of its five largest customers, the company designed its products around their specific and complex needs, only to find out it was too expensive and difficult to implement for the majority of its customers small companies with more limited budgets and less IT administrative support available to them.

#7 Using unreliable product timing information Often the product introduction cycle is built on a house of cards, McHenry says.

For example, the product manager, he says, works with engineering who insists they need a release with certain features, on a certain date, with a certain team. And the product manager, under the gun from upper management, reluctantly agrees, despite misgivings about whether the grocery list of features is achievable in the ambitious time frame. He figures hell make nice for now and worry about the fallout later.

But if forecasts are built around having the product on time, theyre built on a house of cards, McHenry says.

He says it also happens when companies face the redesign conundrum. For instance, they have a long-running, very successful product that over the years theyve relied on and asked more and more of, by hanging new requirements or functionalities on it, until it finally reaches a breaking point where it must be modernized through redesign. Because the original design must be maintained while a new one is being rolled out, resources are often stretched thin and the redesign ends up taking much longer than ever imagined.

Many such projects are cancelled before they ever come to maturity because they are always late and management loses confidence that the team will ever be able to deliver, McHenry says. And revenue forecasts are generally built on having the new system by a certain date.

#6 Failure to recognize that you shipped the prototype Not literally, but

McHenry says when faced with some of the pressures outlined in #7, companies will refeature their product again and again until theyre left with something so pared down that it more closely resembles a prototype than it does the product they set out to introduce. Subsequent releases, he says, only serve to bring the product back to square one, and still cost all the time and money that bringing a truly enhanced product to market would have.

And the real kicker their forecast was based on the full suite of features being introduced with the initial release.

#5 Inaccurately predicting market adoption rates The newer and more revolutionary a product is, the longer the flat part of the curve is before the inflection point of increased adoption is reached, McHenry. When building a forecast for these kinds of products, he urges companies to build in enough time for the product to stabilize and for early adopters to come on and evangelize.

On the flip side, he says, companies also are guilty of

#4 Inaccurately predicting demand fall-off His advice to plan for this phenomenon earlier than it seems reasonable to expect. Its never as late as its predicted to be, McHenry warns.

And thats usually, he says, because companies have downplayed the competitive landscape.

#3 Building the forecast around the target McHenry says that, despite what weve been lead to believe, management, Wall Street and our mothers arent always right. And instead of building our forecasts around whatever target were being asked to meet, we must build forecasts around reality.

Start-ups reliant on venture capital and divisions within bigger companies lead by new managers are particularly susceptible to being pressured to meet targets and tailoring their forecasts to meet expectations.

Dont get me wrong, he says. Sometimes aggressive forecasts are good to push reform. Sometimes they help flush out underperforming people."

But, McHenry says, you must understand the risk in allowing forecasts to be influenced by such targets.

#2 Counting on complex dependencies between release time and revenue If youre shipping a product on the last day of the a quarter so that you can recognize revenue in that quarter, youre playing a very dangerous game, he warns. Any hiccup in that strategy pushes you into the next quarter. And it puts you under incredible pressure to get the product out, no matter what.

And it can all tie back to scenarios like those considered in previous planning mistakes (e.g. #3, #6, #7), he says.

#1 Failure to recognize your actual market One of the most fatal planning mistakes

This, McHenry says, is the boiling the frog analogy. In short, if you put a frog directly into a pot of boiling water, hell jump out. But if you put him into a pot of warm water and slowly heat the water to boiling, hell hang out and cook. Why? Because he never senses a dramatic change in temperature.

Smith-Corona, the now defunct typewriter manufacturer, found itself in a slow-to-boil pot of water, McHenry explains.

In 2000, the president of Smith-Corona, at the closing of the companys very last plant, gathered together the remaining employees and told them that on that day, the company had the highest quality product, with the lowest defect rate, greatest customer satisfaction levels and lowest return rates it had ever produced, he says. And then he told them that they had perfected the irrelevant.

Why? Because computers had made the typewriter obsolete. So despite making the best typewriter ever produced, the company was out of business.

They mistook their activity building typewriters for their value proposition getting ideas onto paper, recording and formatting ideas, McHenry says. But it probably took 10 or 12 years for them to slowly boil and cook their company.

But there are counter examples, he says. Television didnt kill radio as it was predicted it would, it just changed the way people used it.

And McHenry offers a bonus mistake

#0 Not doing it Dude its the new way to do business. Forecasting is so old school. We just put up a cool website with an e in front of it.

Despite all of these hurdles and pitfalls, McHenry argues that failing to build a forecast, would be the biggest mistake of all.