netrashetty
Netra Shetty
Digi-Key is the fourth largest electronic component distributor in North America and a broad-line distributor of board level components. It ranks as the 8th largest electronic component distributor in the world. Ronald Stordahl founded the company in 1972 and its name was derived from the digital electronic keyer kit that he developed and marketed to amateur radio enthusiasts.[1] It was called the “Digi-Keyer Kit.”
Mark Larson, who joined Digi-Key in 1976 as its general manager, has been its president since 1985. He has led the company from its initial focus on the hobbyist market to the expanded market it serves today.
In 1995, Digi-Key introduced its website that offers complete online commerce capabilities along with access to product inventory. Digi-Key currently hosts 82 websites in eight supported languages.
In only six years, Digi-Key has moved from 16th largest to fifth largest among the more than 300 electronic component distributors in North America. According to their website, in 2007 EE Times conducted a survey in which engineers rated Digi-Key “#1 for Overall Performance for the 16th Consecutive Year.”
Digi-Key is located in a single, centralized location in Thief River Falls, Minnesota, USA. Its facility measures 600,000 square feet (56,000 m2) and houses over 2,300 employees. Its product distribution center stocks over 500,000 products from over 440 manufacturers and ships to 170 countries worldwide.
Digi-Key serves design engineers and the prototyping market, as well as an expanding role in supplying production quantities for OEMs and contract manufacturers.
[edit]
results are having a huge impact on advancing the engineering design of CNH loader backhoes. This was expected. Operators indicated clear differences in performance among the five brands. These findings are being used not only to better differentiate CNH brands from competitors but also to differentiate among the CNH brands.
Not expected, however, was the finding that non-performance attributes play a much bigger role in brand selection than was widely believed. There was a time when the loader backhoe brand with the best engineering and job performance would garner brand share. The market is more complicated and operators are more demanding now. In many ways operators now mirror the demanding purchasing behaviors of consumer product customers.
Today's loader backhoe market has evolved to the point where operators now expect excellent engineering and high performance from all brands. They then make purchase decisions in part on non-engineering factors such as after-purchase service, loyalty to a dealer, convenience features like easy-to-use controls, and in some countries, in response to aggressive marketing campaigns.
As the loader backhoe market has taken on some of the dynamics of a consumer product, many more teams within CNH Global beyond engineering have been impacted by the research results. Marketing and sales, after-purchase parts support and service, financing and credit, strategic planning, marketing research, and other areas are digesting results and planning for the future. Increases in market share will result from integrated product development programs involving all these areas of CNH Global in many varied countries.
The other broad implication of this research to CNH Global is for planning future marketing research. Immense benefit was gained from designing research that fit the way operators think - customized scale questions, use of three translators to capture language nuances, field tests conducted by someone outside of CNH Global, etc. Using standard off-the-shelf questions and procedures would not have revealed the subtleties and richness of detail in this market. One size clearly does not fit all when unearthing construction equipment advancements through marketing research.
, a forecast is based on past data, as opposed to a prediction, which is more subjective and based on instinct, gut feel, or guess. For example, the evening news gives the weather "forecast" not the weather "prediction." Regardless, the terms forecast and prediction are often used inter-changeably. For example, definitions of regression—a technique sometimes used in forecasting—generally state that its purpose is to explain or "predict."
Forecasting is based on a number of assumptions:
The past will repeat itself. In other words, what has happened in the past will happen again in the future.
As the forecast horizon shortens, forecast accuracy increases. For instance, a forecast for tomorrow will be more accurate than a forecast for next month; a forecast for next month will be more accurate than a forecast for next year; and a forecast for next year will be more accurate than a forecast for ten years in the future.
Forecasting in the aggregate is more accurate than forecasting individual items. This means that a company will be able to forecast total demand over its entire spectrum of products more accurately than it will be able to forecast individual stock-keeping units (SKUs). For example, General Motors can more accurately forecast the total number of cars needed for next year than the total number of white Chevrolet Impalas with a certain option package.
Forecasts are seldom accurate. Furthermore, forecasts are almost never totally accurate. While some are very close, few are "right on the money." Therefore, it is wise to offer a forecast "range." If one were to forecast a demand of 100,000 units for the next month, it is extremely unlikely that demand would equal 100,000 exactly. However, a forecast of 90,000 to 110,000 would provide a much larger target for planning.
William J. Stevenson lists a number of characteristics that are common to a good forecast:
Accurate—some degree of accuracy should be determined and stated so that comparison can be made to alternative forecasts.
Reliable—the forecast method should consistently provide a good forecast if the user is to establish some degree of confidence.
Timely—a certain amount of time is needed to respond to the forecast so the forecasting horizon must allow for the time necessary to make changes.
Easy to use and understand—users of the forecast must be confident and comfortable working with it.
Cost-effective—the cost of making the forecast should not outweigh the benefits obtained from the forecast.
Forecasting techniques range from the simple to the extremely complex. These techniques are usually classified as being qualitative or quantitative.
QUALITATIVE TECHNIQUES
Qualitative forecasting techniques are generally more subjective than their quantitative counterparts. Qualitative techniques are more useful in the earlier stages of the product life cycle, when less past data exists for use in quantitative methods. Qualitative methods include the Delphi technique, Nominal Group Technique (NGT), sales force opinions, executive opinions, and market research.
THE DELPHI TECHNIQUE.
The Delphi technique uses a panel of experts to produce a forecast. Each expert is asked to provide a forecast specific to the need at hand. After the initial forecasts are made, each expert reads what every other expert wrote and is, of course, influenced by their views. A subsequent forecast is then made by each expert. Each expert then reads again what every other expert wrote and is again influenced by the perceptions of the others. This process repeats itself until each expert nears agreement on the needed scenario or numbers.
NOMINAL GROUP TECHNIQUE.
Nominal Group Technique is similar to the Delphi technique in that it utilizes a group of participants, usually experts. After the participants respond to forecast-related questions, they rank their responses in order of perceived relative importance. Then the rankings are collected and aggregated. Eventually, the group should reach a consensus regarding the priorities of the ranked issues.
SALES FORCE OPINIONS.
The sales staff is often a good source of information regarding future demand. The sales manager may ask for input from each sales-person and aggregate their responses into a sales force composite forecast. Caution should be exercised when using this technique as the members of the sales force may not be able to distinguish between what customers say and what they actually do. Also, if the forecasts will be used to establish sales quotas, the sales force may be tempted to provide lower estimates.
EXECUTIVE OPINIONS.
Sometimes upper-levels managers meet and develop forecasts based on their knowledge of their areas of responsibility. This is sometimes referred to as a jury of executive opinion.
MARKET RESEARCH.
In market research, consumer surveys are used to establish potential demand. Such marketing research usually involves constructing a questionnaire that solicits personal, demographic, economic, and marketing information. On occasion, market researchers collect such information in person at retail outlets and malls, where the consumer can experience—taste, feel, smell, and see—a particular product. The researcher must be careful that the sample of people surveyed is representative of the desired consumer target.
Mark Larson, who joined Digi-Key in 1976 as its general manager, has been its president since 1985. He has led the company from its initial focus on the hobbyist market to the expanded market it serves today.
In 1995, Digi-Key introduced its website that offers complete online commerce capabilities along with access to product inventory. Digi-Key currently hosts 82 websites in eight supported languages.
In only six years, Digi-Key has moved from 16th largest to fifth largest among the more than 300 electronic component distributors in North America. According to their website, in 2007 EE Times conducted a survey in which engineers rated Digi-Key “#1 for Overall Performance for the 16th Consecutive Year.”
Digi-Key is located in a single, centralized location in Thief River Falls, Minnesota, USA. Its facility measures 600,000 square feet (56,000 m2) and houses over 2,300 employees. Its product distribution center stocks over 500,000 products from over 440 manufacturers and ships to 170 countries worldwide.
Digi-Key serves design engineers and the prototyping market, as well as an expanding role in supplying production quantities for OEMs and contract manufacturers.
[edit]
results are having a huge impact on advancing the engineering design of CNH loader backhoes. This was expected. Operators indicated clear differences in performance among the five brands. These findings are being used not only to better differentiate CNH brands from competitors but also to differentiate among the CNH brands.
Not expected, however, was the finding that non-performance attributes play a much bigger role in brand selection than was widely believed. There was a time when the loader backhoe brand with the best engineering and job performance would garner brand share. The market is more complicated and operators are more demanding now. In many ways operators now mirror the demanding purchasing behaviors of consumer product customers.
Today's loader backhoe market has evolved to the point where operators now expect excellent engineering and high performance from all brands. They then make purchase decisions in part on non-engineering factors such as after-purchase service, loyalty to a dealer, convenience features like easy-to-use controls, and in some countries, in response to aggressive marketing campaigns.
As the loader backhoe market has taken on some of the dynamics of a consumer product, many more teams within CNH Global beyond engineering have been impacted by the research results. Marketing and sales, after-purchase parts support and service, financing and credit, strategic planning, marketing research, and other areas are digesting results and planning for the future. Increases in market share will result from integrated product development programs involving all these areas of CNH Global in many varied countries.
The other broad implication of this research to CNH Global is for planning future marketing research. Immense benefit was gained from designing research that fit the way operators think - customized scale questions, use of three translators to capture language nuances, field tests conducted by someone outside of CNH Global, etc. Using standard off-the-shelf questions and procedures would not have revealed the subtleties and richness of detail in this market. One size clearly does not fit all when unearthing construction equipment advancements through marketing research.
, a forecast is based on past data, as opposed to a prediction, which is more subjective and based on instinct, gut feel, or guess. For example, the evening news gives the weather "forecast" not the weather "prediction." Regardless, the terms forecast and prediction are often used inter-changeably. For example, definitions of regression—a technique sometimes used in forecasting—generally state that its purpose is to explain or "predict."
Forecasting is based on a number of assumptions:
The past will repeat itself. In other words, what has happened in the past will happen again in the future.
As the forecast horizon shortens, forecast accuracy increases. For instance, a forecast for tomorrow will be more accurate than a forecast for next month; a forecast for next month will be more accurate than a forecast for next year; and a forecast for next year will be more accurate than a forecast for ten years in the future.
Forecasting in the aggregate is more accurate than forecasting individual items. This means that a company will be able to forecast total demand over its entire spectrum of products more accurately than it will be able to forecast individual stock-keeping units (SKUs). For example, General Motors can more accurately forecast the total number of cars needed for next year than the total number of white Chevrolet Impalas with a certain option package.
Forecasts are seldom accurate. Furthermore, forecasts are almost never totally accurate. While some are very close, few are "right on the money." Therefore, it is wise to offer a forecast "range." If one were to forecast a demand of 100,000 units for the next month, it is extremely unlikely that demand would equal 100,000 exactly. However, a forecast of 90,000 to 110,000 would provide a much larger target for planning.
William J. Stevenson lists a number of characteristics that are common to a good forecast:
Accurate—some degree of accuracy should be determined and stated so that comparison can be made to alternative forecasts.
Reliable—the forecast method should consistently provide a good forecast if the user is to establish some degree of confidence.
Timely—a certain amount of time is needed to respond to the forecast so the forecasting horizon must allow for the time necessary to make changes.
Easy to use and understand—users of the forecast must be confident and comfortable working with it.
Cost-effective—the cost of making the forecast should not outweigh the benefits obtained from the forecast.
Forecasting techniques range from the simple to the extremely complex. These techniques are usually classified as being qualitative or quantitative.
QUALITATIVE TECHNIQUES
Qualitative forecasting techniques are generally more subjective than their quantitative counterparts. Qualitative techniques are more useful in the earlier stages of the product life cycle, when less past data exists for use in quantitative methods. Qualitative methods include the Delphi technique, Nominal Group Technique (NGT), sales force opinions, executive opinions, and market research.
THE DELPHI TECHNIQUE.
The Delphi technique uses a panel of experts to produce a forecast. Each expert is asked to provide a forecast specific to the need at hand. After the initial forecasts are made, each expert reads what every other expert wrote and is, of course, influenced by their views. A subsequent forecast is then made by each expert. Each expert then reads again what every other expert wrote and is again influenced by the perceptions of the others. This process repeats itself until each expert nears agreement on the needed scenario or numbers.
NOMINAL GROUP TECHNIQUE.
Nominal Group Technique is similar to the Delphi technique in that it utilizes a group of participants, usually experts. After the participants respond to forecast-related questions, they rank their responses in order of perceived relative importance. Then the rankings are collected and aggregated. Eventually, the group should reach a consensus regarding the priorities of the ranked issues.
SALES FORCE OPINIONS.
The sales staff is often a good source of information regarding future demand. The sales manager may ask for input from each sales-person and aggregate their responses into a sales force composite forecast. Caution should be exercised when using this technique as the members of the sales force may not be able to distinguish between what customers say and what they actually do. Also, if the forecasts will be used to establish sales quotas, the sales force may be tempted to provide lower estimates.
EXECUTIVE OPINIONS.
Sometimes upper-levels managers meet and develop forecasts based on their knowledge of their areas of responsibility. This is sometimes referred to as a jury of executive opinion.
MARKET RESEARCH.
In market research, consumer surveys are used to establish potential demand. Such marketing research usually involves constructing a questionnaire that solicits personal, demographic, economic, and marketing information. On occasion, market researchers collect such information in person at retail outlets and malls, where the consumer can experience—taste, feel, smell, and see—a particular product. The researcher must be careful that the sample of people surveyed is representative of the desired consumer target.