I need you to answer 3 discussion post questions from my peers and state whether you agree or disagree: (BUSI 2113 Production and Operations Management)

Post 1: Demand forecasting for a new product is the estimated gain concerning a product in a physical unit. The techniques used for demand forecasting are the substitute approach, evolutionary approach, buyer or consumer's view, and vicarious approach. There are some challenges attached to the demand forecasting process. Some of them could be higher availability and higher data accuracy.

The following techniques are used for demand forecasting of new products/services. Substitute approach - It is assumed that the new product will replace an old product; thus, surveys are carried out, and results for demand are declared. Evolutionary approach - It is assumed that the new product will be considered a better version of the old product. It is believed that the new product can follow the life cycle of the old product to a certain extent. Buyer's or consumer's view - In this approach, the buyers are asked for opinions and, if they would buy the product, how much they would buy. Sales forecasts are made based on their answer. Vicarious approach - This approach takes into consideration the opinion of the experts who are present in the marketing area. Based on collected data, forecasts are made.

Challenges to forecasting a new product or service area, Limited data is available. The starting point for predicting demand for a new product is a review of previous data or current sales figures. For most products, it is not possible to find them. It is possible if the new product is like the old one. Organizational bias- The stakeholders investing in the forecast research want to hear that the product will gain success in the market, and that creates a bias in the study results declarations. Low Accuracy -There is high inaccuracy in studying the demand for new products. In the FMCG industry, most of the demand forecasts have a failure rate of 70-80%

Post 2: That is Silicon Valley in Northern California's Southern subregion, close to San Francisco Bay. We act as a hub for high-tech, innovation, and social media communication on a worldwide scale.

Today's leading businesses are always developing novel concepts, items, offerings, and business methods.

How did you acquire your knowledge? In general, financial reporting, operational tracking, data analysis, and model building are the key goals of forecasting. The primary prognostic categories include:

1. According to qualitative and quantitative analyses

2. The prognosis in full

3. Forecasting time series

Customer feedback is analyzed using qualitative/quantitative analysis, which forecasts the future based on historical numerical data. Qualitative forecasting is how Delphi analyses previous lifecycle data and consumer input. The same chapter claims that time-series forecasting and judgemental forecasting, a sort of casual prediction employing regression analysis and moving averages using a data approach termed casual forecast, are vital for the market analysis required for forecasting high-tech, innovation, ideas, etc. By considering historical data, these analyses are utilized to forecast fluctuation factors. The value of variables for future output may be predicted using mathematical functions in opportunity forecasting.

Evaluation predictions include mixed forecasts, Delphi approaches, statistical analyses or polls, and surveys. This chapter claims that products represent novel and ground-breaking concepts to be developed.

In order to predict the future, time series forecasting use a number of methods, including moving averages, exponential smoothing analysis, and historical data. Data in a time series are those that are accumulated through time. For instance, one business said that he sold 20 billion things in 30 days in January. He had two 30-day periods with revenues of 20.5 billion, and the following 30-day period saw 18 billion. The needed output is 19 billion, which is higher in terms of organizational innovation services, product volume, social factors, etc. when calculated using the average technique.

Post 3: Forecasting new products or services in Silicon Valley, often hailed as the epicenter of technological innovation, is a multifaceted and dynamic process that requires a combination of industry knowledge, data analysis, and a keen understanding of market trends. As a hotbed for high technology, cutting-edge companies in Silicon Valley continuously churn out new ideas, products, services, and business models that have the potential to shape the future (pique et al., 2018). However, navigating this fast-paced landscape and accurately predicting which innovations will become game-changers is no easy task.

One of the primary ways companies forecast new products or services is through extensive market research. In this ever-changing tech landscape, understanding consumers needs, preferences, and pain points is critical (Davis et al., 2020). Market research involves collecting and analyzing data from a variety of sources, including surveys, focus groups, online reviews, and competitor analysis. By meticulously examining this data, companies can uncover potential opportunities for innovation and identify gaps in the market that could be filled with ground-breaking products or services.

Technology scanning plays a pivotal role in forecasting within Silicon Valley. With the rapid pace of technological advancements, staying ahead of the curve is paramount. Companies keep a close eye or research papers, patents, and development in relevant fields to anticipate how cutting-edge technologies can be applied to create novel solutions (Fung et al., 2018). The convergence of different technologies often leads to revolutionary breakthroughs, and having foresight in this area can give a company a significant competitive advantage.

In addition to technology scanning, trend analysis is another critical component of forecasting. By analyzing macroeconomic trends, societal shifts, and changes in consumer behaviour, companies gain insights into where the market is headed. This understanding helps them identify potential growth areas and align their strategies accordingly. For instance, the rise of remote work and the gig economy has spurred the development of collaborative tools and platforms that cater to the needs of remote teams.

Customer feedback and co-relation also serve as valuable sources of information for forecasting. Engaging directly with customers and involving them in the product development process can lead to ground-breaking insights. By actively listening to customer feedback and involving then in co-creating sessions, companies can identify pain and areas of improvement in existing products, as well as generate innovative ideas that precisely align with customer needs.

A significant challenge in forecasting is the risk associated with overestimating or underestimating demand. If a company overestimates demand, it may lead to overproduction, excess inventory, and financial losses. On the other hand, underestimating demand might result in missed opportunities and the inability to meet customer needs, potentially leading to lost market share (Taylor and Letham, 2018).