What are the most common challenges in forecasting demand for new products?

Many factors can impact the demand forecasting for a new launched product. The most important ones from my point of view is the competitive landscape which can influence demand for a new product. Understanding competitor strategies, pricing, and market positioning is crucial for accurate forecasting. In addition to consumer preferences that can shift rapidly, particularly in industries with high innovation rates. Forecasting demand requires anticipating these changes and their potential impact on the market.

In my experience, "market research" is too broad a concept to rely on for product development. If you have an existing customer base, that's your best market to research, bar none. Customers cannot tell you what products you can create - that's your job. Instead, there are three open-ended questions they should be asked -- in actual conversations, not in online surveys (you'll get real insights this way): 1. What do you like about the products you've bought from us and others? 2. What have you NOT liked about these products? 3. What are some problems your organization has that you wish there was a solution for? As innovators, studying and creatively responding to these insights can help you develop relevant and useful products.

1. Primary research is important , this not only help in forecasting but also confidence of go to market strategy 2. Gut feeling 3. Knowledge of distribution channel and methodology of product promotion 4. Experienced team 5. Selection of right tool 6. Understanding of analytical result and data.

O desafio na previs?o da demanda por novos produtos reside na falta de dados confiáveis, seja pela ausência de registros de vendas e feedback de clientes ou pela distor??o causada por fatores como promo??es e sazonalidade. Para superar isso, os gerentes de logística podem usar métodos como pesquisas, grupos focais, mercados de teste, opini?es de especialistas e compara??es com produtos similares, a fim de obter insights mais precisos e informar suas decis?es de lan?amento de produtos.

In my experience, insufficient market data is one of the highest risk categories in demand planning. You can't speculate on trends or product life cycle with out

Indeed. As much as you plan well, definitely uncertainties are part of the real world. Understanding customers needs and investing on market researches can for sure manage expectations for all stakeholders.

Scenarios, sensitivity analysis, and simulation models help logistics managers overcome uncertainty by providing tools to assess and quantify the potential impact of uncertain factors on demand forecasts. Scenarios allow managers to explore different possible future scenarios, sensitivity analysis helps identify key variables that influence demand, and simulation models simulate different scenarios to evaluate their potential outcomes. These methods enable managers to make more robust and flexible forecasts that account for uncertainty and variability.

Previs?o de demanda para produtos "maduros" já é difícil, mesmo tendo dominancia nas categorias, agora, super mais difícil é prever demanda para novos produtos. Existem diversas técnicas matemáticas/estatísticas que ajudam, mas uma ótima forma é ir apostando aos poucos, mas avan?ando sempre é de olho no que está dando certo e o que n?o está dando. A rápida percep??o e mudan?a ainda costuma ser a melhor forma de melhor o produto é servi?o, principalmente quando novos.

In my view, a key challenge with new product demand forecasting is understanding the true Incremental volume versus cannibalization of existing products. Even with good aggregate forecasts, it's difficult to predict how a new product may divert demand from older offerings. This makes it tricky to dimension manufacturing, inventory, and distribution just for the incremental volume. Continuously monitoring sales mix trends and customer segmentation analysis is important to adapt forecasts as actual cannibalization rates become clearer after launch.

A previs?o de demanda por novos produtos pode encontrar desafios comuns relacionados à falta de dados históricos para embasar a análise. Sem um histórico adequado, a previs?o pode se basear em suposi??es mais do que em informa??es concretas. Além disso, a falta de dados precisos sobre o mercado, comportamento do consumidor e tendências emergentes pode dificultar a precis?o das previs?es. Nesses casos, estratégias como pesquisa de mercado, estudo de tendências e incorpora??o de dados substitutos podem ajudar a mitigar o impacto da escassez de dados históricos

Certainly, the challenge of forecasting demand for new products is compounded by the complex interactions between various factors. These interactions can make it difficult to isolate the impact of individual variables and accurately predict overall demand. Some examples of these complex interactions include: Market dynamics: The market for new products is influenced by a multitude of factors, including consumer preferences, competitive landscape, economic conditions, and regulatory environment. These factors often interact in complex ways, making it challenging to forecast demand accurately.

Forecasting demand for new products becomes even trickier when considering the intricate web of relationships they might have with existing products, processes, or systems. Each interaction introduces another layer of complexity to the forecasting process.

Imagine que o item 1 sobre dados foi resolvido e que você pode trazer uma camada de inteligência artificial com o objetivo de trazer mais visibilidade e previsibilidade para toda a cadeia, pode ser um caminho para cadeias complexas e com baixa previsibilidade

Complex interactions are one of the critical areas in demand planning. Even Geo-Political and Socio - Political reasons across nations plays a bigger role in complexities to overcome challenges, Agile supply chain , Portfolio analysis and Advance forecasting and planning for allocation is required., The resources must be aligned to have all the data base and all possible means to overcome intricacies

Rapidly changing customer preferences and behavior can make accurate forecasting a challenge. It is essential to consider evolving consumer trends, demographic shifts, and new technologies that might affect customer purchasing decisions. Effective forecasting requires a deep understanding of statistical models, data analysis techniques, and domain expertise. Lack of expertise and experience in forecasting methods can lead to suboptimal predictions and inaccurate forecasts.

Limited resources and capabilities within logistics management can pose a significant challenge when forecasting demand for new products. These can stem from various factors with one of them being lack of specialised skills among the team. Forecasting demand for new products requires analytical, statistical and domain specific knowledge. the absence of these leads to inaccuracies in the process. for on-the-job training, a solution could be to foster a culture of continuous learning and improvement within the organisation. We should encourage employees to stay updated on industry trends, emerging technologies, and best practices in the field through ongoing education and professional opportunities.

A Importancia da Administra??o de Pessoal na Gest?o de RH: Além da Folha de Pagamento. Muito se fala sobre Recrutamento & Sele??o, Treinamento & Desenvolvimento e compensa??es e benefícios na gest?o de Recursos Humanos, e com raz?o, s?o aspectos fundamentais para o sucesso das organiza??es. No entanto, há um elemento muitas vezes subestimado que desempenha um papel crucial nos bastidores: a Administra??o de Pessoal. Enquanto R&S e T&D s?o responsáveis por trazer e desenvolver talentos, respectivamente, a Administra??o de Pessoal atua como a espinha dorsal, garantindo que todos os processos relacionados aos colaboradores sejam executados de forma eficiente e precisa. Um dos principais pilares dessa fun??o é a gest?o da folha de pagamento.

A limita??o de recursos e capacidades representa um obstáculo significativo na previs?o de demanda para novos produtos, exigindo abordagens inovadoras para otimizar o processo. A colabora??o entre diferentes partes interessadas, a terceiriza??o e o uso de solu??es tecnológicas avan?adas, como plataformas baseadas em nuvem, emergem como estratégias cruciais. Estas abordagens n?o apenas ampliam o acesso a habilidades e ferramentas especializadas, mas também promovem uma compreens?o mais holística e integrada das expectativas de mercado, permitindo previs?es mais precisas e adaptáveis. A chave para superar esses desafios está na capacidade de alavancar eficientemente recursos externos e fomentar a colabora??o interdepartamental.

Join forces with stakeholders - product devs, marketers, even customers - for diverse insights. Embrace agile, iterative forecasts & leverage cloud-based tools to boost efficiency. Remember, community is power - share & learn with fellow logistics pros!

La evaluación constante es un punto importante para controlar la previsión de la demanda en el lanzamiento de un nuevo producto, el área de operaciones tiene que analizar los datos recolectados desde el primer día de lanzamiento, así como la utilización de herramientas de gestión importantes para realizar un pronóstico. A pesar de tener recursos limitados, siempre debe existir un análisis del proyecto, y evaluar si su funcionamiento es el esperado o en defecto buscar alternativas de mejora.

Forecasting demand for new products is inherently challenging due to uncertainties and dynamic market conditions. The fifth challenge revolves around the effective evaluation of the forecasting process and the incorporation of feedback mechanisms to enhance accuracy and responsiveness. Successfully addressing the evaluation and feedback challenge in forecasting demand for new products requires a holistic approach that combines advanced technologies, collaborative processes, and a continuous improvement mindset. By embracing agility and adaptability, organizations can enhance the accuracy of their demand forecasts and respond effectively to market dynamics.

What gets measured improves. With focus on quicker results and fast turnover, looking back for introspection is neglected. PDCA cycle needs to be adopted, even in demand forecasting.

No setor que estou inserido a incerteza da informa??o é o maior Gap. N?o diretamente ligado ao processo operacional, no entanto muito pelo que o mercado fala, hoje de flexibilidade de atendimento, varia??o de produtos ou também da personaliza??o do perfil do cliente. Parte em si pela quest?o da falta técnica e da informa??o de produtos alternativos o departamento logístico sofre em cumprir o nível de atendimento desejado.

Las reuniones de S&OP son fundamentales para estar todos informados ya sea para realizar cambios de aumento o disminución, es 100% recomendable para la salida de nuevos productos

Forecast errors are the result of: *Unreliable data. The inaccuracy of inventory records on the physical, as well as the lack of reliability of historical demand data, *Sales on demand. It is necessary to develop tools that allow to record the real demand for the items and not only the quantities sold. In addition, it must be considered that these could be different due to low levels of service. *Peaks in demand. The demand, even if it is inelastic, could present variations that normally cannot be predicted and sometimes cause a wrong response, by considering it as constant for the next periods and not identifying it as atypical behaviors motivated by promotions or inventory breaks.

Common Challenges in Forecasting Demand for New Products: Limited Historical Data: Lack of past sales data makes predictions challenging. Market Uncertainty: Changes in consumer preferences and market dynamics are hard to predict. New Product Introductions: Forecasting demand for entirely new products is inherently uncertain. External Factors: Economic shifts, geopolitical events, or pandemics can impact forecasts unexpectedly. Seasonality and Trends: Identifying and adjusting for seasonal patterns and industry trends is complex. Competitive Landscape: Actions of competitors can influence demand unpredictably. Supply Chain Disruptions: Unexpected disruptions can impact the availability of products.

My expertise is focused around these challenges in DoD. Firstly, limited historical data within the defense sector makes it challenging to identify trends or patterns accurately. Secondly, the dynamic nature of defense requirements, supply chain issues, coupled with evolving geopolitical landscapes, contributes to market uncertainty, making it difficult to predict future demand accurately. Additionally, competition among defense contractors and fluctuations in defense budgets can impact demand projections. Lastly, factors such as seasonality in procurement cycles, changes in economic conditions, and unforeseen events like geopolitical tensions or technological advancements can complicate forecasting efforts within the defense sector.

1. Difficulty measuring marketing impact: It can be challenging to quantify and separate the influence of marketing efforts from other aspects, which makes it more difficult to include this element in the prediction. 2. Challenges in Operations: It can be expensive and time-consuming to modify production lines and capacity to accommodate unanticipated changes in demand. It takes careful planning and buffer stock to strike a balance between overstocking and stockouts while managing erratic demand and perhaps lengthy lead times. It might be challenging to maintain a seamless supply chain for new parts and materials with a short demand history.

You'll find plenty of books with methodologies on how to create, develop and market innovative products. Most of them will convey an image of a structured approach and a controllable process for a controllable outcome. This is not how a startup works. Creating a brand new product is chaos. You have more questions than answers, the market never thought about the problem, there's nobody to validate, there's no established method for what you are doing. You can't do alphas and betas. In most cases you go straight to live with what I'd call a pre-alpha prototype and if that doesn't work, you just go back to the drawing board. The reality is that there isn't much time or money to spend with fancy methodologies. Just prototype and try to sell it.

It's very important to consider: - Supply chain management ( lead time since raw materials up to finalized product). - Shelf life - Transit time of goods