How can predictive analytics improve seasonal product demand planning?

Predictive analytics have been beneficial in material planning and gaining efficiency through reducing changeovers, allowing for longer run-times. In my opinion, one of the biggest benefits in the current work environment is to allow better planning for staffing surges. Staffing up for peak seasons in manufacturing with continued potential for increased turnover of the hourly workforce has emerged as one of the biggest hurdles to overcome and ensure OTIF success.

Predictive analytics can significantly improve seasonal demand planning by analyzing 1. Historical data to identify patterns, trends, and seasonal variations in demand. 2. We may also get inputs from i. Sales data, ii. Market trends iii. Seasonal & economic changes iv. Natural disaster v. Regional or global events & holidays. This holistic view enhances the accuracy of predictions. It also helps in understanding how demand has behaved during the past same seasons. This will also be helpful in generating accurate Forecasts to optimize inventory levels, avoiding overstocking or stockouts, reducing holding costs, and improving overall supply chain efficiency. We can also make contingency plans as well.

El consumo no solo se puede prever, también se puede crear la necesidad, para esto está la innovación, dando solución a una necesidad que no ha sido satisfecha y esto desvía también la predicción , creando un nuevo valor agregado en la investigación de Mercado llamado “ tendencia”

Increased Accuracy: Analyze historical sales data and external factors: This includes weather patterns, holidays, trends, promotions, and competitor activity. Account for seasonality: Predictive models can automatically adjust for seasonal fluctuations, preventing understocking during peak periods and overstocking during off-seasons. Enhanced Efficiency: Streamline production and procurement: By anticipating demand peaks and troughs, production schedules and procurement activities can be adjusted in advance. Dynamic pricing strategies: Predictive models can inform pricing decisions based on expected demand and market conditions. Deeper Insights: Predict customer behavior Identify trends and emerging markets

In CMMS systems, Predictive, Preventive maintenance data along with failure analysis from corrective work orders drive the predictions that become evident when the patterns and anomalies become evident. Repetitive anomalies, ie: failures, move the system from preventive inspection to predictive component change, while operation with minimal failures begins to lengthen preventative inspection time changing labor and scheduled downtime costs, analytical inspections of failure drive component change, or lubricant specification change or perhaps even complete engineering change of machine or process. Using predictive analytics properly drives all costs to their lowest point if done properly, eliminating waste in the process.

Unfortunately, predictive analysis is not infallible. The plan is never adhered to, and unexpected events always occur. The biggest challenge is the weather, which has a considerable influence on product quality and quantity. We need to have a plan B, such as another supplier. This has economic consequences for consumers, as a rare product becomes more expensive.

In seasonal product demand planning, predictive analytics excels by synthesizing diverse data sources—sales history, weather, holidays, promotions, social media, and feedback—for nuanced forecasts. Leveraging machine learning and AI, it adapts, learns from past patterns, and refines predictions in real-time. This empowers food manufacturers to simulate scenarios, assess trade-offs, and make informed decisions, enhancing strategic planning and adaptability.

Mejora la calidad de la PCP ahorrando costos productivos y tiempos muertos. Esto genera una mejor organización para el desarrollo del negocio.

L’analyse prédictive des données prend également en compte,la place qu’occupant la matière première dans la zone,dans le pays et aussi l’engouement au tour de sa culture par les agriculteurs etc..

Certainly, predictive analytics in seasonal demand planning offers substantial benefits for food manufacturers. It minimizes inventory costs and waste through precise supply-demand alignment, enhancing operational efficiency. By ensuring timely and reasonably priced products, it fosters customer satisfaction and loyalty. Moreover, the identification of new trends and tailored offerings can drive revenue growth and establish a competitive edge in the market.

From my understanding and experience we can not just rely on figures or numbers when you are talking about human needs specifically in this circumstances the fluctuations between rare and events in globe is happening in different regions. I tend to think anticipation for short team might be more important than long term planing in this unstable environment throughout the world. Instead of that having several risk mitigation plan will be good enough to encounter the critical situation.

Es muy útil. Cómo desafío principal y el más importante, es que en tiempos modernos y con el nivel de velocidad con el que transcurren las cosas y las modas, cómo ya mencioné, se tornan más impresos los análisis, generando en ocasiones posibles fallas en los cálculos de un pronóstico.

To stay at the forefront of the food industry, consistently upgrade your skills through hands-on experiences, experiment with new ingredients, and cultivate a mindset of curiosity to foster creativity and adaptability.

El desarrollo de nuevas tecnologías en alimentos, genera un abanico gigante de nuevas oportunidades. Esto sirve en gran medida para comenzar a entender cuáles son las costumbres y gustos de la sociedad (siempre tan variables y a altísima velocidad), cómo así también para comprender que la comida está relacionada con la salud y se debe mejorar en todos los aspectos del desarrollo y elaboración de las mismas para poder comenzar a entender que la salud es importante. También como apartado extra, podemos comprender que tenemos que comenzar a alimentar al mundo que es pobre.