In my experience, Maximum visibility of Secondary Sales & Customer Stock holding is also helps a lot on predicting Primary Sales, now with capabilities of Machine Learning (ML) is easy to process the huge data with as many as combination.

Alle genannten Punkte sind wichtig, aber ich finde die Beschreibung irreführend. Beim Wort "Bedarfsprognose" würde ich zunächst davon ausgehen, dass es sich um die unbeschränkte Nachfrage handelt, die ich nicht schon durch Machbarkeitsüberlegungen verfälscht habe. Natürlich sollte dieser realistische, aber unbeschränkte Bedarf im Nachgang gegen das realistische Angebot an Material und Kapazität abgeglichen werden.

Aligning demand forecasting with supply chain and inventory management involves integrating forecasting data with supply chain planning systems. This ensures that production, procurement, and distribution activities are synchronized with expected demand levels. By forecasting accurately, businesses can optimize inventory levels, reduce stockouts, and minimize excess inventory. Continuous communication and collaboration between demand planning, supply chain, and inventory management teams are essential to adjust strategies in real-time and adapt to changing market conditions.

To align demand forecasting with supply chain and inventory management, I take a proactive, data-driven approach. Drawing on my expertise in integrated enterprise planning (IBP), S&OP, and S&OE, I start by collaborating closely with sales, marketing, and finance teams to create demand forecasts that reflect historical data and market trends. Leveraging tools like SAP IBP and specific KPIs, I continually monitor forecast performance and adjust plans as needed to keep inventory levels optimized and aligned with customer demand. By integrating these processes and fostering cross-functional communication, I can anticipate demand fluctuations and make agile adjustments across the supply chain ultimately minimizing stockouts and excess inventory.

I believe CPFR here collaborative planning , forecasting and replenishment plays a vital role on having cost efficient and highly effective replenishment system with your distributors , key accounts , chains depending in the industry you are in , Go and talk with planning teams at your partners distributors , or big chains and meet at a point where by using technology or system integration you can have a seamless ordering system , It’s a definite win win collaboration and include this in a addendum or part of the CDA contracts , Safety stock levels and order points per sku specially for class A Products.

Yes, In my experience managing phase in - phase out, seasonal products forecasting for complete season, New launches for first 3-6 months is KEY to optimize the inventory & avoid inventory cost.

Hier würde ich ebenfalls hervorheben, dass es weniger die Prognose ist, die an die Ziele, Kosten, etc. angepasst wird, sondern vielmehr die Strategien und Parameter mit denen man auf die Prognose reagiert, um Ziele wie Lagerkosten, Service Level, etc. zu erreichen. Insofern würde ich die Prognose von den darauffolgenden Anpassungen trennen.

To enhance your demand forecasting strategies, consider integrating real-time data analytics and scenario planning. Real-time analytics can provide immediate insights into changing market conditions, enabling more agile adjustments to forecasts. Engaging in continuous improvement practices by regularly reviewing and adjusting forecasting models and methodologies based on new data and outcomes can also significantly improve forecast accuracy over time.

Dem würde ich zustimmen. Wobei die beste Prognose die ist, die man nicht benötigt. Insbesondere an den Unternehmensgrenzen wird heutzutage noch viel mit Prognosen oder gänzlich ohne Prognosen gearbeitet. Insofern können die Vorteile einer Kollaboration und Informationsteilung über die Partner der gesamten Wertschöpfungskette hinweg, nicht genug betont werden.

Demand management is both an art and a science. Having as much relevant data is just as important as being able to truly understand what the numbers imply. Engage the right stakeholders. Get as much insight as possible - not only about historical performance, but also what the plans moving forward are. Gaining these insights allow the team to anticipate potential opportunities and risks.

A demanda não pode ser estática, contudo deve respeitar o "período de congelamento", ou seja, o intervalo de tempo o qual não se pode alterar os números. Mudanças inesperadas podem ocasionar roturas de stock, se houver incrementos, ou obsolescência, no caso de reduções consideráveis. Quanto ao aumento, o tempo de resposta dos fornecedores deve servir como parâmetro. Se diminuição, a validade dos artigos deve ser avaliada.

Meiner Erfahrung nach ist es wichtig, die Prognosegenauigkeit kontinuierlich und einheitlich zu messen. Häufig wird die Prognose auf einer Ebene maschinell erstellt und auf unterschiedlichen Ebenen (z.B. Land, Region) und Horizonten manuell angepasst. Was zu einer Verzerrung führen kann und die Prognose nicht immer besser macht. Der Wertbeitrag und die Sinnhaftigkeit dieser manuellen Anpassungen sollte ebenfalls gemessen und bewertet werden.

Consistency is key! Forecast accuracy is not a one-time goal, it has to be continuously attained and improved to truly make an impact to the organization. Have a collaborative process in place and ensure that the right stakeholders are engaged at every step. As a team: review the results, perform a root cause analysis, take action, repeat.

Hierfür können Simulationen und Szenarioanalysen hilfreich sein, um verschiedene Marktbedingungen oder Ziele (z.B. Kosten, Service Level) und ihre Auswirkungen auf die Wertschöpfungskette zu testen und zu verstehen.

Once there's a consistent process in place, it's easy to fall into a comfort zone and try to keep everything "as-is" - but it is important to recognize that change is ever present. Stay curious and try to understand how things are done by others - be it competitors or even your peers within the organization. They can provide great insights and even spark ideas on how to better handle challenges that you face.

It is very important to treat Demand Forecasting and Supply Planning as two related but also independent processes. Demand Forecasts should be generated in isolation of the Supply Considerations; and Supply Plans should take Demand Forecasts, and other considerations such as service levels, lead times and order cycles as an input.

The first step in the Supply chain cycle that pulls the trigger is ( demand planning) managing and generating the rolling unconstrained forecast the dream where we want to reach and grow reflected in numbers based on a combination of historical data , team qualitative experience & real time demand data depicting market size and shares the company wants to capture , After setting this plan we move to the constraints whether it’s capacity , lack of resources and how to utilize what’s in hand ( Supply planning ) balancing demand and supply happens where we put our feet on the ground , The interrelation between both is managed through availability , distribution planning and inventory management in all its forms finished , raw or semi finished

Planning - Sourcing -Making - delivery - return . The first step is planning without setting the strategy make to order or make to stock working on rolling forecasts for a year or two to build your capex , investment and procurement strategy, Demand planning is the compass that directs the whole supply chain , It’s the starting point that strengthen and ties the chain how will run mrp without understanding demand patterns on total , brand and sku level how will I build the production plan or negotiate on better prices and potential savings without communicating yearly volumes with suppliers. It’s the building block to check if we need more capacity or less and divest or work with 3rd parties etc

Aligning demand forecasting with supply chain and inventory management involves integrated business planning, data integration, and utilizing accurate demand forecasts as inputs for supply chain operations. By optimizing inventory levels, coordinating lead times, and streamlining transportation logistics, businesses can achieve better operational efficiency and cost optimization. Continuous monitoring, feedback, and the adoption of advanced analytics and automation further enhance the alignment. This iterative process helps businesses respond effectively to market demands, minimize stockouts and excess inventory, and ultimately improve customer satisfaction and maintain a competitive edge.

Meiner Erfahrung nach führt die Kombination von maschinellem Lernen, Simulation und Optimierung im Bereich der Bedarfsprognose zu einer Art 'Decision Intelligence'. Maschinelles Lernen hilft uns, Muster in den Daten zu erkennen und Trends vorherzusagen, während Simulationen verschiedene Szenarien durchspielen. Die Integration von Optimierungsalgorithmen ermöglicht es, die besten Entscheidungen aus verschiedenen Optionen zu treffen, z. B. beim Bestandsmanagement. Dieser synergetische Einsatz von Technologien verbessert nicht nur die Prognosegenauigkeit, sondern ermöglicht es auch, proaktiv und schnell auf die Herausforderungen im Supply Chain Management zu reagieren.