What do you do if your inventory forecasting is facing challenges and you need to be more resilient?

Demand forecasting is always a challenge. This is because the market is in a state of flux. Which international event can impact the demand we cannot say. In general, the following are influencing factors that merit study: a) Impact of the growth of the GDP of one's country b) Impact of the growth of the GDP of the country where the company's products are exported c) Impact of one's country's currency against the US$ d) Crude oil prices in the international market e) Major political or other event that may impact the market. While regular forecasting techniques like regression analysis may be used, studying the above factors helps as these could impact the forecasting accuracy.

Begin by thoroughly examining the quality and reliability of your inventory data. Scrutinize historical records, sales trends, and demand patterns to identify any inconsistencies or inaccuracies. Evaluate the effectiveness of data collection methods and ensure that you have access to real-time insights. By conducting a comprehensive assessment of your data, you can uncover potential issues and take corrective measures to enhance the accuracy and reliability of your inventory forecasting processes.

Understanding your historical data properly and applying demand forecasting method and understanding the trends, seasonality is one of the key steps in mitigating your risk completely. Improving your forecast accuracy and gathering insights from the sales team, demand planning team and having a pre planned S&OP meeting is also very useful. Prediction from sales data and applying methods like Exponential smoothing specially when you have time series data or weekly data available, also applying Holt's winter model for model validation and finding the right forecast is another key steps in optimisation of inventory. Having a transparency from end-to-end will also help you to improve your accuracy and reduce losses.

Take a critical look at your current inventory forecasting methods and techniques. Consider whether they are still effective in meeting your evolving business needs and market dynamics. Explore alternative forecasting models and algorithms that may offer better predictive capabilities and adaptability. Experiment with different approaches and strategies to find the ones that yield the most accurate and reliable forecasts. By revising and refining your forecasting methods, you can enhance your resilience to uncertainty and improve the overall effectiveness of your inventory forecasting efforts.

Invest time and effort into building strong and collaborative relationships with your suppliers. Foster open communication channels to exchange vital information such as demand forecasts, production schedules, and inventory levels. Work closely with suppliers to develop flexible agreements and contingency plans that can accommodate fluctuations in demand and supply. By cultivating strong supplier relations, you can establish a more agile and responsive supply chain that is better equipped to handle inventory forecasting challenges and disruptions.

Proper communication with suppliers helps a lot in mitigating the risks of inventory. Also a proper forecast can help you to plan and schedule according as per demand insights and inventory available at your storage.

Apart from the specific actions outlined, consider broader factors that may influence the resilience of your inventory forecasting processes. Stay informed about macroeconomic trends, regulatory changes, and geopolitical events that could impact supply chain dynamics. Foster a culture of innovation and continuous improvement within your organization, encouraging experimentation and agility in response to changing market conditions. Regularly review and refine your inventory forecasting strategies based on performance feedback and lessons learned to strengthen your resilience and adaptability over time.

To enhance resilience in inventory forecasting amidst challenges, employ robust data analytics to detect patterns and anomalies, integrate machine learning algorithms for predictive modeling, diversify data sources for a comprehensive understanding, implement agile inventory management strategies for adaptability, establish contingency plans for sudden disruptions, foster collaboration between departments for holistic insights, and continuously iterate and improve forecasting methodologies based on feedback and real-time data. Embracing technological advancements and fostering a culture of innovation and adaptability will ensure a resilient inventory forecasting system capable of navigating uncertainties effectively.