2022.10.03
How to Improve Your Logistics Forecasting
Forecasting and staging inventory effectively is critical, making a strong understanding of current and future inventory requirements.
Forecasting and staging inventory effectively is critical, making a strong understanding of current and future inventory requirements. We have already examined how reliable logistics forecasting can be an accelerator of business growth. Let’s explore the common strategies and steps that supply chain managers can use to significantly enhance forecasting speed and accuracy.
Logistics forecasting requires robust computer modeling. To achieve this, it’s important to focus effort on three main areas:
- Process-based forecasting improvements
- Data-based forecasting improvements.
- Technology-based forecasting improvements.
There are many enablers in each of these areas, dependent on your sector, industry, and marketplace. We provide helpful starting points below.
Process-based forecasting improvements
Process-based improvements focus on creating efficient communication channels and information sharing protocols throughout the supply chain to ensure logistics forecasting can take internal and external factors into account.
These factors will feed into a logistics forecasting model to generate scenarios that represent a range of real-world changes and disruptors.
- Integrate with internal teams like sales and marketing, operations, and product development to get early sight of promotional and other activities that could influence demand for particular items.
- Engage with risk management teams to understand planning for black swan events (such as the Suez Canal blockage or the COVID-19 pandemic) and other supply chain disruptors. Design and provide a forecasting framework based on an organization’s supply chain resilience and aligned with risk management priorities.
- Establish what contingencies and fallbacks are already in place with your supply chain partners like suppliers, manufacturers, and transportation providers. Agree on reporting and decision-making criteria with internal and external stakeholders to make logistics forecasts actionable.
- Understand strategic imperatives for adding or changing distribution channels, like e-Commerce, third-party marketplaces, or omnichannel.
- Build reliable information-sharing and reporting channels with supply chain partners for the efficient collection of data. Understand possible supply chain constraints and their impact on forecasting. Ensure you and your stakeholders have the level of visibility necessary to make decisions.
- Prioritize a range of forecasting and modeling scenarios based on promotional activities, operational needs, risk analysis, contingencies, supply chain constraints, and other factors.
Data-based forecasting improvements
Data-based improvements focus on measuring, sharing, and using accurate and timely data to ensure you have high-quality inputs for forecasting, which lead to high-quality outputs.
- Understand and audit existing supply chain monitoring of stock levels, lead times, delivery times, and other areas to ensure you’re accurately measuring the right things.
- Build market research data into demand planning so you have early sight of changing consumer behaviors and preferences, and the impact of those changes on demand for particular products.
- Map historic and seasonal trend data into your forecasting model to allow for predictable changes over time.
- Establish contractual (agreed) and actual (real-world) lead times for ordering, supplying, and manufacturing products with supply chain partners so you understand delays between ordering and receiving products.
Technology-based forecasting improvements
Technology-based improvements focus on the applications and systems you use to generate logistics forecasts. This ensures you have proper integration, centralized data, and actionable scenarios.
- Make use of a centralized platform for gathering, analyzing, rationalizing, and reporting on data from all supply chain partners. This gives you “one source of the truth” that creates robust inputs and outputs for logistics forecasting.
- Integrate with data sources from internal partners like sales and marketing, product development, operations planning, and other areas.
- Integrate with data sources from all external partners across the supply chain, including disparate, siloed, and legacy systems.
- Take advantage of the latest machine learning models to create accurate forecasting algorithms that can be tested and refined against real-world data.
- Create multiple forecasting scenarios and outputs based on operational needs, promotional activities, product development, risk analysis and contingencies, supply chain constraints, and other factors.
- Build reactivity and flexibility into forecasting models to allow rapid recalculation and prediction based on fast-changing real-world data.
- Test and refine forecasting outputs to ensure accurate predictions that stakeholders can act on.
- Use available data to create descriptive, predictive, and prescriptive analytics to refine existing supply chain processes.
Conclusion on business strategy
Business strategy relies on solid forecasts of likely future supply and demand. Good modeling supports stronger, more confident planning and strategy. We describe on three main areas in which you can optimize your supply chain forecasting process, meeting customer needs while mitigating risk and preparing for future demand.
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