What is Logistics Forecasting?
Logistics demand forecasting is the process of accurately anticipating the demand for products, services, and shipments throughout the supply chain.
This takes into account even the most uncontrollable conditions or circumstances. To accomplish this, it would be ideal for manufacturers to implement a forecasting model to predict capacity demand, relying on a combination of their own historical data and multiple external variables. The best model is one that allows for the automatic adjustment of forecasts in order to include new customers or other changes in business and increase accuracy.
Why is Logistics Forecasting a Complex Process?
Of all the stages of the supply chain, logistics often gets the rougher deal as it appears to be a combination of the mostly unpredictable unknowns such as varying weather patterns, erratic fuel costs and the skyrocketing costs associated with last-mile delivery in recent years. This makes it even more complicated and therefore is also quite often overlooked when it comes to applying learnings from demand forecasting.
Demand forecasting at the basic level looks at historical customer demand data, applying it to certain predictions for future buying patterns, and generating a forecast of how much product you need to have available at a given time and place. When applied to logistics, demand forecasting can help plan for the seemingly unexpected scenarios, cut costs, and streamline existing and future manufacturing logistics planning efforts including load distribution, flexibility in case of disruptions, seasonality, inventory costs, and inbound logistics planning.
Are you Forecasting Logistics Accurately?
For many logistics companies, the road to digital transformation and AI implementation is not an easy one. Making a shift from manual tasks to automation using modern software and tools can seem challenging and even overwhelming. What most organizations don’t realize, however, is that they are creating an even bigger challenge for themselves by not introducing some of this cutting-edge technology into their operations.
Here are some of the proven ways to ensure your logistics demand forecasting is a success and accurate:
1. Seasonal Planning–
Seasonal demand is a trend that tends to stay consistent from year to year. Logistics demand that depends on seasonal products can ensure planning for fluctuating seasonal demand shifts. This can create an accurate and specific forecast to ensure distribution on time before that season starts.
2. Buyer Trend Analysis–
Market demand can vary depending on what is needed by consumers at that point in time and this happens without prior warning. Buyer trends are difficult to track and to meet the wavering demands of buyers, your logistics function needs to be flexible enough to cater to buyer-centric supply needs. Being aware of these buyer trends can save manufacturers from having static inventory and keep the logistics operating costs to the minimum. Fortunately, reaching the appropriate level of inventory is possible with logistics.
3. Demand Exception Management-
Demand forecasting is needed especially since trends fluctuate every month. Logistics should be prepared to cater to exceptions in demand shifts and having an effective demand exception management strategy can help manufacturers react when an expected change in demand doesn’t occur or vice versa.
4. Intuitive Planning-
Many times intuition works well in forecasting based on past experience and market knowledge. Many experienced logistics and seasoned supply chain professionals can find potential shifts in consumer demand just by feeling them out. This is called intuitive planning. Also, intuitive planning works as it can consider several factors that your SCMS cant track. Despite not being completely technical in nature, it can prove critical in certain industries and market conditions.
Benefits of Logistics Forecasting:
1. Reduced Operations Costs
In the absence of strong forecasting models, logistics companies can end up with reduced profit margins due to unnecessary costs. Logistics fleets which operate at half or lower capacity, coupled with inefficient operations and maintenance can end up costing logistics companies more than they realize. With a robust logistics demand forecasting model that induces actions based on data-driven analytics, can help optimize operations and slash down costs across the board.These solutions help with the reduction of fleet sizes, leasing costs, maintenance costs, storage costs, parking and driver costs.
2. Dynamic Pricing
A good dynamic pricing can help with stable ROI based on the current supply, demand and market status. Thos logistics companies who have control of their own data can successfully understand trends such as seasonality, weekly and monthly fluctuations and reflect them in price changes almost daily. This can result in benefits for the carrier who fills his trucks with freight, and good for the shipper who saves money. Finally, logistics companies can effectively sell their goods and services for the right price at the right time. A sound demand forecasting, combined with a solid understanding of capacity and inventory, allows companies to better scope prices and where they should be set.
3. Increased Employee Efficiency
With the right use of data analytics, employees can perform better, more accurately and timely than before. If logistics planners have all insights and timely data on hand, they can dedicate their productive hours on critical operations rather than wasting efforts on calculating and predicting where they need to locate their assets and check how full they are. A higher reliance on advanced technologies to make data-driven decisions can make employment much more efficient.
4. Improved Resource Planning and Scalability
By having accurate demand forecasting techniques in place, scalability can be achieved with sufficient flexibility to scale up or down without wasting time and effort. Operations resources can be adequately planned to meet demand and supply gaps when required. Manufacturers have readily available data to drive decisions on resource planning and expand or reduce capacity easily. Having a robust Logistics Management Software that is proficient in forecasting is the ideal choice for most organizations. With accurate forecasting, organizations can procure what is actually required and hence eliminate the possibilities of over or under stocking inventory. Optimal forecasting efficiency improves customer satisfaction and eliminates bottlenecks in the operations cycle.