Logistics companies face a market that demands constant improvement — cutting costs, finding shorter routes, and maintaining sufficient inventory. This pressure on the supply chain can be alleviated greatly through the use of demand forecast tools. These can ensure that your company always has the right volume of stock and can meet customer needs promptly.
In today’s article, we’ll cover the intricacies of solutions for logistics forecasting. Our focus will be on showing the value of logistics demand forecasting and the best way to integrate it into your processes. We’ll also discuss the cost of these tools to give you an idea of how viable they are for you. Let’s get into it.
Why Demand Forecasting Is So Important in Logistics
While the latest data shows an uptick in the logistics market, we see that COVID-19 and other global events of recent years have disrupted the entire industry. Companies that didn’t employ forecasting in logistics may now struggle to assess just what their future holds. Experts call forecasting tools “essential for success.” Here’s how they impact logistics.
For one, using a demand forecast allows you to procure exactly what you need and structure your collaboration with your own suppliers. This is thanks to the fact that the tools will allow you to precisely plan when shipments should go out and ensure that you’ll have vehicles available. These points allow you to avoid expenses associated with sudden demand surges or dips.
Another, less discussed point, is the ability to schedule staff shifts, as an accurate demand forecast lets you assign them to tasks proactively. Plus, you will reduce waste, as your stock will never linger in the warehouses too long, always ready for shipment. In short, you’re optimizing your supply chain greatly.
What Integration Looks Like for a Custom Forecasting Tool
To take advantage of qualitative forecasting, you’ll need to insert it into your business operations. Here’s how to do just that.
Data Inputs and Integration Paths for Demand Forecasting in Logistics
In order to ensure your supply planning works well, you’ll need a variety of data types, which you can get from your own business. This includes:
- Order history;
- Shipments;
- Delays;
- Seasonal trends;
- Real-time delivery data.
All of this can be pulled from your ERP and WMS/TMS SaaS platforms, CRM software, or just plain spreadsheets. The key is structuring that data to make it usable, so that you can generate analytical insights. For that, there are three typical integration approaches for your forecasting model.
The first option is to keep the data in your software and process it there, though that requires either already having such capacity or adding it during logistics software development. The latter isn’t possible with all off-the-shelf solutions, though, so be mindful of it when choosing your tools.
The second option is creating middleware or ETL (extract, transform, load) pipelines that feed data into the cleaning process. These can be set up regardless of the tools you pick, but add extra steps to the operations.
Then the third choice is syncing your data with your cloud environment or data lake, if you have those. The advantage here is the easy access and reliable uptime.
The Role of Accurate Data in Accurate Forecasting
It’s not enough to just make a forecast because a wrong prediction can be more damaging than no prediction at all. However, an accurate forecast can be a lifesaver, and it hinges almost entirely on the quality of your data. This means standardizing and cleaning up are the linchpins of your data processing.
Issues like inconsistencies in data formatting can result in some information not being processed, leaving significant blind spots in your predictions. Make sure that the clean-up leaves the data in a unified format with all sources feeding the latest information stably. This ensures that logistics demand predictions will be accurate and reliable.
What Affects the Cost of Development Demand Forecasting Tools
A typical tool that predicts demand trends will also have the typical factors determining its final price:
- Scope and complexity;
- Integration needs;
- Volume of data;
- Visualization features;
- Post-launch updates.
The question of scope is fully up to you, revolving around how broad your forecasting is — from a single warehouse to an entire region. You also determine whether you want it done with a simple or an AI-powered model. Then the matter of integrating it, which becomes more expensive the more systems you have. Simplifying and structuring your environment will lower this part of the cost.
You also need to account for the volumes of data you’ll be processing and the training your model will require to provide a reliable forecasting method. Then, the work will focus on creating a method to visualize the insights generated by this data. Lastly, your logistics tool will require some work after you first launch it, such as feature updates and bug fixes.
Development Costs for Custom Forecasting Tools
The best way to ensure that you can forecast demand precisely for your own conditions and supply chain is to have it tailor-made for you. Some companies avoid this purely due to worries about the cost of custom solutions, but it’s often less than you may think.
Our first example is the MVP of a custom forecasting tool. The final cost of this project should usually range between $20,000 and $40,000. By focusing on the essentials, you get a nice discount on the usual price of a full solution while still receiving the core functionality.
If you’re willing to pay a bit more to get a fully realized product, complete with all the necessary integrations, you may be looking at a bill of $60,000 to $100,000. Certainly a sizable increase, but you also get more in-depth analytics and nice bonuses like automation. Investing in such a tool should pay off in no time, simply by optimizing your supply chain.
You should note that whichever option you choose might also incur the additional costs of maintenance and support, averaging around $1,000 to $3,000 per month. If it does happen, these expenses will contribute to enhancing your solution, adding more features, and reducing bugs. They also serve to keep it compatible with any additions to your ecosystem, guaranteeing its longevity to predict future demand.
Conclusion
As you can see, demand forecast tools are indispensable and more than worth it for companies that want to streamline their supply chain management. We’ve outlined the best way to build your forecasting capabilities into your business processes and the potential cost of this venture. In order to make sure it’s successful and has optimal results, partner up with Lember.
We’ve spent over 11 years helping clients across the world with 150+ successful projects in our portfolio and a high return rate among our customers. Our team guarantees quality and care for your project, so if you want accurate demand forecasts, get in touch today.