In many industries, effective contract management between a supplier and a customer is crucial for businesses to ensure smooth operations and maximize profitability. Contracts govern various aspects of business relationships including pricing, deliverables, volume, and timelines. These contracts typically span one to several years, depending on the nature of the industry and the needs of the parties involved. You often see this in your daily life when dealing with your utility contracts. In the industry, this contract-driven market dynamic prevails for specialty chemicals, pharmaceutical components, sophisticated semiconductors, machinery components, and even non-basic consumer goods. To optimize contract management, suppliers must integrate demand planning and forecasting into their execution mechanism. In this blog post, we will explore how statistical and advanced forecasting techniques can help to facilitate contract management on the supplier side and discuss key considerations for businesses to streamline these processes.
Unlike pricing for commodity materials, which are usually sold and purchased on spot market, pricing for more complex special materials involves long-term contracts with fixed volumes and customized pricing structures. These contracts work as so-called frame or umbrella orders. The purpose of this “frame order” is to define annual or rarely quarterly volume without specific information about exact order and delivery dates. From a business perspective, these contracts are valuable for both suppliers and buyers because they prevent unexpected price-related fluctuations in demand and guarantee certain volumes. However, for the supply chain, these contracts do not give a detailed outlook into monthly or weekly volumes.
This flow creates a significant challenge for inventory and capacity management. Knowing annual volume gives you limited insights for proper management of your production, stock level, or for example tank capacities. The agreed volume can be claimed by the customer in a single order, equal portions throughout the contract period, or in most cases, following more sophisticated patterns. The driving force behind these patterns varies depending on the industry. It can be seasonal patterns, repetitive patterns connected to production campaigns on the buyer’s side, or the specific behavior of the final customer in the chain. Despite knowing the annual volume, having a good demand forecast is vital for maintaining optimal inventory levels to anticipate peaks in demand without facing excessive operational costs. A statistical forecasting technique usually referred to as “forecasting-up-to-target” or “forecasting-up-to-level” is designed to tackle this issue. The total volume of sales per product-customer combination is predefined, but statistical methods are used to define seasonal and/or other patterns based on historical data which will help to anticipate demand fluctuation during the contracted period. Accurate forecasts will be a basis for accurate management of inventory levels and production and storage capacities on the side of producers and raw material suppliers.
As a third-party forecast provider, EyeOn Planning Services runs all our services on Honeycomb: EyeOn Data Science Platform for advanced forecasting, inventory optimization and scenario analysis. Honeycomb combines state-of-the-art data science capabilities for data preparation, modelling, machine learning and analytics, and visualization with a secure and fully scalable data storage environment. Combining forecasting-up-to-target and conventional statistical methods, our Service owners utilize the Honeycomb platform to deliver you the most accurate monthly or even weekly forecast for your annual contracted volumes. This forecast can be used for accurate inventory and capacity management to ensure greater cost control on your side. As an enhanced alternative, machine learning-powered Driver Based Forecasting can also be provided as a part of our Planning Services. Suitable drivers can be derived from customers’ inventory levels, sales of final products, or price fluctuations on the spot market. Harnessing the power of AI and machine learning can mitigate the risks connected to using statistical forecast on dynamic and price-driven markets.
By implementing robust forecasting practices, leveraging catting-edge solutions like Honeycomb, and maintaining open communication with customers, companies can overcome inventory and capacity-related challenges and optimize their operations. Successful demand planning ensures timely contract fulfillment, customer satisfaction, and long-term profitability in the context of annual contract engagements.