Order Availability, Processing, and Allocation
Rethink Your Order Process. Optimize Operations. Enhance Customer satisfaction.
What is Order Availability, Processing, and Allocation?
Since the time they were introduced into the supply chain industry, the concepts of order availability, promising, and allocation have gone through tremendous changes. The rise of digitization has made it imperative for suppliers to commit to the customer’s order requirements – including order specifics, allocation prerequisites, and requested dates. Businesses need to cater to disparate systems while meeting order and allocation demands, coming from affiliates like Amazon or retailers like Walmart, and still have a single tool for order management. To ensure that your order process delivers on your customer’s expectations, you have to address the following aspects:
Affiliates and retailers
Supply and demands
Process, methods, and strategies
In a supply chain, when a client places a request for a product or a service by placing an order, that order needs to be examined for its availability, then it has to be promised to the client, and finally it has to be scheduled for delivery. Order availability is the process of organizing the way you deliver services to your customers. Essentially, it is being able to commit to the order in real-time and enhance the client’s experience by boosting delivery performance. Order processing involves gathering, packing, transporting, and delivering the product to a shipping carrier. This component is a crucial part of order fulfillment. The order allocation component allows allocating inventory to the clients’ orders. It enables existing stock on hand and new incoming stock (purchase orders) to be chosen and allocated to the client sales orders.
Order availability, processing, and allocation gives organizations a comprehensive and global view of demand and supply. These processes help determine shipment strategies, delivery dates, and the sourcing of the materials needed. By staying up to date with the amount of stored inventory as well as receiving constant updates on orders, the system allows for reliable deliveries at the correct time and place, and it automatically matches the demand with supply. With a master schedule detailing the material and capacity availability as well the shipping nodes, you can provide several functionalities including available-to-promise (ATP), capable-to-promise (CTP), and profitable-to-promise (PTP). This makes it easy to make quick decisions and results in increased client satisfaction and improved relationships.
Accelerated order process time:
Enable suppliers to integrate the order system with that of their affiliates and retailers, hence completing the order process in a short period of time.
Reduce training and maintenance costs by digitizing scheduling and sequencing, enabling the system to do it automatically.
Enable enterprises to pull specific pricing and currency for specific customers directly from the system, ensuring zero data errors unlike outdated manual processes.
Promote standardization, portal harmonization, and an increased ability to do business with the implementation of processes and methods for order management, as well as relevant strategies.
What Intrigo Provides
Integration of the order system with affiliates and retailers
Specific pricing and currency to specific customers
Processes and methods for order management
Implementation of strategies such as GATP, CTP, and allocated ATP
Our Implementation Methodology
Assess current order process
Establish methods and remodel
Intrigo enables a fully digital process for order availability, promising, and allocation. Using strategies such as GATP (Global Available to Promise), CTP (Capable to Promise), and allocated ATP (Available to Promise), we enable end users to get allocations separately from the supply chain allocation of the B2B environment.
What kind of work have we already done?
Tune in to our webinar to get an overview of order promising and the creation of machine learning models to predict late shipments, and how a new data platform called online predictive processing (OLPP) can automate this insight.