Unlocking Buildable Schedules: The Case for Smarter Order Allocation
In today’s fast-paced manufacturing and supply chain landscape, achieving operational efficiency extends well beyond raw materials and manpower. It begins with one crucial foundation: buildable schedules. However, to reach this point, businesses must first address one of the most complex challenges in planning: order allocation.
The Hidden Complexity of Order Allocation
When we think about buildable schedules, a variety of business rules must be considered during the planning process: order allocation, time bin-based criteria, subject to inventory policies, supplier feature limits, regional distribution, retailer fairness, feature leveling, and operational efficiency constraints. Let’s consider an example of a global automotive manufacturer producing electric vehicles (EVs) across multiple plants worldwide, with each plant sourcing different components.
Now, let’s envision a scenario where there is a massive demand for a specific EV model in North America due to new green incentives, while a supplier in Europe is experiencing battery cell shortages. The allocation of orders, or any combination of features to multiple sources, is subject to thousands of constraints and is very complex. The allocation solution directly affects the quality of the schedule that can be generated: a low-quality allocation solution will adversely impact the scheduling solution. Allocation requirements can arise at different business process levels, each influencing the buildability of the plan and necessitating rapid reallocation capabilities during times of disruption.
Therefore, for the automotive manufacturer to create an effective schedule across the network, they must allocate the right orders to the appropriate plants while balancing battery inventory constraints and retailer fairness policies. Additionally, they need to consider distributing high-value configurations evenly across production, maintaining operational efficiency, and adhering to supplier feature limitations, which means certain elements are restricted to specific regions, adding to the complexity.
Where Traditional Platforms Fall Short
So, why do these problems persist? The answer lies in the limitations of conventional supply chain planning platforms.
Conventional platforms work with predefined modules that have limited configuration capabilities. They are not designed to address the complexity of allocation problems. Allocation requirements can arise in various business processes and necessitate flexibility to address these successfully. Providing the necessary flexibility for any application point in a company’s business process has proven to be a significant challenge.
More importantly, optimization technology is required to achieve the best balance among the many trade-offs. Different vendors adopt varying approaches, and most have avoided optimization with in-depth modeling due to the complexity threshold. Therefore, in this complicated environment, traditional planning modules fall short. They cannot model all these layers of constraints, nor adapt quickly when disruptions occur.
However, here are the benefits of getting allocation right. The value lies in buildable plans, which result in reliability and, thereby, customer satisfaction (reliable ETAs) and supply chain stability (reducing the need for large inventory levels and, consequently, carrying costs). High-quality allocation output becomes a high-quality input for detailed scheduling, as constraints have been addressed at a higher level. This, in turn, reduces violations during scheduling and increases operational efficiencies (e.g., it avoids invalid back-to-back combinations of features). The alignment of objectives across different business processes unlocks significant value.
A New Approach to Allocation: Flexibility Meets Optimization
If you’re wondering how we solved it, we approached the problem differently, starting with the flexibility needed to meet real-world complexity. The design of the data model targets extreme flexibility in addressing a wide range of complex requirements. To solve intricate allocation problems, the devil is in the details.
The data models utilized for scheduling and solving allocation problems rely on the same core competencies focused on our ability to present a wide variety of business rules or constraints to the solver through the depth of our constraint configuration, which is easily accessible to the user. As a result, the software is future-proof. Solving the allocation challenge is not easy, but it directly enhances productivity and efficiency across the supply chain.
Remember the automotive manufacturing example earlier? If the manufacturer begins using a flexible, optimization-driven allocation approach, it can dynamically reassign orders, prioritize shipments based on service levels, and provide high-quality inputs into its scheduling engine. Consequently, buildable plans are generated that improve reliability, reduce costly inventory buildup, and maintain production flow across regions.