#quantum Specifically relevant for a refractory manufacturer's international freight transport. ### Objective Function The aim is to minimise the total cost of freight transport operations while ensuring on-time delivery. This could involve both direct costs such as fuel, personnel, and maintenance, and indirect costs like those associated with delays or disruptions. ### Key Variables 1. **Carrier Selection:** Which carriers are assigned to which routes based on cost, capacity, and reliability. 2. **Route Selection:** The path taken from origin to destination. 3. **Load Planning:** How freight is allocated across different routes and carriers. 4. **Schedule:** The timing of freight pickups and deliveries. ### Key Constraints - **Capacity:** Each carrier has a maximum amount of freight it can handle. - **Time:** There may be deadlines for freight pickup and delivery. - **Route Constraints:** There may be restrictions on routes due to infrastructure or regulatory issues. - **Cost Constraints:** There is likely a budget limit for the overall freight transport operation. - **Personnel Related**: There may be restrictions on working hours and regulatory stipulations leading to certain conditions that need to be fulfilled to ensure feasibility. ### Existing Solutions: Examples: - Enterprise Resource Planning (ERP) Systems (SAP, Oracle's Transport Management System, Microsoft Dynamics) - Supply Chain Management Software ( Infor, JDA, Kinaxis) - Transportation Management Systems (TMS) (C.H. Robinson's Navisphere or MercuryGate) - Inventory Optimisation Tools (EazyStock and Slimstock) **Pros:** - Offer **comprehensive features** to optimise carrier selection, route planning, and load planning. - They incorporate real-time data and analytics, allowing for **dynamic optimisation.** - These systems are typically highly scalable and can be integrated with other enterprise systems. **Cons:** - Existing solutions might **not be fully customisable to the specific needs of a company** that has specific requirements based on the nature of their goods and their extensive global operations. - There can be **high upfront and ongoing costs** associated with these systems. - The effectiveness of these solutions largely depends on the **quality and timeliness of the input** data, which can be a challenge. ### The Cutting-edge today: Any leading research work on innovation projects in this space or a parallel but relevant one Specifically supply chain optimisation for steel manufacturers / refractors involving trains. - Title and Link - Key point - xx - xxx ### Value Estimation: Defining the value drivers and quantifying the Opportunity or the Pain - **More efficient freight transport** can significantly **reduce operating costs**. - Improved reliability and timeliness of freight deliveries can enhance customer satisfaction and reputation. - More optimal route and carrier selection can help to **minimise environmental impact.** Pain Points: - **Additional Demurrage and Detention Fees** from the shipping companies if the containers are not collected and emptied on time - ### Complexity: What are the complexity drivers? - **Large Problem Scale driven by:** - Continuous Nature of Transport Goods - continuous nature warrants discretisation - Dependencies between containers, personnel, trains, pick up and drop off locations - Time window - Planning ahead for a longer timeline or with smaller fineness of time windows - Contract Obligations - Possibilities are many, feasible ones are few, the ideal ones are fewer - **System-wide optimisation** - incorporating a large no. of pick-up, intermediary handovers and drop off points for containers across the train network, further drives complexities - **Large Search Space of Potentially Good Solutions** - Exploring this efficiently and dynamically, to derive not only optimised but also feasible solutions is challenging. ### Existing Solution Challenges: What sucks today? - Quality and timeliness of data, especially in a complex and dynamic global supply chain. - **Incorporating multiple objectives and constraints** into the optimisation process. - The need for **flexibility and responsiveness to unexpected disruptions** or changes in the business environment. ### Potential Quantum Opportunities: What & Why? - Hybrid Optimisation x Evolutionary Algorithms - Handling multi-objective, dynamic optimisation with a large search space and multiple hard/soft constraints - Hybrid Quantum Machine Learning - Predicting potential discrepancies with domain specific learning to help in making the best planning decisions ### Open Questions: 1. How is this problem different for a refractor/steel manufacturer? 2. How mission critical is it? 3. Given existing innovation in the space is finding more alpha here of interest or it's done for the time being? ### Sense Checks: - Qualitative Questions - Is it a real opportunity? (Significant) - Is it worth pursuing? (Sustainable) - Can we win? (Superior) - What is essential to get right for success here?