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• The reliability of equipment working under challenging conditions


• Keeping track of the location and amount of stock in the forest


• Forest roads are often unsealed, single- track and slow to traverse, making transit


times unreliable


In essence, wood, being a natural product, is variable, and production and transport occur in physically difficult conditions over a wide geographical area.


The planning and scheduling task is a multi-faceted optimisation challenge that includes the following sub-problems:


• Sequencing at load and unload points without exceeding the capacity of the


loading and unloading equipment.


• Allocating and sequencing loads onto vehicles, subject to compatibility


constraints, delivery windows and driver fatigue management constraints.


• Determining product flows in cases where one product may go from the same source


to multiple destinations or from various sources to the same destination.


Each of these is a complex optimisation problem and, combined, they form a unique challenge requiring an integrated optimisation solution.


OPTIMISATION IN THE TIMBER TRADE: ARAUCO CHILE


The ARAUCO project essentially upgrades a first-generation optimisation application. ARAUCO is a global manufacturer of forest products, serving customers with excellence in the manufacturing and distribution of wood products. With roots in sustainable forestry and innovative product development, ARAUCO brings more than a half-century of manufacturing and supply chain excellence to the market.


The project has benefitted immensely from ARAUCO’s experience and the infrastructure put in place to support the previous application. However, the new challenge was to provide a much richer solution, with more detailed scheduling and consideration of many more critical constraints, such as driver breaks, loading times and queuing. Another aspect of the project was to improve scalability so that ARAUCO could optimise its entire operation in one go, rather than splitting it up into parts. Solving the combined problem opened up new opportunities for better co-ordination and efficiencies. The key objectives are:


• Utilise the available drivers, equipment and vehicles as efficiently as possible


• Create detailed schedules that avoid queueing and consider driver breaks


• Create practical schedules that have a high degree of adherence


Tactical planning and scheduling is now in daily operation, and the priority is to embed the system into business as usual, incrementally improving the solution and consolidating the expected productivity benefits. Further applications in the project plan include real-time dynamic optimisation and long-term planning.


THE TIME HORIZONS FROM LONG-TERM PLANNING TO MINUTE-TO-MINUTE OPTIMISATION


Supply chain optimisation typically operates over different time horizons:


• Enterprise Resource Planning (ERP) or Sales and Operating Plan (S&OP) driven by sales


forecasts


• Tactical planning over periods from weeks to months


• Detailed scheduling, day ahead to week ahead


• Rescheduling


Over time, the problem changes. Long- term planning is more about estimating the resources, in terms of material, people and assets, that would be required to meet the sales forecast. If the forecast exceeds the available resources, we have a supply problem and need to find additional resources. Otherwise, we will have a problem with demand and will look for more customers. Tactical planning is more about allocating the available resources. Scheduling is a much more difficult challenge. Detailed schedules are required to co-ordinate upstream and downstream resources and avoid conflicts and congestion. Rescheduling requires detailed status information as a starting point to go forward. It has complex logic to express what can be changed (for example, a future pickup) and what must be left alone (a delivery from a loaded truck en route).


INTEGRATION OF OPTIMISATION We were fortunate that ARAUCO had optimisation experience and mature business processes to support it. Customers new to optimisation need to think about change management. Moving to more automated planning and scheduling requires accurate, complete data to work correctly. Humans are very good at filling in gaps, interpreting data and memorising rules and constraints. For success, it is essential to:


• Agree on the interpretation of the rules, regulations and other constraints.


• Build and maintain a fleet and driver information database and other sources of


truth that change infrequently.


• Ensure that customer orders have sufficient, accurate data


• Provide the ability to track the current situation, such as order status and


inventory


It is also essential that users can build their solution and compare it with the optimiser so that they develop faith in the solution.


THE BUSINESS BENEFITS Productivity, efficiency, or cost reduction has typically motivated optimisation. Increasing asset utilisation, trucks, drivers and cranes, and reducing empty running and waiting times result from better scheduling and co-ordination. Practical schedules that reflect reality and can be adhered to are also essential. However, the benefits of customer service and safety are equally important. Service levels and safety rules can be coded as hard constraints, never to be violated. If there is discretion, we can use soft limitations and warnings. Either way, customer service and safety are kept front and centre in the scheduling task and never forgotten. Actual examples of benefits for more complex, dynamic operations include:


• 10% increase in productivity for the retail supply chain


• 20% reduction in customer complaints for courier delivery


• 10% increase in productivity for advanced manufacturing


We don’t expect forest products to be that different.


FUTURE PERSPECTIVES


Re-optimisation, whereby the frequency is more often than once a day, is just the start. Our experience of dispatch, where the optimiser runs every minute or so, suggests that additional benefits are possible by using additional data streams. This development has the potential for automated operation without a planner or allocator. The system directs the operation.


Data fusion will enable more accurate schedules with up-to-date information on stock levels, harvesting rates, vehicle location and queues.


The last piece of the jigsaw is machine learning, where equipment and human performance in the field, such as loading and unloading and queueing behaviour, can be analysed, modelled and predicted to enhance schedule adherence even further.


CONCLUSIONS


Forest product supply chains have unique characteristics but many things in common with those in retail and advanced manufacturing – complexity and variability present challenges and opportunities for improvements in productivity, customer service and compliance. New optimisation technology based on AI is proving to be up to this challenge and delivers accurate, detailed schedules with high adherence. The ARAUCO project is a case in point. ■


www.ttjonline.com | July/August 2024 | TTJ


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