Logistics – BSC Global

Decision Making in a World of Uncertainty: Integrated Planning Capabilities to Navigate Tariff & Trade Uncertainty

BSC Global — Tue, 20 May 2025 14:17:00 +0000

President Trump's aggressive tariff policies are reshaping global commodity markets, creating unprecedented volatility for businesses across sectors.

The recent implementation of 25% tariffs on steel and aluminium imports, coupled with the looming threat of similar measures on copper, has triggered immediate price surges and supply chain disruptions that reverberate throughout the economy. This volatility is further amplified by the on-again, off-again nature of trade policy implementation.

Tariffs can be announced, paused, modified, and escalated within days, as demonstrated by the recent 'Reciprocal Tariff Plan,' which saw tariffs on Chinese goods fluctuate from 10% to 125%, while tariffs on other countries, initially escalated, were paused for 90 days, all within a single week. This policy whiplash leaves businesses in a perpetual state of uncertainty, unable to confidently plan or secure stable supply chains.

U.S. copper prices have already experienced significant premiums over global benchmarks, with the COMEX-LME price gap widening to over $1,200/tonne in recent weeks as traders rush to stockpile inventory ahead of potential tariff implementation. For businesses dependent on these metals, this new reality presents a complex strategic challenge.

This volatile landscape necessitates a fundamental shift in how organizations approach planning and decision-making. The once stable trade environments can no longer serve as a reliable basis for effective business planning.

How can organizations commit to a 5-year strategic plan when tariff policies can change overnight?

Why Traditional Planning Approaches Fall Short in the Tariff Era

Traditional planning approaches that primarily extrapolate historical data at an aggregated outcome level are particularly vulnerable in today's trade policy environment. When a 25% tariff can be announced and implemented within weeks, forecasts based on historical data quickly become obsolete.

Quantifying the additional impact at the outcome level is challenging because the financial model does not isolate levers with the necessary granularity to rapidly run scenarios. Fixed budgets and rigid planning cycles leave businesses unable to respond to sudden cost increases or supply chain disruptions. This new reality demands a more dynamic, agile approach to financial planning and analysis, one that can rapidly assess the impact of policy changes across the entire value chain and enable swift, informed decision-making.

Driver-based modelling provides precisely this capability, offering a framework that connects underlying drivers, including macro-economic assumptions, to operational and financial outcomes. While many companies have adopted more agile planning methods, a significant number still depend on traditional approaches. Even those transitioning to modern methods may face challenges in full implementation.

This underscores the need for continuous adaptation in planning processes to maintain competitiveness in a rapidly changing market.

Integrated Driver-Based Modelling

The foundation of a modern planning capability is an integrated driver-based model that facilitates scenario modelling. Driver-based forecasting offers a dynamic and responsive approach by directly linking financial outcomes to key business drivers.

Unlike traditional methods that rely on top-line forecasting and adjustments to historical outcomes, driver-based models focus on specific factors influencing performance. Assumptions made at the driver level are inherently more accurate because they are based on specific, measurable factors that directly influence outcomes. This approach allows businesses to adapt quickly to changes, providing a more accurate and reliable framework for decision-making.

By establishing a direct connection between inputs, drivers, and outputs, driver-based models enable organizations to conduct scenario analyses and adjust forecasts in real-time. This flexibility is crucial in today's volatile economic environment, where businesses must be prepared to respond to sudden shifts in market conditions.

Let’s demonstrate the complexity of managing a business in this environment by way of an example.

Illustrative Example:

A Base Metals Mining Company in a Tariff-Affected Geography

Consider a base metals mining company operating in a region impacted by steel and aluminium tariffs. These tariffs can significantly alter the company's cost base, affecting decisions across various aspects of the business.

Cost Base Impacts:
Tariffs typically lead to a marginal increase in inflation across the economy, indirectly affecting the overall cost base of the operation. Additionally, specific costs for items directly impacted by tariffs, such as steel mill balls used in the milling operations in a mine, will rise. The business will need to consider alternative suppliers and, in some cases, alternative processes where the availability of certain consumables might become limited. When making business decisions, it is essential to have the capability not only to capture the broad increases in costs but also the specific impacts on tariff-affected items. A driver-based model allows for key consumables and cost rates to be adjusted independently. Accurately predicting these cost dynamics and understanding their effects on profitability and competitiveness are crucial for shaping the company's strategic direction.
CAPEX Projects and Expansions:
Projects and expansions that rely heavily on steel and aluminium construction will face increased capital expenditure and must be re-evaluated. The company needs to reassess ongoing and future projects with updated cost bases to determine their feasibility under new conditions and assess their impact on the overall business. Having a tool that easily allows for reassessing the feasibility of the projects, given changes to specific assumptions as well as overlays those projects have on the overall business is critical for ensuring a good Capex strategy.

Production Considerations:
A driver-based model allows the company to explore different production profiles to manage costs while tariffs are high. For instance, if mill ball costs increase significantly, the company might choose to mine softer ore initially to reduce operational costs. If the cost base is not competitive globally, some pits or shafts might not be viable, leading to considerations of putting certain operations into care and maintenance.
Revenue Impact:
Whilst the initial points focus on challenges, the impending copper tariffs also present potential opportunities. As outlined in the introduction, the U.S. is actively considering implementing tariffs on copper imports, which would likely lead to higher domestic prices. This could present a lucrative opportunity for U.S.-based copper producers. In this context, driver-based modelling becomes essential. By incorporating off-take agreements and freight routes into the model, the company can accurately quantify potential upside.

For example, the driver-based model in the example above reveals that while the copper mine will face an increase in operating costs due to steel tariffs, the premium on domestic copper prices would more than offset this challenge, increasing overall profitability. This potential increase in revenue could significantly alter the company's approach to cost management, CAPEX decisions, and production strategies. If revenue rises substantially, the company might consider expansion opportunities. A higher cost base might become palatable, and instead of reducing production, the company might want to increase it. Additionally, the feasibility of CAPEX projects and expansions might improve with increased revenue. Understanding this balance is crucial to deciding whether to pursue an aggressive or conservative strategy. A driver-based model allows for quick adjustments to these assumptions, optimising value within the organization.

Overall Portfolio Considerations:
Whilst the example has been focused on a single asset so far, the complexity escalates significantly for a larger commodities company managing a diverse portfolio of assets, including mines, smelters, and refineries spread across various geographies. The challenge of managing this portfolio is compounded by the need to navigate multiple off-take agreements, tolling arrangements, and tariffs. An integrated driver-based model of the value chain enables efficient management of each asset, as well as material delivery and sourcing strategies across assets, aligning operational decisions with strategic objectives. This approach is essential for optimising value across the organization, rather than just at an individual asset level.

Conclusion:

Driver-Based Modelling as an Essential Tool in Tariff Uncertainty

The importance of a driver-based model lies in its ability to flex different assumptions in a swift and integrated manner across the business. This capability is crucial for balancing the impact of tariffs on input costs with potential benefits on selling prices. Without such a model, managing these complexities becomes extremely challenging, especially for companies with extensive operations and diverse geographical footprints.

As we've seen, the current tariff environment creates both challenges and opportunities for businesses. The winners in this new landscape will be those who can quickly assess the impact of policy changes, model different scenarios, and adapt their strategies accordingly. Driver-based modelling provides exactly this capability, enabling businesses to:

Quantify the direct and indirect impacts of tariffs across the value chain.
Identify which parts of the business are most vulnerable or advantaged.
Test alternative strategies before committing resources.
Communicate the rationale for strategic shifts to stakeholders

As businesses continue to navigate uncertain trade environments, adopting a driver-based forecasting approach is no longer optional, it's essential for survival and success. It empowers organizations to make informed decisions, optimise strategies, and seize opportunities in a rapidly changing world.

Keep an eye out for our next article, where we will delve deeper into integrating sensitivity analysis and stochastic modelling into your planning toolkit to drive better risk management and planning in the face of continued trade policy uncertainty.

The Planning Science division of BSC specialises in helping businesses navigate exactly these types of challenges.

Through our proprietary Qerent Platform, we develop sophisticated driver-based financial models that enable our clients to conduct scenario testing and tailored analysis for strategic planning, capital optimisation, and risk modelling, particularly valuable in today's volatile trade environment.

Our approach has helped numerous commodity producers not only weather tariff-related disruptions but identify hidden opportunities within these challenges.

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Optimizing Copper Smelting with Predictive Simulation

BSC Global — Wed, 13 Nov 2024 12:48:00 +0000

A Case Study with Sinomine Tsumeb Smelter

The journey toward optimizing logistics and production planning for any complex operation involves numerous challenges, particularly when dealing with high-value materials such as copper. At Sinomine Tsumeb Smelter in Namibia, these challenges were compounded by the need to manage both third-party smelting and stringent regulatory timelines for the movement of copper.

To tackle these obstacles, Business Science Corporation (BSC), through its Productivity Science division, collaborated with Sinomine to implement a predictive simulation solution.

This partnership, led by Sean Mowatt, Bryan Moore, and the smelter's metallurgist Dina Tiets, transformed what was once a manual, Excel-driven process into an integrated and dynamic planning tool that has reshaped day-to-day operations.

The Challenge:

Bridging the Gap Between Logistics and Production

Sinomine’s copper smelting process relies heavily on the timely delivery of copper concentrates from various locations to its production facilities.

However, as Bryan Moore explained, the production and logistics departments were previously working in silos, each following separate planning methods. The logistics team focused on getting material to the plant, while production planned its processes assuming the material would be there on time. When delays occurred, production was left scrambling.

As Bryan noted, "it's essential to stay aware of material supply timelines, as they play a crucial role in keeping production running smoothly through the course of the plan and beyond. The simulation tool actually predicts - given the logistics plan - whether production can proceed as expected."

This prediction capability was a major turning point for Sinomine, allowing the smelter to plan holistically across the value chain rather than in isolation.

BSC’s Solution:

Predictive Simulation with WITNESS

The core of the solution was a predictive simulation model developed using WITNESS, a powerful tool for logistics and production planning.

In less than six weeks, BSC developed a model that allowed Sinomine’s logistics and production teams to visualize and simulate the entire copper processing journey - from concentrate arrival at Walvis Bay to stockpiling, smelting, and dispatching the final product.

This simulation model integrated logistics planning with production needs, providing real-time feedback on potential bottlenecks or material shortages before they could disrupt operations. As Dina Tiets described, "Before using the simulation model, we had no real-time overview of concentrate movements. Now, we know exactly when shipments are arriving and can adjust our production plans accordingly."

Key Innovations:

Collaborative Planning and Dynamic Forecasting

One of the standout features of the simulation was its ability to force collaboration between previously siloed departments. "Now," Bryan explained, "planning can go and run their plan, see that there’s a problem, and collaborate with logistics to adjust either the plan or the material delivery schedule." This capability empowered the teams to make informed decisions before issues arose, fostering a new level of operational synergy.

The simulation also allowed for scenario testing, enabling both departments to explore various logistics and production scenarios before committing to a final plan.

Whether it involved adjusting truck and train schedules or planning around maintenance events, the simulation provided the foresight needed to avoid costly mistakes. "It has helped us avoid the risk of furnace stoppages," said Dina, reflecting on how the tool has improved decision-making at critical moments.

The Results:

Reduced Planning Time and Increased Efficiency

The benefits of this predictive simulation were immediately measurable. For Sinomine, the time required for weekly production planning meetings dropped from three hours to just 20 minutes.

The ability to visualize and adjust plans based on real-time data meant that logistics and production schedules were aligned, reducing costly downtime and ensuring a more stable production flow.

One particularly notable outcome was the impact on sulfuric acid production, a byproduct of the smelting process. With the simulation, the smelter could accurately forecast sulfuric acid output and ensure timely delivery to its customers.

As Dina explained, "We model shipments and production together, so we know exactly when we can deliver sulfuric acid without exceeding our capacity or causing production to stop."

The Sinomine team have been using the simulation for more than four years now – a testament to the value they feel it provides them. “Our most successful implementations come down to three, equally important, factors – data, modeling, and people” Sean Mowatt explains. “Regardless of the technical sophistication of the solution, if it is not perceived to be easy to use while delivering real-world value, it will not be used sustainably. The inputs and guidance provided to us by the Sinomine team in setting up and enhancing the solution was, and continues to be, crucial to its longevity.”

Conclusion:

A Future of Smarter, Integrated Planning

The collaboration between BSC’s Productivity Science division and Sinomine Tsumeb Smelter highlights the power of predictive simulation in modern logistics and production planning. By integrating data from both departments and providing a unified platform for scenario analysis, BSC’s solution has helped Sinomine reduce costs, avoid production delays, and improve decision-making across the board.

As industries like mining and smelting continue to evolve, tools like the predictive simulation model implemented at Sinomine will play a crucial role in shaping the future of operational efficiency. Whether it’s reducing downtime, minimizing costs, or fostering greater collaboration between teams, the results speak for themselves: smarter planning leads to better outcomes.

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