The power of making data-driven decisions to solve complex supply chain problems

With uncertainty around every corner, our world needs supply chains that are resilient, sustainable and efficient. However, most supply chains are reactive, meaning that they lose value to uncertainty. Using simulation and scenario exploration to make data-driven decisions, leaders can be more proactive, future-proof their networks and gain competitive advantage.

  • By Phil Pettit
  • 3 min read

The availability of mass data has prompted supply chain and logistics leaders to adopt new approaches to problem solving. However, these efforts have mostly remained deterministic and linear, making supply chains brittle and more sensitive to error and disruption. This often results in higher costs, brand reputation damage and business risk due to fragility from recurring disruption and ineffective resolutions. Building resilient and sustainable supply chains means being able to mitigate the impacts of uncertainties and disruptions proactively without constant firefighting. Consequently, containing customer impact and preventing recurrence in the face of uncertainties are crucial for avoiding value leakage and driving enhanced CX.

Read on to learn more about how data science, simulation and scenario exploration can help supply chain and logistics leaders make data-driven decisions and solve their most complex challenges in a risk-free, optimised way;

The value of data science for solving complex supply chain challenges

  • Identifying optimisation opportunities: The first and most critical step in effective problem solving is validating customer challenges. With scenario exploration, supply chain leaders can quickly uncover fragile points in their network design. Taking a scientific approach, whereby discovery is prioritised and problem statements are verified, provides a probabilistic view of defect rates, scope and trends, clearly highlighting the challenges that need to be addressed, as well as opportunities for innovation.
  • Assignable and Systemic root cause(s) determination: After verifying the problem statement, the next critical step shapes the nature of permanent corrective actions, instilling customer confidence and ensuring process accuracy. Scenario exploration makes it possible to identify and explore all potential problem causes while thoroughly examining correlations between metrics. This ultimately helps build models that reveal any underlying issues effectively, and suggest optimal transformations in a risk-free, virtual environment.
  • Verified and robust corrective action: To ensure effective problem solving, any corrective actions must be efficient, resilient and sustainable. These actions should resolve the issue identified in the first step, as customers expect lasting solutions. Any recurrence of the problem can damage customer confidence and loyalty. By leveraging data science, leaders can demonstrate the robustness of these actions through simulation and scenario exploration before real-world implementation, ensuring cost-effectiveness. This analytical approach can demonstrate reliability growth as the implemented solution matures, ensuring ongoing improvements and stability.

How scientific based problem solving approaches can manage uncertainty in network design

Supply chain and logistics organisations often operate within a complex web of interconnected networks, generating an overwhelming amount of data that spans products, processes, systems and services.

This vast data landscape can be challenging to manage, but network science and scenario exploration can help uncover hidden drivers of KPIs. By analysing historical data, we build models that reveal different scenarios which offer a comprehensive view of network behaviours. These models provide statistical insights into potential risks and opportunities that traditional methods overlook, empowering leaders to anticipate and mitigate disruptions proactively.

Helping supply chain leaders solve their complex challenges with data-driven decisions

We partner with some of the world’s most complex supply chain and logistics networks to drive targeted improvements for the resilience, efficiency and sustainability of their networks. Using scenario exploration and simulation, we deliver strategic insights which enable our clients to optimise and de-risk transformations.

Whether you are looking to explore opportunities for optimisation, or need to better manage uncertainty, our models provide actionable insights that empower data driven decision-making. This proactive approach helps to optimise operational efficiency across networks, resulting in reduced cost to serve, minimised brand damage and reduced value leakage associated with unresolved problems.

If you are a supply chain leader interested in finding out how our technology can help you transform your business potential with resilient, sustainable and efficient supply chains, email us at info@hackandcraft.com today to speak to one of our experts.

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