Supply chain resilience refers to a company’s ability to adapt and recover from unforeseen disruptions in its supply chain. This is a necessity today owing to the growing complexity of global supply chains and the increased frequency of disruptions such as natural disasters, political instability, and an anomaly like the COVID-19 pandemic.
As it stands, machine learning (ML) can play a crucial role in improving supply chain resilience by ensuring real-time visibility into the supply chain, identifying potential disruptions before they occur, and optimizing decision-making in response to disruptions. This can lead to speedier recovery times, reduced costs, and increased customer satisfaction.
But before we dive into the exact role ML plays, let’s understand what constitutes a resilient supply chain.
Key Components of Supply Chain Resilience
- Risk Management: Identification of potential risks and developing mitigation plans to avoid or minimize their impact.
- Visibility: Real-time visibility into supply chain operations, including inventory levels, supplier performance, and logistics, to identify and address potential issues.
- Collaboration: Strong collaboration between suppliers, partners, and customers to share information, identify risks, and coordinate response efforts.
- Information Technology: Leveraging technologies such as IoT, Big Data, and AI to optimize supply chain operations and enhance visibility.
- Continuity Planning: Developing and testing contingency plans to ensure operational continuity in the event of a disruption.
What Factors Affect Supply Chain Resilience?
As mentioned above, supplier disruptions, natural disasters, geopolitical instability, transportation delays, demand fluctuations, and cyber threats — all these contribute to supply chain disruptions. For example, Statista outlines that 41% of automotive and transportation supply chain leaders experienced losses of $50-100 million in 2020 due to the COVID-19 pandemic.
To build a resilient supply chain, businesses must identify potential risks and create strategies to mitigate their impact. Leveraging technology such as machine learning can enable businesses to better anticipate and respond to disruptions, ultimately improving supply chain resilience.
How Does ML Improve Supply Chain Resilience?
Identifying and Mitigating Risks
One of the primary ways that ML improves supply chain resilience is by identifying potential risks in real-time. In essence, ML algorithms can analyze vast amounts of data from various sources. This can include social media, news feeds, weather reports, and supplier performance data, to identify potential risks and disruptions.
For example, ML algorithms can monitor news feeds and social media to identify any signs of political instability or protests. Likewise, if a supplier consistently delivers late or has quality issues, the ML algorithm can flag this as a potential risk and suggest alternative suppliers or backup plans.
Once potential risks are identified, ML can also help mitigate the impact of these risks on the supply chain. ML models can analyze historical data to identify the most effective response strategies to specific disruptions. This can help supply chain managers develop contingency plans that are tailored to specific risks.
Demand Forecasting and Inventory Management
ML algorithms can analyze data associated with sales, customer behavior, market trends, and external factors, such as weather, to predict future demand more accurately. This can help supply chain managers optimize inventory levels, reduce excess inventory, and ensure that they have adequate inventory at the right time to meet customer demand.
Some of the most prominent algorithms that can help with forecasting include:
- Autoregressive Integrated Moving Average (ARIMA)
- Temporal Fusion Transformer
- Long Short-Term Memory (LSTM)
- Autoregressive (AR) models
By improving demand forecasting and inventory management, ML algorithms can also help supply chain managers mitigate the impact of disruptions. For example, in an event like the COVID-19 pandemic, ML algorithms can help realize better stock management strategies by anticipating the effect of the pandemic on inventory levels and demand, recommending alternative suppliers, expediting shipments, and more.
Logistics and Transportation
Machine learning models can evaluate data related to traffic patterns, weather, and driver behavior, to optimize transportation routes and schedules, reduce transit times, and improve delivery accuracy. By using ML algorithms to optimize logistics and transportation, supply chain managers can improve efficiency, reduce costs, and improve customer satisfaction.
ML algorithms can also help supply chain managers identify potential disruptions in transportation, such as traffic jams or severe weather, and adjust their transportation schedules accordingly. This can help minimize the impact of disruptions on the supply chain and ensure that products are delivered to customers on time. More importantly, this can help keep the drivers informed, thereby reducing the risk of accidents in dangerous road conditions, and improving the overall safety of drivers.
Not to forget the benefits that are accrued from making data-driven decisions for logistics and transportation investments. Supply chain decision-makers can, as such, be better equipped to predict resource and capacity needs and sustainability initiatives, thereby saving costs and resources.
Apart from these, ML models can also serve the following use cases to keep the supply chain resilient:
- Optimizing the last-mile delivery by analyzing the data created across all touchpoints and discerning patterns that can be used to reduce the time taken to ship and deliver goods
- Ensuring robust supply chain security by identifying anomalies and fraudulent transactions via automated process monitoring
Realizing the ML Vision in Supply Chain
Integrating ML in supply chain management can be complex. To that end, there are several key considerations that businesses should keep in mind to ensure success:
Data Quality
Machine learning algorithms rely heavily on data. To ensure accurate predictions and insights, it is critical to have high-quality data that is clean, consistent, and up-to-date.
Infrastructure and Technology
Implementing ML models for supply chain management requires a robust infrastructure and access to advanced technologies. We’re talking about an infrastructure that can accommodate seamless cloud computing, big data platforms, and machine learning frameworks.
Integration with Existing Systems
Of course, there’s no point in integrating ML models into supply chain management if existing systems aren’t compatible. Preferably, ML models should be able to seamlessly connect with the existing systems and processes to ensure that all relevant data is available when needed.
Explainability and Transparency
Machine learning algorithms can sometimes be difficult to interpret, leading to questions about their accuracy and fairness. To build trust and confidence in the technology, it is important that supply chain managers get the opportunity to understand how the algorithms work and the assumptions they are based on. This entails bringing on board skilled data stewards to interpret the algorithms and help with decision-making.
Regulatory Compliance
Different countries may have different regulations governing data privacy and data management. This could influence the type of data that can be collected, shared, and analyzed, something that should be addressed and planned for at the early stages of the integration and planning process.
Continuous Improvement
It’s critical that ML models are constantly monitored and improved to ensure accuracy and relevance over time.
So, there you have it! Machine learning models are powerful tools that can help businesses build a more resilient supply chain and improve their bottom line. Contact us to learn more about how you can leverage these algorithms for your benefit.