In today’s complex global supply chains, it’s crucial for goods and services to move smoothly. However, these supply chains are more complicated and interconnected than ever, making them susceptible to various risks, both from within and outside the organization. Traditional methods, which heavily rely on human decision-making, often struggle to identify, evaluate, and monitor these risks, especially given the fast-paced nature of the business world.
To overcome these challenges, businesses are actively seeking innovative solutions to enhance their risk management strategies. This blog post explores a hybrid model for supply chain risk management, the fusion of advanced Artificial Intelligence (AI) models with SCM Globe supply chain simulations, providing an in-depth look at how this combination can effectively address vulnerabilities in the supply chain. By identifying problem areas, simulating potential scenarios, and suggesting satisfactory action plans, this integration strengthens the resilience of the supply chain. The outcome is a proactive approach that minimizes potential disruptions, prevents costly consequences, and considers an end-to-end perspective in both reactive and preventive measures.
What are the Risks in Supply Chains?
Before delving into the framework of the proposed approach, it’s essential to understand the risks associated with supply chains. Many supply chain risk typologies have been proposed in the literature and in practice. The simplest and most straightforward one we’ve found is the classification of risks between operational risks and disruption risks. Example of operational risks in the supply chain are:
- Fluctuating Consumer Preferences:
Rapid changes in consumer tastes can lead to sudden spikes or dips in demand. Businesses must stay agile to prevent overstocking or understocking, both of which can have detrimental effects.
- Logistics risks:
Delays in shipping, whether due to congestion at ports or unforeseen transportation issues, can disrupt the carefully planned timing of the supply chain. On the other hand, warehouses for instance may face challenges if they reach their storage capacity, leading to congestion, inefficient space utilization, and the need for additional storage facilities, all of which are interrupting the flow of goods.
- Supplier Reliability:
Reliance on a single supplier or a supplier going bankrupt can lead to disruptions in the supply chain. Diversifying suppliers and maintaining strong supplier relationships are crucial. Also, subpar quality of raw materials can lead to production delays, product recalls, or damage to brand reputation.
- ESG Non-Compliance:
Non-compliance with ESG (Environmental, Social, Governance) mandates can lead to legal issues, reputational damage, and loss of customer trust. So even if a company complies, it must also ensure its partners are doing the same. Other than legal issues, a failure for example to adhere to ethical labor practices may result in supply chain disruptions due to protests or consumer backlash.
- Cybersecurity Threats:
In our super-connected world, sophisticated attacks on supply chain systems can lead to unauthorized access, manipulation of data, and even the sabotage of critical processes.
In most cases, the business and supply chain impact associated with disruption risks is much greater than that of the operational risks. Examples of disruption risks are:
- Natural Disasters:
Nature’s unpredictability poses a significant threat. Earthquakes, hurricanes, or floods can cripple manufacturing units or disrupt transportation networks, causing delays and bottlenecks.
- Geopolitical Instability:
Political tensions, trade wars, or sudden policy changes can impact the global movement of goods.
- Economic Downturns:
Economic recessions can significantly impact purchasing power, causing a cascading effect on demand. Supply chains need to be resilient to navigate through these downturns.
Undoubtedly, the list of potential risks in the supply chain is vast and continuously evolving, making it impossible to create an exhaustive catalog. Supply chain unpredictability comes from the multitude of parameters and the dynamic interplay between them, creating ripple effects throughout the entire supply chain. Here, the role of AI becomes evident, offering the ability, when applied properly, to rapidly make sense of vast datasets with diverse forms, and organizing and presenting critical information in a quick and understandable manner so humans can make timely and effective decisions necessary for effective supply chain risk management.
An Example of a Smart Supply Chain
In the subsequent section, we delve into a systematic exploration of how the synergy between AI models and supply chain simulation software, such as SCM Globe, can strengthen companies against unforeseen events. This fusion forms what we term a “Smart Supply Chain.”
The Four Phases of Robust Risk Management
- IDENTIFICATION
In a Smart Supply Chain, advanced and predictive data analytics play a pivotal role in detecting sudden, unexpected changes in supply chain parameters. Additionally, the extraction and analysis of data from diverse external sources, including social media and news, leverage technologies like text mining to identify potential risks. Based on the company’s risk appetite, highlighted risks undergo assessment.
- ASSESSMENT
The assessment phase involves a collaborative effort between AI models and the users which are generally supply chain professionals. Leveraging the insights gained, an important role is played by SCM Globe simulations. Here, the AI model makes necessary adjustments to the supply chain model within the SCM Globe tool. Multiple realistic scenarios are simulated, assessing the impact on the flow of goods and quantifying financial and organizational impacts. This process involves multiobjective optimization, considering factors like cost reduction and supply chain continuity, depending on the company’s priorities.
- MITIGATION
Chosen mitigation plans are then suggested to the user, fostering a collaborative human-AI interaction. The flexibility for human experts to assess results and make adjustments ensures a tailored and effective mitigation plan. Information from this phase is stored for continuous learning.
- MONITORING
Continuous monitoring is integral to the Smart Supply Chain framework. It facilitates ongoing learning for both AI models and the organization. The end-to-end perspective allows the simulation and evaluation of the ripple effect of any risk in any node of the supply chain, capturing the dynamic nature of supply chain risks over time.
Advantages of the Approach
- QUICK UNDERSTANDING: Risks are not only identified but also highlighted, with assessment results presented in a map illustrating the supply chain flow.
- HUMAN-AI INTERACTION: Users actively collaborate with AI models to generate optimal mitigation plans, fostering a synergy that enhances decision-making.
- END-TO-END PERSPECTIVE: The model enables the simulation and evaluation of the ripple effect of any risk across the entire supply chain, providing a holistic view crucial for effective risk management.
Conclusion: Moving forward
To sum up, supply chain risk management’s present shortcomings may be addressed by combining cutting-edge AI models with SCM Globe simulations. Leveraging this potent mix can provide firms a strategic advantage as they battle the ever-increasing complexity and uncertainties. By creating plausible “what-if” scenarios, companies may improve their decision-making procedures and create robust supply chains and backup plans that can respond effectively to the instability of the current market.
See also: “The Transformative Power of AI in Supply Chain Management”, “How IoT, AI, and Blockchain Can Create a Sustainable Supply Chain“, and “IoT: Enabling Sustainable Shipping and Warehousing“