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Using Fuzzy Logic and AI to Improve Supply Chain Efficiency


The integration of fuzzy logic and AI presents significant opportunities for organizations like smart automotive manufacturers to enhance their supply chain management practices.

Supply chain problems permeated the global economy after the pandemic. All types of parts and components were in short supply or took extraordinarily long times to arrive. The automotive manufacturing industry was highly impacted, like many other industries.

Supply chains have always been a complex, uncontrollable factor in whether companies could fulfill promises. And there’s no end in sight, even post-pandemic. A 2022 report from S&P Global found that even governments are taking notice, viewing supply chain resilience as a national security imperative.

Artificial intelligence could help companies make the best of supply chain disruptions by creating better predictions, running scenarios using digital twins, and allowing fast decisions based on real-time data to mitigate ongoing disruptions. One of the ways AI can do this is through a mathematical concept known as fuzzy logic. Here’s what you need to know.

What is fuzzy logic for the supply chain?

In math, fuzzy logic is a framework that deals with reasoning and decision-making in situations where uncertainty, vagueness, and imprecision reign supreme. Since supply chains are highly complex operations that bend to the whims of weather, national disasters, and unexpected hiccups in supply and demand, this type of math is perfect.

It’s an extension of classical (crisp) logic that allows for representing and manipulating subjective and uncertain information. In classic logic, a proposition is one of two things—true or false. But that doesn’t work in areas where many factors work together to get that semiconductor chip you need because, in many real-world scenarios, the boundaries between true and false are not clearly defined, and there exist degrees of truth.

Without getting into the weeds of high-level mathematics, fuzzy logic allows for handling uncertainty, imprecision, and qualitative factors. It provides a powerful tool for modeling and reasoning in situations where precise or binary logic is inadequate. It has found applications in fields ripe with uncertainty, including, of course, supply chain management.

When is fuzzy logic appropriate for supply chain tasks?

Fuzzy logic is appropriate for supply chain tasks when dealing with situations that involve imprecise or uncertain information, subjective assessments, and qualitative factors— so basically everything that causes headaches in the first place. Here are some scenarios where fuzzy logic can be instrumental in supply chain management.

  1. Demand Forecasting: Fuzzy logic is beneficial when demand patterns exhibit vagueness and imprecision. In cases where historical data is limited, customer preferences are subjective, or market conditions are volatile, fuzzy logic models can capture and manage uncertainties effectively. By incorporating linguistic variables and qualitative inputs, fuzzy logic enables more accurate demand forecasts, even when dealing with incomplete or ambiguous information.
  2. Inventory Management: Uncertain demand and supply variability make fuzzy logic an invaluable tool, as anyone waiting for “this season’s hot product” can attest. It allows organizations to factor in qualitative indicators, such as market sentiment, expert opinions, or subjective risk assessments, alongside the usual quantitative data. Fuzzy logic models can adjust inventory policies dynamically—by considering factors like demand volatility, lead time variations, and supply disruptions—to ensure optimal inventory levels and minimize two scenarios that cause severe consequences: stockouts or the opposing nightmare, excess inventory.
  3. Supplier Selection: Fuzzy logic is applicable when evaluating and selecting suppliers based on multiple criteria that cannot be precisely quantified (gut feelings, anyone?). Supplier selection processes often involve subjective assessments, qualitative factors, and diverse considerations such as quality, cost, lead time, and geographical location. Fuzzy logic enables decision-makers to incorporate their expertise and subjective preferences into the evaluation process, providing a more comprehensive and nuanced supplier selection outcome. This blend of human and mathematic decision-making accounts for the complex nature of the supply chain.
  4. Risk Management: Fuzzy logic is suitable for assessing and mitigating supply chain risks that involve, once again, imprecise or uncertain information. By considering multiple risk factors, subjective judgments, and historical performance, fuzzy logic-based models can provide a holistic view of potential risks and their impact on the supply chain. This allows organizations to proactively identify and address risks, improve resilience, and develop effective risk mitigation strategies. The humans involved can avoid black-and-white predictions based on classic logic models to encompass “what if” factors successfully.

It is important to note that fuzzy logic should be applied judiciously, depending on the specific characteristics of the task and the availability of data. In scenarios where data is abundant, precise, and easily quantifiable, traditional deterministic approaches may be more appropriate. However, in situations involving uncertainty, ambiguity, and qualitative factors, fuzzy logic can significantly enhance decision-making capabilities in supply chain management.

When are supply chain tasks inappropriate for fuzzy logic?

Speaking of applying fuzzy logic judiciously, in these scenarios, it may not provide value or make a difference.

  1. Well-Defined and Quantitative Data: Sometimes, things are just too straightforward for fuzzy logic models to make sense. If the data can be accurately quantified and there is little ambiguity or uncertainty, traditional deterministic models and statistical techniques can provide more accurate results.
  2. Data Availability and Quality: Fuzzy logic heavily relies on the availability and quality of data, particularly when dealing with linguistic variables and qualitative inputs. If the necessary data is lacking, incomplete, or of poor quality, companies may not get quality results. Decisions made from these insights would only make supply chain challenges worse.
  3. Time and Computational Constraints: Fuzzy logic models can be computationally intensive, especially when dealing with complex supply chain systems or large datasets. If the task requires real-time or near-real-time decision-making, and the computational resources or time constraints do not allow for complex fuzzy logic calculations, companies may want to leverage alternative methods with faster processing times.
  4. Well-Structured and Deterministic Processes: Again, some supply chain problems just aren’t that complex or vague. If the task at hand does not exhibit significant uncertainty, ambiguity, or qualitative factors, fuzzy logic isn’t going to magically add substantial value compared to more deterministic approaches.
  5. Lack of Expertise or Understanding: Fuzzy logic requires a certain level of expertise and understanding to develop and interpret the models effectively. If the organization lacks the necessary knowledge or resources to implement and utilize fuzzy logic appropriately, it may not be the most suitable approach. In such cases, alternative methods that align with the organization’s expertise and capabilities should be considered.

Fuzzy logic could provide ways to tame supply chain woes

The integration of fuzzy logic and AI presents significant opportunities for organizations like smart automotive manufacturers to enhance their supply chain management practices. Models that account for the type of vagueness and uncertainty could reduce the impact of surprises as world events and continued pandemic repercussions continue to make supply chains a beast.

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Elizabeth Wallace

About Elizabeth Wallace

Elizabeth Wallace is a Nashville-based freelance writer with a soft spot for data science and AI and a background in linguistics. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain - clearly - what it is they do.

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