Generative AI AI analytics and predictions AI real-time data enrichment Autonomous driving Cyber-risk mitigation
The AI boom is not a hype
Hardly any technology has found so many areas of application in the past year as artificial intelligence. And with interest still growing, the potential of AI is far from achieved. AI offers support to solve problems like big data analysis or image and language recognition that are difficult to handle with traditional technologies. AI offers various opportunities to make supply chain processes more efficient and resilient.
With its ability to recognize patterns in large amounts of data, artificial intelligence can make valuable predictions about demand and utilization, for example, or enable smart automation of workflows with robotics. On top, AI learned to generate various kinds of content. Despite the multitude of promising areas of application, most businesses have not yet implemented AI – so a strategic use of AI can unlock significant cost reduction and efficiency improvements, as well as a competitive advantage.
Arguably one of the most important effects of the rise of applicable generative AI is that it opens the door for the development of artificial general intelligence (AGI), i.e., a machine that can learn to accomplish any intellectual task, or even artificial superintelligence (ASI), a computer that is much more generally intelligent than humans. These kinds of AI would have even further-reaching consequences than the generative AI that exists today.
Generative AI – in focus
Affected industries:
Industries that rely on communication or marketing
Affected supply chain segments:
All supply chain segments
Putting AI on top of priority lists
Generative AI are artificial intelligence programs that can be used to create content like written text, images, audio or computer programs. When combined with a natural language processing (NLP) interface, users can instruct the AI to create content tailored to their specific needs. Generative AI applications like chatbots have recently drawn huge attention across all industries and businesses.
The high automation potential and easy integration into supply chain processes are expected to contribute to this trend’s high impact.
Businesses should be prepared for the first disruptive business models using generative AI. However, especially large language models (LLMs) are still facing trust issues given the inconsistent quality of the results they generate.
Related developments
Facets of this trend
- Chatbots can create marketing material and computer code
- Generative AI facilitates contract management by summarizing contents and creating documents
- Data protection and data sovereignty issues
Getting ahead of the trend
Generative AI has huge potential to facilitate time-intensive creative processes. The technology is developing rapidly and expected to disrupt various workflows in all parts of the industry.
Decision makers now need to identify use cases to benefit from the technology while still ensuring data confidentiality and high quality of results.
AI analytics and predictions – in focus
Affected industries:
All industries
Affected supply chain segments:
All segments
Leveraging new optimization potential
AI analytics and predictions can be used where traditional calculation methods reach their limits. Methods like machine learning and deep learning can discover patterns and correlations in large data sets that are too complex for humans to see. AI-based forecasting or big data analytics can unlock new potential for optimization.
Related developments
Facets of this trend
- ETA predictions
- Risk prediction and assessment
- Demand forecasting
- Dynamic pricing
Getting ahead of the trend
AI analytics and predictions have already proven their potential to enhance supply chain optimization processes.
Businesses that have data science competence should evaluate where these methods can be used to improve their results or unlock new use cases.
AI real-time data enrichment – in focus
Affected industries:
All industries
Affected supply chain segments:
All segments, especially warehousing
Processing data on the fly
AI real-time data enrichment refers to algorithms that process data directly after it is created. It depends on accurate and ubiquitous sensors whose information can be processed to enable predictive maintenance and identify defects in machines and vehicles. Video data can be used for augmented reality and computer vision processes such as truck driver fatigue identification, quality control and damage detection or warehouse safety surveillance.
In terms of this trend’s timeliness, the technical components have been available for several years, but there are still not many success stories that integrate sensors, data and processing. Besides the maturity of the technology, trust issues are additional hurdles to the implementation of this trend in the near future.
Related developments
Facets of this trend
- Computer vision (e.g., for warehouse safety, process optimization or access control)
- Identification of defects and predictive maintenance
- Vision picking
Getting ahead of the trend
Real-time data enrichment offers many possibilities to create value in logistics processes.
Especially computer vision promises to unlock improvements regarding process accuracy, safety and optimization.
Businesses should evaluate which application yields the highest benefits and which regulatory and trust hurdles they need to consider.
Autonomous driving – in focus
Affected industries:
Especially industries that rely on transportation
Affected supply chain segments:
Road transportation and warehousing
On the long road to productivity
AI makes way for autonomous robots and vehicles that can be used in manufacturing and freight transportation. The technology has immense potential to reduce costs while increasing productivity and safety. However, there are still regulatory and liability issues to overcome before autonomous driving will be present on public roads.
Related developments
Facets of this trend
- Regulatory developments for autonomous driving
- Real time data analytics (computer vision, processing of sensor data)
Getting ahead of the trend
Autonomous driving is a trend that has high potential to disrupt the supply chain industry. Technological and regulatory developments need to be closely monitored.
LSPs with owned assets need to know when to engage in this trend, while other businesses in supply chain need to understand the consequences that changing service levels and costs yield for their business models.
Cyber risk mitigation – in focus
Affected industries:
All industries
Affected supply chain segments:
All segments
Addressing the shady sides of technology
AI tools and methods can unlock huge savings and improve results in various tasks and processes. Influenced by the broad range of benefits AI offers, some users tend to neglect the risks related to the use of AI. Decision makers need to ask themselves: What happens to the data that you feed AI algorithms? Are AI-generated results reliable? Who is responsible for possible errors? Can hackers use AI to harm my business operations?
Related developments
Facets of this trend
- Hacker attacks and phishing attempts
- Data security and data protection concerns
- Liability issues connected to AI
- Quality concerns: chatbot “hallucinations” and inability to explain answers
- Governments discuss legislation on AI usage with regard to security, among other concerns
Getting ahead of the trend
Make use of the potential of AI while considering the risks connected to it.
Cross-check AI-generated solutions with proven methods. Train employees regarding the hazards of AI phishing.
Evaluate how data is processed by external companies and set up appropriate rules to prevent the exposure of confidential data.
Authors
Holger Clasing
Vice President and Head of Strategy Practice
4flow consulting
Wendelin Gross
Head of
4flow research
Gero Holzheid
Supply Chain Scientist
4flow research
