Adoption of AI has been fast and furious; in May, a McKinsey survey showed that 65% of businesses are regularly using generative AI, which is twice the amount reported last year. Leaders are (understandably) excited by the possibility of simple solutions to complex problems, writes Jason Popillion, director technology at SPS Commerce.
And one problem plaguing sellers for several years has been the supply chain issues that were highlighted by the pandemic, but did not go away when it was in the rearview. Problems ranging from bridge collapses and climate change in the west to global conflicts in the east continue to cause shipping bottlenecks and cost increases worldwide.
For ecommerce, AI tools have long been a staple, from chatbots to automated sales funnels. But as AI gets better, more will jump on the trend in an effort to improve business processes and brand experience.
When used correctly, AI can help companies create a more efficient and resilient supply chain. However, rushing into AI solutions that are not trained to solve the specific problems unique to supply chain management may not deliver the expected results, and could lead to bigger problems, such as security risks. As retailers consider implementing AI, they need a frame of reference to help make smarter decisions and waste less time going after tools and solutions that aren’t a good fit.
Gauging your AI readiness for supply chain optimisation
AI is most effective when the right data is available to support it. There is value in utilising AI at any level, but knowing how prepared you are helps you align with the correct tools and anticipate the outcome.
To help gauge your readiness for integrating AI into your supply chain management, try categorizing the way you implement AI into three stages: Initial Adoption, Data Exploration and Advanced Implementation.
Stage I. Initial adoption
AI can enhance routine supply chain tasks like scheduling deliveries, tracking inventory and managing supplier communications. This phase involves asking AI to solve simple problems, so it requires minimal background data and is accessible to companies of all sizes. Focusing on internal efficiency helps build confidence in AI’s capabilities, laying the groundwork for advanced applications down the road.
As trust in AI grows among the workforce, organizations can explore more complex applications in subsequent stages.
Stage II. Data exploration
Good data allows AI to handle deeper analysis and interpretation, providing significant insights for supply chain management. AI models process large volumes of information to uncover patterns or trends not immediately apparent in raw data, aiding in decisions about inventory, staffing, and promotions. For example, AI can project trends for the next year using sales data from past seasons, or validate electronic data interchange (EDI) documents.
Investing in data management ensures reliable, valuable, and insightful AI outcomes.
Stage III. Advanced implementation
The most advanced phase of AI adoption happens when employees and leadership trust that the AI is tuned to both the industry and the organization, ensuring it understands tasks and can guide processes. In this phase, AI is actually driving decision-making. For example, if AI can categorize system errors, as in the previous phase, the next logical step is for it to provide specific instructions for fixing those errors, reducing the burden on the IT team.
Combining AI tools (i.e., machine learning (ML) and generative AI) can further optimize processes, deriving more results from a single set of data.
What to think about cefore implementing AI
Implementing AI in supply chain management requires careful planning and attention to practical considerations.
Top of mind should be ensuring data security and governance. Safeguarding proprietary information is not only important to protect your company from financial loss and reputational damage, but failing to protect vendor or client data can be cause for strained partnerships and/or legal consequences.
Second, organizations should understand that AI cannot and should not function without human oversight. AI is not intended to replace employees; rather, it is intended to enhance humanwork. When we embrace it that way, we all win.
Finally, just as you seek out experts for accounting, legal help, logistics and marketing, it makes sense to look for AI integration partners that are specifically geared towards supply chain management. You can’t just use an open-source tool, start a subscription and expect to get value. The right vendor will have industry expertise, a proven track record, scalable solutions and support services unique to your supply chain needs.
Applications of AI in the retail supply chain
You have a clean, organized dataset, training programs in place for your employees, and the right partner on board to begin serious AI integration. You might now be asking––what can AI actually offer in terms of strengthening my supply chain?
From predicting what customers want to keeping inventory in check, the applications are practically endless. Let’s dive into some of the ways AI can have a profound impact.
- Demand forecasting
AI-driven demand forecasting helps retailers predict customer demand with greater accuracy by analysing historical sales data, market trends, and external factors such as weather and economic conditions. This allows for more precise inventory planning, reducing overstock and stockouts.
- Inventory management
AI improves inventory management by automating stock tracking and replenishment processes. Real-time data analysis ensures optimal inventory levels, minimising carrying costs and preventing lost sales due to inventory shortages.
- Supply chain optimisation
AI optimises supply chain operations by identifying inefficiencies and suggesting improvements. Machine learning algorithms can streamline logistics, reduce transportation costs and enhance supplier collaboration, resulting in a more agile and responsive supply chain.
- Personalised customer communications
AI enables personalized customer communications by analyzing purchasing behavior and preferences. Retailers can use this information to tailor marketing messages, promotions and product recommendations, enhancing customer satisfaction and loyalty.
- Risk management
AI addresses risk management by predicting disruptions in the supply chain, such as supplier failures or transportation delays. By proactively addressing these risks, retailers can maintain continuity and avoid costly interruptions.
Preparing for the future with AI
AI is changing the way most industries operate, and those changes will continue to accelerate. Retailers who don’t dive into AI will find themselves left behind.
We are only scratching the surface of what AI can do, and it will take time to fully understand its potential. Retail has always been about adaptation, from the invention of cash registers and customer credit to the evolution of digital technology. Each new advancement has led to greater efficiency and growth. AI is simply the next step in this ongoing evolution, poised to streamline supply chains, boost market responsiveness and enhance the delivery of goods. Embracing this journey now will prepare retailers to thrive in the future, transforming challenges into opportunities for success.
Jason Popillion, director technology at SPS Commerce
You are in: Home » DeliveryX News » Supply chain » GUEST COMMENT AI is here, and it’s revolutionising the supply chain. Are you ready?
GUEST COMMENT AI is here, and it’s revolutionising the supply chain. Are you ready?
Katie Searles
Adoption of AI has been fast and furious; in May, a McKinsey survey showed that 65% of businesses are regularly using generative AI, which is twice the amount reported last year. Leaders are (understandably) excited by the possibility of simple solutions to complex problems, writes Jason Popillion, director technology at SPS Commerce.
And one problem plaguing sellers for several years has been the supply chain issues that were highlighted by the pandemic, but did not go away when it was in the rearview. Problems ranging from bridge collapses and climate change in the west to global conflicts in the east continue to cause shipping bottlenecks and cost increases worldwide.
For ecommerce, AI tools have long been a staple, from chatbots to automated sales funnels. But as AI gets better, more will jump on the trend in an effort to improve business processes and brand experience.
When used correctly, AI can help companies create a more efficient and resilient supply chain. However, rushing into AI solutions that are not trained to solve the specific problems unique to supply chain management may not deliver the expected results, and could lead to bigger problems, such as security risks. As retailers consider implementing AI, they need a frame of reference to help make smarter decisions and waste less time going after tools and solutions that aren’t a good fit.
Gauging your AI readiness for supply chain optimisation
AI is most effective when the right data is available to support it. There is value in utilising AI at any level, but knowing how prepared you are helps you align with the correct tools and anticipate the outcome.
To help gauge your readiness for integrating AI into your supply chain management, try categorizing the way you implement AI into three stages: Initial Adoption, Data Exploration and Advanced Implementation.
Stage I. Initial adoption
AI can enhance routine supply chain tasks like scheduling deliveries, tracking inventory and managing supplier communications. This phase involves asking AI to solve simple problems, so it requires minimal background data and is accessible to companies of all sizes. Focusing on internal efficiency helps build confidence in AI’s capabilities, laying the groundwork for advanced applications down the road.
As trust in AI grows among the workforce, organizations can explore more complex applications in subsequent stages.
Stage II. Data exploration
Good data allows AI to handle deeper analysis and interpretation, providing significant insights for supply chain management. AI models process large volumes of information to uncover patterns or trends not immediately apparent in raw data, aiding in decisions about inventory, staffing, and promotions. For example, AI can project trends for the next year using sales data from past seasons, or validate electronic data interchange (EDI) documents.
Investing in data management ensures reliable, valuable, and insightful AI outcomes.
Stage III. Advanced implementation
The most advanced phase of AI adoption happens when employees and leadership trust that the AI is tuned to both the industry and the organization, ensuring it understands tasks and can guide processes. In this phase, AI is actually driving decision-making. For example, if AI can categorize system errors, as in the previous phase, the next logical step is for it to provide specific instructions for fixing those errors, reducing the burden on the IT team.
Combining AI tools (i.e., machine learning (ML) and generative AI) can further optimize processes, deriving more results from a single set of data.
What to think about cefore implementing AI
Implementing AI in supply chain management requires careful planning and attention to practical considerations.
Top of mind should be ensuring data security and governance. Safeguarding proprietary information is not only important to protect your company from financial loss and reputational damage, but failing to protect vendor or client data can be cause for strained partnerships and/or legal consequences.
Second, organizations should understand that AI cannot and should not function without human oversight. AI is not intended to replace employees; rather, it is intended to enhance humanwork. When we embrace it that way, we all win.
Finally, just as you seek out experts for accounting, legal help, logistics and marketing, it makes sense to look for AI integration partners that are specifically geared towards supply chain management. You can’t just use an open-source tool, start a subscription and expect to get value. The right vendor will have industry expertise, a proven track record, scalable solutions and support services unique to your supply chain needs.
Applications of AI in the retail supply chain
You have a clean, organized dataset, training programs in place for your employees, and the right partner on board to begin serious AI integration. You might now be asking––what can AI actually offer in terms of strengthening my supply chain?
From predicting what customers want to keeping inventory in check, the applications are practically endless. Let’s dive into some of the ways AI can have a profound impact.
AI-driven demand forecasting helps retailers predict customer demand with greater accuracy by analysing historical sales data, market trends, and external factors such as weather and economic conditions. This allows for more precise inventory planning, reducing overstock and stockouts.
AI improves inventory management by automating stock tracking and replenishment processes. Real-time data analysis ensures optimal inventory levels, minimising carrying costs and preventing lost sales due to inventory shortages.
AI optimises supply chain operations by identifying inefficiencies and suggesting improvements. Machine learning algorithms can streamline logistics, reduce transportation costs and enhance supplier collaboration, resulting in a more agile and responsive supply chain.
AI enables personalized customer communications by analyzing purchasing behavior and preferences. Retailers can use this information to tailor marketing messages, promotions and product recommendations, enhancing customer satisfaction and loyalty.
AI addresses risk management by predicting disruptions in the supply chain, such as supplier failures or transportation delays. By proactively addressing these risks, retailers can maintain continuity and avoid costly interruptions.
Preparing for the future with AI
AI is changing the way most industries operate, and those changes will continue to accelerate. Retailers who don’t dive into AI will find themselves left behind.
We are only scratching the surface of what AI can do, and it will take time to fully understand its potential. Retail has always been about adaptation, from the invention of cash registers and customer credit to the evolution of digital technology. Each new advancement has led to greater efficiency and growth. AI is simply the next step in this ongoing evolution, poised to streamline supply chains, boost market responsiveness and enhance the delivery of goods. Embracing this journey now will prepare retailers to thrive in the future, transforming challenges into opportunities for success.
Jason Popillion, director technology at SPS Commerce
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