AI opportunities for supply chain functions
Supply chain functions have often historically been constrained by extensive manual processes and reactive decision-making. Yet, recent advances in AI look set to reshape almost every aspect of how organizations manage their goods and services. From real-time demand forecasting to the automation of repetitive tasks, implementing AI could unlock a new era of efficiency and resilience within the supply chain landscape.
But, with a vast array of AI opportunities available, how do you determine the use cases most appropriate for your organization?
Function-specific AI initiatives should always be considered within the context of your organization’s overall strategy for AI and should be closely linked to your business objectives.
AI implementation
Our recommended approach
Step 1
Define the AI vision and guiding principles
Step 2
Identify AI use cases and define a strategic blueprint
Step 3
Prioritize pilots and define an AI roadmap
Identifying potential AI use cases within your supply chain function
Though the scope of supply chain functions varies between organizations, there are common activities and associated challenges which AI initiatives may help tackle. Clearly defining the pain points you want to address will help you to identify relevant AI use cases you may want to implement.
Selecting and focusing on four to six use cases specific to your organization’s circumstances can guide your AI implementation approach and help make a potentially overwhelming landscape of opportunities feel more manageable.
Recent AI opportunities within supply chain functions
AI analytic capabilities can identify and set target inventory levels across all supply chain elements, from warehouses to localised stock. Algorithms can analyse historical data and automatically order stock when approaching low levels. This approach builds resilience in the organization by ensuring a steady stream of available stock, which can be used to avoid overstocking scenarios and the associated warehouse fees or impact of increased working capital.
AI can absorb vast amounts of historical data, including market trends, sales figures, and even social media sentiment, to identify patterns humans might miss. This enables AI systems to create highly accurate demand forecasts, accounting for factors like seasonality, product promotions, and external events. AI's adaptive nature allows it to continuously learn and constantly refine its forecasts, incorporating real-time information like sudden product trends.
AI can identify risks across the entire span of your organization’s supply chain, linking internal operational risks with information about potential external risks, from geopolitical changes to extreme weather events. This information can be used to predict the potential likelihood and severity of supply chain delays. AI can also strengthen an organization’s ability to react to, and manage, risks using holistic risk impact analysis tools and co-ordinate responses across disparate parts of an organization.
Multiple companies, including Amazon and Walmart, are already employing autonomous robots to carry out many warehousing activities ,such as moving shelves and picking and packing products. AI can be used to automate other elements of the warehouse too, including packaging and labelling based on product specifications and order information, or automatically completing order documentation by parsing text from delivery images, increasing accuracy and speed.
AI is also being used in procurement functions to radically alter the way in which organizations make the sourcing decisions which feed their supply chain processes.
Evaluating feasibility and implementation risks
AI's potential to change supply chain management is undeniable, but its implementation comes with feasibility concerns that require careful consideration. A good evaluation of available AI offerings must consider technical and organizational feasibility challenges before implementation decisions can be made.
Some of the technical feasibility challenges include:
- Quality of, and ease of access to, the data required to carry out advanced analytics, especially third-party data from within your supply chain
- Challenges over integrating AI with legacy systems
- Complexity involved in tailoring generic AI solutions to your individual business needs
- The computing power required for complex AI models can strain existing IT infrastructure, demanding upgrades or solutions that may not be budget-friendly.
Organizational feasibility factors may include:
- High cost of implementation, with AI tools, data infrastructure, and expert talent potentially requiring a significant upfront investment
- Ethical considerations around displacing jobs, bias in algorithms, and data privacy all require careful navigation and transparency
- Organization structure – the ability to automate large elements of the supply chain may rely on redeploying resources into new positions or retraining the workforce with the necessary technical skills to run your initiative after implementation
- Difficulty in aligning your functions' local goals with the organization’s overall AI strategy.
Leaders implementing AI initiatives should also be aware of external factors, such as rapidly changing regulation. This can create uncertainty for organizations looking to implement AI solutions, making it difficult to plan effectively. Changes to regulation could also result in increased compliance costs so always consider whether your organization could afford such ongoing costs as part of your initial case for change.
A balanced view of whether your wider organization is ready to implement AI should always form part of your decision before proceeding with a selected use case.
Calculating the return on AI investment
Any prospective AI project should start with creating a clear case for change. This involves defining the costs, identifying the potential risks and benefits, and calculating your projected return on investment.
While every use case and specific organization will have different potential benefits, some of the common benefit categories to consider for AI initiatives are:
From the implementation of AI-powered robots to automate warehouse activities, through to the use of AI for data entry and invoice processing, the efficiency benefits across a supply chain function are significant. These efficiencies can reduce the amount of time human workers spend on low value-add tasks, freeing up capacity which can be used for more strategic activities.
AI can also increase productivity in other areas of the supply chain, such as optimizing transportation routes by considering factors such as traffic, fuel consumption and weather.
While increasing efficiency often comes with associated cost savings, AI can directly save money in other areas too. For example, by optimizing your inventory levels using AI algorithms and removing excess stock, organizations can reduce their working capital to immediately free up cash.
AI data and analytics capabilities can additionally be used to assess historic data and identify which future cost-saving opportunities are likely to give you the best return on investment.
AI-enabled optimization of transportation routes leads to obvious sustainability benefits in the form of reduced fuel consumption and carbon emissions, but AI can deliver other ESG benefits too. These include reducing waste associated with overproduction and overstocking and improving energy efficiency by analyzing and optimizing consumption. The AI technology developed by DeepMind enabled Google to reduce the energy used at its datacentres by up to 40% and could feasibly be deployed within warehouses and production facilities to make similar savings.
AI can also assist organizations with gathering relevant ESG reporting information and providing visibility of end-to-end supply chains. This is an increasingly important consideration given incoming changes to reporting requirements.
A pragmatic approach for deploying AI within your supply chain function
Ultimately, the success of an AI rollout within an organization will rely on adopting a pragmatic approach to implementation. This involves identifying the right AI use cases which address specific challenges, aligning these with your organization’s overall strategy, and taking a holistic view of the potential benefits, risks and organizational constraints.
Aiming to completely overhaul your warehouse operations with a robotic workforce may not be a feasible use case for many organizations, but relatively simple AI tools can deliver productivity enhancements and decision-making potential, forming a potentially more compelling proposition.
An organization’s AI success story is likely to feature small scale trials to reduce risk and test user sentiment, bring the workforce on the change journey from the start, and take an adaptable approach to AI strategy which can rapidly take advantage of new technologies as they become available.
The journey towards realising the full benefits of AI in supply chains is ongoing, but it promises transformative outcomes for those who are willing to embrace its potential.
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