Innovations in big data storage, cloud computing, and graphical processing units have enabled businesses to incorporate AI into their mainstream products and services. The use of AI to improve customer experience and drive innovation is not just limited to product-based companies. Organisations depend on AI to improve the operations of internal enterprise functions such as supply chain, human resources, sales, marketing, finance, etc.
Like other functions, supply chain management is vital for garnering a competitive advantage in the marketplace. Organisations are rushing to compete by developing an outstanding supply chain function, and recent supply chain disruptions exponentially propelled this sentiment. Businesses are taking several key initiatives to drive innovation in supply chain management, and digital/ technology is consistently at the top of their list.
A recent survey by Gartner indicates that 34% of supply chain leaders say that adapting to new technology is the most important strategic change supply chain organisations will face five years from now. Unsurprisingly, AI and analytics are among the top technology themes.
AI and supply chain management functions are made for each other. Most processes in the supply chain leave a trail of data, and this big data can then be used to understand historical trends using AI.
In this article, I will discuss how AI can be applied to supply chain processes to yield transformative results. I will describe how AI can help with the three levels of supply chain management by providing a few examples.
Strategic Level
Strategic level planning involves making plans and decisions that align with the long-range vision of your organisation. The decisions made at this level are often influenced by the executive leaders from other departments and will act as the building blocks for the enterprise. The decisions made at this level will serve as anchors for projects to come in the upcoming years. One of the classic examples of strategic-level decisions is deciding the long-term product vision for the company. Consumer needs are everchanging, and an essential step for businesses to succeed in this ultra-competitive environment is to predict customers’ needs and design products that excite them. Anticipating customer needs is a complex task correlated with several factors: market size, demographics, customer sentiment, micro and macroeconomic factors, brand value, price, emerging trends, etc. One way to quantify what impacts customer needs is to individually examine how the above-mentioned variables impact product design strategy. But often, all of these variables are correlated with each other, making it impossible to precisely understand what’s vital for customers.
AI can help organisations to conduct experiments with multiple covariates and understand the contributions of each factor that influences customer needs. Decision-makers can then use this information to design a compelling product vision that’s fully grounded in data. The developed models can continually be refreshed with the latest information to reflect the dynamic external markets.
Tactical Level
Tactical-level supply chain management involves translating high-level strategic goals into tangible and manageable tactical projects. A strategic-level objective will typically have multiple tactical projects under it. One example that fits into this category is material procurement – can we optimize the procurement of raw materials and parts from suppliers?
Procurement in the supply chain is typically planned. Sourcing teams are responsible for developing and nurturing relationships with suppliers globally, and they are used to placing orders for their upcoming product launches. But the recent supply chain disruptions are increasing the complexity of the sourcing function. Trusted vendors are often running short of the components, and the price fluctuations are at an alarmingly high level.
This rapid change in pricing and availability is forcing companies to make purchase decisions on the spot. It is unrealistic for organisations to have people continuously monitor hundreds and thousands of websites and track price movements. Fortunately, AI is capable of predicting a “good buy.” We can feed the historically good decisions that were made by humans and train the models to learn the attributes or traits of a good purchase. The trained model can then be deployed to make autonomous purchases for scarce or high-demand components.
Operational Level
Supply chain operations deal with the day-to-day tasks that are required to keep the light bulbs on. Inventory management, warehouse distribution, setting up schedules, resource management, on-time delivery, processing returns, etc., are some key tasks that fall under supply chain operations. Operational-level tasks are critical, and any challenges with the operations can have a detrimental effect on an organization’s brand as it impacts customers directly.
Demand forecasting is one of the key tasks performed by the supply chain operations team. Having a good forecast is critical for organisations on multiple levels. Forecasting helps organizations – procure the right amount of raw materials and parts to build the product, deliver the products to suitable warehouses on time, distribute the products to resellers, and finally meet the customer’s demand. Time series modeling, a sub-field of AI, can generate accurate forecasts by incorporating historical sales of the product. AI models can take into account the impact of marketing spending, promotions, external competition, and macro/ microeconomic conditions and can generate a robust forecast by day or week.
There is no doubt that AI is revolutionizing the way businesses think about their supply chains. Organisations that want to lead with AI should be prepared by storing and organising their data. AI is not a magic bullet; it needs good-quality data to learn the latent trends. With representative data, organisations will be able to generate meaningful AI-driven insights.
The writer is Analytics and AI leader at Bose Corporation.
