The importance of sensing, analysing, and responding at speed for future supply chain planning

Supply chain planning processes and tools have traditionally been focused on – forecasting immediate demand, creating production schedules, and optimising inventory levels. However, with the increasing frequency and severity of disruptions such as natural disasters, political instability, and technological advancements, it is clear that these traditional approaches are no longer sufficient. The supply chain planning processes and tools must transform to include the ability to sense, analyse, and respond quickly.

Artificial Intelligence (AI) is rapidly becoming an important tool for addressing future disruptions in supply chain planning. AI can be used to sense potential disruptions, analyse data, and respond quickly, providing a significant advantage to companies that are able to implement it effectively.

Sense Disruptions

Enhancing the ability to sense potential long-term and short-term disruptions is critical for organisations to provide exceptional customer experience.

Organisations spend months, sometimes years, designing new products that can attract customers. But the same organisations that develop stellar products fail at accurately sensing consumer demand ahead of time. Organisations can sense potential disruptions early on by generating cold-start forecasts.

Cold-start forecasting is a type of time series forecasting that deals with the problem of predicting future values when there is little or no historical data available. This is a common problem in many industries, such as retail, finance, and transportation, where new products, services, or customers are constantly being introduced. In these cases, traditional time series forecasting methods, which rely on historical data, are not effective.

AI can help with cold-start forecasting by using other sources of data to make predictions. For example, AI-powered systems can use data from related products, services, or customers to make predictions about a new product, service, or customer. Additionally, AI can use demographic data, such as age, gender, and income, to make predictions about a new customer.

One example of how AI can be used to address cold-start forecasting is through the use of deep learning algorithms. These algorithms, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, can be trained on a large dataset of related time series data and then applied to the new dataset. This allows the algorithm to learn patterns and relationships in the data that can be used to make predictions about the new dataset.

Another way AI can be used to address cold-start forecasting is through the use of transfer learning. This is a technique where a pre-trained model is fine-tuned to a new dataset, allowing it to learn from the new data while still retaining the knowledge from the previous dataset. This can be particularly useful when there is a limited amount of data available for the new dataset.

Sensing long-term disruptions through demand forecasting is just not enough. The supply chain function should develop capabilities to address emerging and unforeseen troubles as well. One of the key ways AI can be used to respond to emerging supply chain disruptions is through real-time monitoring systems.

AI-powered sensors and IoT devices can be used to collect data on a wide range of factors that can affect the transportation of goods, such as weather patterns, traffic patterns, and even social media sentiment. By having this data in real-time, AI-powered systems can detect potential disruptions before they occur and alert supply chain managers to take action.

For instance, if an AI system detects a weather pattern that could cause a transportation disruption, it can alert managers to reroute deliveries or adjust production schedules to minimise the impact. Additionally, by monitoring social media and other online platforms, companies can identify early warning signs of potential disruptions, such as strikes or protests.

Quantify Risk
AI can also be used to analyse data to determine the potential impact of disruptions on the supply chain. Machine learning algorithms can quickly process large amounts of data and identify patterns and trends that can indicate potential disruptions.

For example, if a company sees a significant increase in demand for a particular product, it may indicate a potential shortage of that product. By analysing this data, AI-powered systems can provide supply chain managers with insights on preparing for potential disruptions and proactively minimising their impact.

Furthermore, the collected data can be used to quantify the potential impact. By generating AI-powered simulation and optimisation algorithms, managers can identify potential risks and develop mitigation strategies. For example, a simulation can be run to analyse the potential impact of a natural disaster on the supply chain, allowing managers to identify potential vulnerabilities and develop contingency plans.

Once potential disruptions have been identified, AI can also be used to respond quickly and adapt to changing conditions. For example, AI-powered inventory systems can be used to quickly adjust production schedules to meet changing demand.

Additionally, AI-powered logistics systems can be used to optimise transportation routes and minimise delays. By having a flexible and agile supply chain that can quickly adapt to changing conditions, companies can minimise the impact of disruptions on their business.

AI is here to stay! Organisations should prioritise investing in supply chain tools that are powered by AI and informed by data. By enhancing the ability to sense potential disruptions, using advanced analytics to determine the potential impact, and responding at speed, companies can minimise the impact of disruptions on their business.

Companies that can implement these techniques will be better equipped to navigate the rapidly changing business environment and remain competitive in the future.

The author is Analytics and AI leader at Bose Corporation.

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