In the early weeks of the COVID-19 pandemic, as cities went into lockdown, people across the US made a dash for the supermarkets, local grocery stores, and wholesalers in an attempt to stock up on essentials, in particular, toilet paper.

This panic buying led to a country-wide inventory shortage. Yet, manufacturers recognised that once the panic subsided, customers who had hoarded tissue rolls would not require new stock any time soon. Instead of ramping up production, some brands decided to get rid of their stock and moderate new production in response to the changing demand.

The shortage of toilet paper was a unique disruption brought about by the pandemic. However, the industry learned a couple of lessons from it. So, when energy prices in Europe threatened to trigger another toilet roll shortage, manufacturers planned ways to combat the problem (sometimes through shrinkflation) and prevent hoarding.

This was possible, in part, through data analytics, which helped businesses understand market fluctuations and provide predictive data to prepare them for future disruptions.

What is predictive analytics?

Simply put, data analytics answers 4 important questions, as explained by Harvard Business School:

  • What happened?
  • Why did it happen?
  • What may happen in the future?
  • What the next step should be?

While descriptive and diagnostic analytics help us identify past and current trends and the reasons for them, predictive analytics provides a forecast of future events, market trends and potential disruptions. Meanwhile, prescriptive analytics suggests how we can prepare to meet these future challenges.

In the past few decades, as technology has allowed for more detailed and thorough data collection and analysis, it has provided the opportunity for businesses to tailor their production, logistics, and sales to better suit the demands of the market while also helping them prepare for the future, optimize their operations, and stay ahead of the market.

How can predictive analysis prepare your supply chain for future disruptions?

With predictive analytics, businesses can predict future events and potential trends by leveraging historic data of past operations, industry insights, and market evolution. When supported by artificial intelligence it can help anticipate potential issues and develop strategies to mitigate them. It can be used for various segments of your supply chain, from manufacturing to logistics and transportation. Here are several ways in which predictive analytics can help:

  • Demand forecasting: Predictive models leverage historical sales data, market trends, and external factors such as seasonality and economic conditions to generate more accurate demand forecasts. This enables businesses to maintain optimal inventory levels, mitigating the risks of stockouts and overstocking. By simulating various scenarios, companies can better understand how changes in demand patterns might affect the supply chain and prepare accordingly.
  • Risk identification and management: Predictive analytics assess supplier reliability and performance by analyzing historical data on delivery times, quality issues, and financial stability. This helps identify high-risk suppliers and develop contingency plans. Additionally, by examining data on geopolitical events, natural disasters, and other external risks, predictive models can forecast potential disruptions, allowing companies to create effective risk mitigation strategies.
  • Inventory optimization: Predictive analytics determine appropriate safety stock levels by analyzing demand variability and lead times, ensuring a buffer against unexpected disruptions. By predicting when inventory levels will fall below a specified threshold, businesses can automate the reorder process, thereby reducing the risk of stockouts.
  • Transportation and logistics: Predictive models analyze traffic patterns, weather conditions, and other variables to optimize shipping routes and schedules, minimizing delays and reducing transportation costs. Evaluating historical data on carrier performance aids in selecting reliable transportation partners and anticipating potential delays.
  • Production planning: Predictive analytics forecast production capacity requirements based on expected demand, assisting in resource allocation and scheduling. Predictive maintenance models can anticipate equipment failures, enabling proactive maintenance scheduling, reducing downtime, and ensuring continuous production.
  • Customer insights and behaviour: By analyzing customer ordering behaviour, predictive analytics can foresee changes in demand and adjust supply chain strategies accordingly. Monitoring social media and other platforms for customer sentiment provides early warning signs of potential issues, enabling proactive responses.
  • Cost management: Predictive models forecast changes in costs related to raw materials, labor, and other supply chain components, aiding in budgeting and financial planning. By simulating various strategies and their potential outcomes, businesses can make informed decisions about cost-saving measures.
  • Supply chain visibility: Predictive analytics offer real-time visibility into the entire supply chain, identifying bottlenecks and inefficiencies. This facilitates quicker responses to disruptions and more agile supply chain management.

By integrating predictive analytics into supply chain management, businesses can enhance their ability to anticipate and respond to disruptions, ultimately leading to a more resilient and efficient supply chain.

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