The ‘Big’ transformation of data science for supply chain
If you’re reading this, it means you’ve recognized the importance of prioritizing the data science for supply chain. And rightly so. What was once seen as a purely operational function has now emerged as a valuable strategic asset for numerous industries and companies.
Its proof of concept goes way back to 1990s where companies like Walmart and Procter & Gamble through academic research and the innovative practices changed the way this worked.
Cut to today, these exciting developments — often referred to as “Industry 4.0,” “Supply Chain 4.0,” and “Supply Chain Digitization” — aim to revolutionize the supply chain management with analytics. And the results are in.
Driven through the integration of data science, particularly AI/ML — companies now observe shorter replenishment times and lead times, complete automation of demand prediction, lower inventory levels, and improved on-time manufacturing and delivery.
Current Applications of Data Science in Supply Chain Management
Now when you see the overall scenario with an eagle’s view, one thing is for sure- supply chains specially involving multiple countries are/will always be highly prone to disruptions due to any discrepancies anywhere on the globe.
An impact from any negative event in one location can affect multiple products and many countries. Take the blockage of the Suez Canal in early 2021 as an example. It held up USD 9.2 billion worth of goods each day causing serious worldwide supply cain shocks.
While we can’t stop disruptions, what we can do is to be prepared for it. Thanks to data science for supply chain management, with right data and the right technical expertise, AI and Analytics is already creating quite an impact through:
1. Enhancing Demand Forecasting
With ability to integrate more data with higher granularity, companies have started to utilize demand forecasting techniques likes time series analysis, regression models, hybrid models and demand sensing. But one thing which we have observed is that there is still confusion regarding choosing the best option.
And to solve this perplexity, it’ll all boil down to the targeted factors of consideration for your company. Similarly, external factors such as product innovation, trends, and government policies also consistently alter demand behaviour, prompting companies to adapt their product portfolios to meet market needs.
Hence, enhancing accuracy in demand forecasting becomes a necessity, considering it becomes the input data for all sales and production related processes which leads directly to better performance in production scheduling and the right inventory safety and cycle stock, such as precision in data and automated parameter adjustments.
2. Better Supply Chain Visibility
We have already talked about how data science for supply chain is already in action to predict demand which is a crucial part of optimizing process. But that’s not all. Every year, at least one in twenty companies experiences a supply chain disruption costing over $100 million. Evident enough, supply chain disruptions have significant financial implications.
In such cases, data science provides valuable data as it encompasses every stage of the product lifecycle of the supply chain. This helps companies and distributors to attain full visibility of the supply chain through a strong digital supply chain system.
Now with these insights, you can streamline your business procedures, monitor inventory changes, and even enhance sales transparency in real time until the final consumer purchases the product.
This leads to greater efficiency, cost reduction, improved responsiveness to market changes, and ultimately, better risk mitigation.
3. Dynamic Routing
Now taking it a step further, modern network optimization considers the entire supply chain ecosystem, including all plants, distribution centres, suppliers, and customers.
Which helps in getting better understanding of interdependencies between different components of the supply chain and how changes in one area can affect others.
With this in place dynamic route optimization continuously adjusts delivery routes based on current data, such as traffic conditions, weather changes, and last-minute customer requests.
This adaptability is achieved through techniques like Genetic Algorithms or Reinforcement Learning which effectively explore large solution spaces and adapt to dynamic conditions throughout the whole supply chain. This enables companies to quickly react to interruptions or fluctuations in demand.
By integrating dynamic route optimization into the supply chain network, companies can reduce idle time and optimize resource utilization. This means that delivery vehicles can be rerouted in real-time to accommodate new orders or avoid delays, ensuring that all components of the supply chain operate smoothly and efficiently.
Seeing the big picture
Overall, this is an exciting time for supply chain management industry. Data is omnipresent when dealing with supply chain process but let’s be honest — it’s not without its challenges. It’s not just about the latest technology; it’s really about changing the way we think and work. The abundance information is not always leveraged to actually improve the overall flow.
To fully capitalize on this opportunity, it’s crucial to begin by pinpointing the right use cases that align with your organization’s objectives. Consider the specific challenges you want to address or the efficiencies you aim to achieve.
A smart strategy is to collaborate with experts who understand the nuances of the industry, have sound analytical domain expertise, and have the tools to provide quick and accurate results.
So, let’s roll up our sleeves and embark on this exciting journey together!
