Supply chain management models can benefit immensely from big data. However, Brands have been slower to adapt big data into their supply chain management strategies as other aspects of their business, such as marketing and cost control.
According to research from Forbes, 64% of supply chain executives expect big data to play a significant role in their inventory management strategy in the coming years. Brands should understand the role picked out a place in supply chain management and the best analytics approaches to take.
Common Fallacies with Big Data and Supply Chain Management
Some analytics approaches art feasible for just in time inventory management strategy predicated on big data. Prescriptive in analytics is a prime example.
Prescriptive analytics creates regression analyses from thousands of known Data sets. Brands attempt to use prescriptive analytics to forecast future demand based on price points and other factors.
While prescriptive analytics is valuable for setting revenue goals, you don’t want to use it with A just-in-time inventory management strategy. You’re just in time inventory management strategy requires you to track inventory in real time. Every prescriptive analytics model has a margin of error, which can leave your warehouse overstocked or mean you won’t have enough inventory to satisfy demand.
Suresh Acharya, head of analytics division for JDA, told Forbes that more effective supply chain analytics models focus on both prescriptive and predictive analytics.
“What we are trying to do is derive insights which are both more predictive – they allow us to see what is going to happen, going forward – and prescriptive – now we know something, what should we do about it? Whatever name we gave it, using data to improve our operations is something that we have always wanted to do, and something our customers have always needed. Of course, some customers are more mature than others in terms of understanding what value data driven analytics can provide.”
Big Data is Creating More Complex Relationships with Suppliers
Throughout the past century, relationships between suppliers and retailers were very simple. The supplier attempted to fill retailer orders as quickly as possible.
In the age of big data, the relationship is more complex. Both parties share transactional and customer demographic data with each other, which creates richer and more profitable relationships for both. Both sides can incorporate each other’s data into their supply-chain management algorithms, which leads to greater efficiency.
Big Data Helps Forecast ETAs Based on Transportation Logistics
Even the best fleeting company is bound to encounter problems transporting goods across certain areas. It was difficult for brands to implement a Just-in-Time inventory management strategy when they had to forecast weather trends, different terrain factors, seasonal traffic patterns and other complex variables. Brands can also use big data to track their fleets better and know when to invest in new trucks.
Big data has made it much easier for brands to take these factors into account and set more accurate delivery ETAs.
Big Data Breaks the Supply-Chain Management Process Down and Makes it More Manageable
Managing a supply-chain management strategy can seem overwhelming, but big data enables brands to break the process down, Silvon Software explains.
“It’s very easy to get into a groove when managing a supply chain. Often supply chains have so many different components that supply chain managers are happy when the end result is working as planned. Big data looks at the supply chain both holistically and at the granular level – which helps to identify areas in need of improvement or opportunities for advancement throughout the supply chain, which can increase effectiveness and output.”