The ultimate guide to big data in supply chain management

It’s no secret that data is the foundation of the digital transformation that is permeating all facets of society and business. 

While supply chain and logistics may not be the first sector that comes to mind when thinking of digital advancement, there are massive cost, efficiency and reliability benefits to be realised from prioritising big data in supply chain management. 

There’s just one big problem: supply chain data management is made notoriously difficult by the fact that supply chain data is highly dispersed across forwarders, carriers, 3PL partners, warehouses, hauliers and other actors. Unifying this data in a single platform is foundational to unlocking the potential benefits of big data in supply chain management. 

In this article, we set out a working definition of supply chain data, break down the challenge of data management, and explore how big data and analytics are transforming supply chain management.

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What is supply chain data?

Supply chain data refers to every data point collected about goods from the moment they are ordered through to the moment they are delivered to your end customers. This includes data points associated with purchase orders, shipment tracking, warehouse inventory and everything in between. 

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The challenge of supply chain data management 

ERP systems are frequently touted as the ‘holy grail’ solution to the problem of  supply chain master data management and visibility. But the reality is, in most cases they aren’t living up to the promise. The systemic shortcomings of ERPs can be boiled down to two key factors.

1.  ERP systems are designed to provide a ‘moment in time’ snapshot 

While understanding the current ETA or warehouse inventory levels of a particular product is undoubtedly important, strategic decision making requires an understanding of how things are changing over time and what factors are causing the changes – ERP systems don’t make either of these things easy. 

To put it into context, knowing that the ETA for a container full of seasonal goods has changed is important, and your ERP system can tell you this. But to prevent it from happening again next year,  you need to know how much the ETA has changed and what went wrong to cause the delay in the first place. This might require an analysis of factors such as the routes, carriers, transshipment ports, days on quay and historical ETA slippage.

2. The data you need is controlled by your partners (and isn’t making it into your ERP)

Understanding the root causes of risk in your supply chain is further complicated by the fact that data points such as those outlined in our prior example typically live inside the proprietary portals of supply chain partners or across hundreds of spreadsheets buried in your email inboxes. 

In other words, much of the data you need to truly understand the drivers of supply chain performance and reliability isn’t even in your ERP!

As such, organisations tend to focus on manually updating a small subset of data points in the ERP that are deemed the most operationally important. Furthering the problem is the fact that manual processes often lead to issues of data accuracy. In some situations, investments may be made to integrate key data sources, but given the wide range of data controllers across the supply chain this ‘build it yourself’ model will almost certainly still lead to data dark spots. Thus, in either situation, ERPs are at best only providing a partial view of what’s happening. 

Supply chain visibility platforms (like Beacon!) help solve this supply chain data management conundrum by providing a plug and play supply chain data integration solution that unifies information from your systems and a wide network of data controllers into a single platform so you can get the insights you need to take control of your supply chain. 

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How to use big data to drive your supply chain

Once you’ve addressed the problem of supply chain data management, you will have set the foundation for reaping the wide ranging benefits of data analytics in supply chain management. 

Among other things, you’ll be well positioned to: 

Improve supply chain data sharing 

Knowledge is power. By bringing all your data into one place, you can equip supply chain partners with a single view of what’s important to them. This means less time going back and forth trying to figure out what’s happened, and more time focusing on solutions that minimise the impact of delays and disruptions. 

Hold supply chain partners accountable

When carriers and forwarders control your access to data, you’re letting them report on their own performance. This can make it difficult to hold them accountable for failures or poor performance. Taking control and ownership of this data means you can equip yourself with the insights you need to prepare for negotiations and compare the performance of partners in a consistent and objective way. 

Understand the drivers of unbudgeted costs

Still shaking off the sticker shock of your last demurrage and detention bill? Unifying your supply chain data allows you to identify failure points in your supply chain and understand whether the charge is stemming from a delayed haulier pickup at port, the warehouse being unprepared for its arrival or something else altogether. 

Leverage historical actuals to improve supply chain planning

Estimates are by definition imprecise. Aggregating and organising historical tracking data can help you understand how long it’s actually taking goods to move through your supply chain. Equipped with this information, you can improve supply chain planning by replacing assumptions and expected values with actual historical data, operate with more accurate lead times and hold partners accountable for underperformance when they aren’t living up to their promises. 

Understand risk concentration

Whether through strikes, extreme weather or political disruption, supply chains are inherently prone to disruption. Owing to this reality, it’s important to ensure you are spreading goods across a range of carriers, vessels and routes to ensure a single supply chain failure won’t bring your business to a standstill. Supply chain analytics can help you understand where your risk is concentrated so you can take corrective action with your freight forwarders and carrier partners. 

Optimise routes

Is one route causing persistent problems? Should you be paying premiums for refrigerated containers? You probably have some anecdotes and gut instincts telling you where the problem areas are and what you should do, but what’s better is being able to put numbers around the impact and magnitude of poor route reliability or crates full of bottles exploding when they’re being hauled through cold climates in the winter so you can make data-backed decisions. 

Set the foundation for supply chain automation 

Transitioning from data in spreadsheets to the mythical ‘automated supply chain’ isn’t going to happen overnight. More likely it’ll take years or maybe even a decade. But, quite simply, automation can’t occur without well organised historical data to feed the AI. Thus, developing a robust data infrastructure now is one of the single most important things you can do to prepare yourself for future automation (whenever it may become a priority). 

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Regardless of where you’re at in your supply chain analytics journey, tapping into the power of big data in supply chain management starts with getting all of your data in one place. Beacon can help you do just that. Get in touch to connect with one of our experts and learn how. 

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