Author: Extensiv Jan 19, 2024 9 Min READ

Supply Chain Predictive Analytics Explained

9 Min READ
Supply Chain Predictive Analytics Explained


How Predictive Analytics Can Optimize Your Supply Chain

With economic uncertainties, evolving customer demands, market changes, and political unrest around the globe, warehouse managers and third-party logistics (3PL) providers often struggle to accurately track and manage inventory as well as forecast demand for more effective data-driven decisions.

When supply chain processes stall, as it did during the pandemic, this can leave these businesses–and the retailers, ecommerce businesses, and brands they support–in a precarious situation. Too little inventory can lead to unhappy customers and lost business. Too much inventory takes up valuable space on shelves and ultimately higher inventory management costs.

See how Extensiv drives the modern supply chain

This is compounded for those who don’t use warehouse management system (WMS) software and have no real-time visibility into their inventory levels or incoming and outgoing orders. However, those who have implemented WMS or similar inventory management software will be more empowered to overcome many of these supply chain challenges. That’s because these logistics solutions provide insight–and data–into inventory, order fulfillment, and other warehousing and logistics metrics, which when used in tandem with predictive analytics, help you more effectively manage inventory replenishment and optimize warehouse space to ensure you can meet customer needs, even in real time as those demands flex and scale.

What is predictive analytics in the supply chain?

Predictive analytics models enhance supply chain visibility beyond demand forecasting. Supply chain predictive analytics takes a look at a bigger picture, for example, past sales, inventory trends, and other relevant historical data to make more than just educated guesses. Predictive analytics utilizes real data across key operations and external sources to support business decision-making so you can optimize everything from your inventory and warehouse space utilization to routes and shipping decisions, all the way through better budget planning, network and processes optimization, and more.

Predictive analytics, especially when used to look at potential supply chain risks, can help reduce costs, streamline workflows, and ensure you always have enough products in stock–but not too much–to keep consumers happy.

How recent world events have shaped supply chains in 2024

While the supply chain has long been considered fragile, nothing highlighted that more in recent years than the pandemic. Routes clogged and stalled. Products were in short supply or impossible to find. Out of fear, brands stockpiled products they could get their hands on, in many cases, overstocking and filling warehouses to capacity.

While much has returned to what’s our “new normal,” the reality is it’s not out of the realm of possibility it could happen again. From political unrest to extreme weather conditions, world events can have a tremendous impact on a tenuous supply chain. What happens in one part of the world can have a ripple effect on others.

Supply chain disruptions and their ripple effects

A seemingly minor disruption in one part of the supply chain can trigger an avalanche of events, with far-reaching consequences across industries and economies. These ripple effects are often complex and unpredictable, highlighting the interconnectedness of a globalized supply chain. Yet, predictive analytics enables you to navigate the complex web of an interconnected supply chain, so you can anticipate these potential disruptions, simulate scenarios, and build a more resilient and agile logistics network.

One of the most immediate impacts of a supply chain disruption is product shortages. When a component or raw material is unavailable, production slows or can grind to a halt. This can lead to empty shelves in stores, warehouses, and distribution centers, which all eventually culminate in frustrated consumers and lost revenue.  

To compensate, businesses often source materials from alternative sources, often at higher costs. This, in turn, ripples down to consumers as higher prices fuel inflation.

Case in point: In 2021, a container ship got stuck in the Suez Canal, a heavily traveled trade route. While the incident was limited to a single location, it disrupted the global supply chain for weeks.

And, because industries are increasingly interconnected, a disruption in one can have impacts on others. For example, during the pandemic, there was a semiconductor shortage that impacted everything from automobiles to electronics. That then had impacts on industries such as healthcare and tech because they needed these elements for their products.

Here’s another example: A disruption in critical infrastructure, such as water or electricity, can lead to outages and price spikes, which can impact everything from businesses to personal lives. Or, a natural disaster in one part of the world, such as an earthquake, could impact businesses on the other side of the globe that depend on export or movement of products in those areas.

Shifts in consumer behavior

Shifts in human behavior can also impact supply chain resiliency. Consumer preferences, societal trends, and global events can trigger unexpected  changes in demand and disrupt a delicate supply chain balance.

One of the most talked about recent examples is the toilet paper shortage in 2020. As consumers rushed to buy–and hoard–toilet paper, it illuminated an unforeseen supply chain weakness for consumer essentials. It also highlighted issues with sustainability across the supply chains when demands peak rapidly and unexpectedly.

Predictive analytics tools could have helped prevent–or at least limit–the impact of this event. By using data-driven insights and scenario planning, businesses and logistics providers could have potentially predicted the possibility and would have then been able to implement contingency plans to address shortages before they became too widespread or far-reaching.

How predictive analytics can solve modern supply chain challenges

Predictive analytics can help address these and other challenges of a complex and unpredictable interconnected, global supply chain.

Advanced forecasting

Traditionally, forecasts relied on historical data, instinct, and a lot of educated guesses. Unfortunately, the outcome was often based on a narrow view, resulting in overlooked unpredictable events and failure to identify future trends. Predictive analytics, however, can help logistics providers and warehouse managers with more accurate advanced forecasting well beyond basic historical data.

With predictive analytics, you can get insight into a range of potential scenarios (for example, a natural disaster or political unrest) and use other relevant data such as weather forecasts, economic indicators, market trends, and competitor strategies to paint a much clearer and more reliable picture of future demand and potential disruptions–based on real data. When you use warehouse management tools and capture your own data, predictive analytics comes even more into focus, providing insight that’s specific to your business’ unique needs.

Most predictive analytics tools also utilize machine learning (ML) and artificial intelligence (AI) tools to help uncover patterns and find interconnectivity amongst supply chain data and scenarios that otherwise might be overlooked. This further fine-tunes forecasting for everything from product demand and inventory needs to potential transportation issues, shipping routes, consumer satisfaction, seasonality, holiday impacts, and more.

By understanding potential scenario impact and other trends, logistics companies can develop proactive contingency plans and adapt operations to limit negative impact.

Real-time inventory optimization

Inventory management has long been a game of chance. Without comprehensive insight into datasets across operations, logistics providers often make educated guesses about how much or how little inventory they may need throughout the year. This usually results in either overstocks or understocking. Predictive analytics eliminates the guesswork by adding real-time business intelligence and proactive supply chain planning to the equation, especially for 3PLs and other warehouse operators that traditionally use paper or spreadsheets to manage inventory.

Predictive analytics collects data from multiple sources such as historical sales patterns, real-time customer orders, seasonal trends, and other factors like weather events and competitor actions. This leads to more accurate forecasts for inventory and demand, even at a granular level such as specific product variations or warehouse locations. You can even use it for predictive maintenance to ensure all of your equipment, systems, and devices are always fully operational.

If you’re using a WMS with analytics capabilities, the software can automatically and proactively adjust inventory levels based on these forecasts, eliminating stockpiles that take up valuable space. With predictive analytics and warehouse automation, you can dynamically adapt inventory levels to meet future demand and decrease waste and unnecessary expenses.

Employee predictive risk modeling

Employees are the heart and soul of warehouse and logistics operations. Employee predictive risk management uses historical and real-time data paired with predictive algorithms to identify potential employee issues before they become problems, such as safety risks, performance metrics, employee dissatisfaction, and turnover rates.

With predictive analytics, you can get much-needed insight into employee risk. For example, you can look closer at accident trends, the work environment, potential hazards, and employee performance. This can help you identify which employees may be at a greater risk of a safety issue so you can proactively intervene with education and additional training before an incident happens.

You can also use these analytics to analyze employee attendance issues and correlate them with potential trends, for example, weather events or during certain times of the year, to address emerging issues before they disrupt workflows.

Optimize distribution networks

It can be challenging for logistics providers to optimize their supply chain networks and increase operational efficiencies if they don’t have actionable insights into potential disruptions, inventory levels, order fulfillment metrics, and demand fluctuations. Traditionally, most 3PLs have tackled this with reactive strategies, which often limit response tactics because they can’t predict every type of disruption. And, supply chains, especially in the intricate realm of fourth-party logistics (4PL) networks, are complex with many uncertainties. But, predictive analytics can help providers get their arms around most disruption types so they can build strategies to optimize their distribution networks.

With insight into these possible disruptions, logistics providers can develop plans to respond and make adjustments for everything from changing suppliers as needed to making updates on transportation routes and shipping options.

Predictive analytics can also help 3PLs that manage multiple warehouses, as well as 4PLs that have expanded logistics networks, increase collaboration and communication across the entire supply chain and order fulfillment ecosystems. These analytics tools can give all stakeholders real-time visibility into potential bottlenecks and identify potential risks, capacity issues, and cost fluctuations so key players can adapt strategies and not react blindly in crises.

Enhance supplier relationships

While the human factor will always be one of the most important parts of supplier relationships, predictive analytics can certainly enhance them. One of the biggest benefits is breaking down data silos that have traditionally created blind spots in the supply chain. For example, some warehouse management tools have dashboards you can share with your suppliers to provide them with the same real-time metrics you can see. This can improve collaboration and create opportunities to work together to proactively see and address issues before they create problems, even often overlooked external trends and factors.

Another benefit of these analytics is you can use data such as delivery times, quality control, and costs to identify areas for improvement. When both parties can see the same data, it facilitates opportunities to develop improvement plans and process optimizations that can have positive impacts on all of your supply chain operations.

Unlock the secrets to prevailing in the world of ecommerce and logistics in our  exclusive webinar – hear Rick Watson’s unique perspective and actionable  insights for leading in the next era of commerce.How to implement predictive analytics

If you’re still using traditional methods to manage your warehouses and supply chain, or you’re still adapting to a WMS or other inventory management software, now is the time to embrace the power of predictive data analytics so you can make your operations more efficient, save time, reduce expense, and automate tasks to free up your employees to focus on more important tasks. Not sure where to begin? Here are seven steps to help you ditch the paper and embrace predictive analytics in supply chain management:

1. Audit and assess processes

Conduct a thorough audit of your key operational processes including manual tasks, data sources, and existing supply chain pain points. Identify areas where existing processes create blind spots and decrease efficiency.

2. Set scope and priorities

Establish metrics and key performance indicators. Determine the scope (objective) of what you want to accomplish by adopting predictive analytics. How can predictive analytics address your existing pain points–for example, develop more accurate demand forecasting and optimize inventory management and warehouse activities? Then set priorities and align them with your business goals and supply chain objectives.

3. Implement the right software

Invest in advanced supply chain software–for example, a WMS and/or inventory management platform with built-in predictive analytics capabilities. Research options and prioritize solutions that seamlessly integrate with your existing systems and operational infrastructure.

4. Understand your data

Understand what types of data you want to collect and how your software manages that data–for example, historical data, sensors, sales and orders, inventory metrics, and market data. Ensure your data is accurate, consistent, and accessible and integrates with your predictive analytics tool.

5. Scale up as needed

Get started with the most important data you need to reach your preliminary goals. When people talk about digital transformation, AI, and machine learning, they conjure big data and mistakenly think they must adopt an “all-or-nothing” approach to data collection. Sure, the more data you have available the more accurate your predictions may be, but you don’t have to take an all-in approach to get started on your predictive analytics journey. You can start small and then scale as you need to. Your project scope should help you here.

6. Build a data-driven workforce

Build a data-driven workforce by explaining why relevant data is important, how and when to collect and use it. Data without context loses its value. Your employees play a critical role in ensuring you’ve got the data you need and that it’s accurate. Talk to your teams about the benefits of predictive analytics and encourage collaboration and communication across your entire organization to ensure success in meeting your data and analytics goals.

7. Measure, Understand, Adjust

Adopting data analytics is an ongoing journey for your warehouse or logistics operations that doesn't end once you have all the systems in place. The data you collect will change over time and so will how you use that information. Going forward, as you continue to analyze your data and understand it, you may need to adapt your processes and practices over time to evolve with your business goals, changing customer behaviors, and market demands.

Using Extensiv to future-proof your supply chains

Extensiv’s powerful data analytics tools are the perfect companion to help you build a resilient supply chain, whether you’re managing warehouse operations on your own or you’re a 3PL or 4PL provider managing logistics operations for multiple customers.

Extensiv’s order management solution for ecommerce brands provides you with unparalleled visibility, insight, and control across your entire business with advanced demand forecasting functionality and a dynamic sales analytics dashboard.

With more visibility into your warehouse operations, you can get more insights to set inventory thresholds and forecast growth and lead times. With advanced demand forecasting, you can also optimize inventory and avoid stock outs or overstocking that can negatively impact your bottom line.

Furthermore, Extensiv's 3PL Warehouse Manager can also help supply chain professionals follow real-time demand analytics and other inventory and warehouse metrics to ensure optimal stock availability without excess inventory holding costs of stockouts, leading to more efficient warehouse operations.

The software also has transportation management system (TMS) features so you can do things like consider real-time traffic conditions, carrier performance, and weather forecasts to create efficient routes for every order fulfillment.

And, with Extensiv, supply chain leaders can seamlessly share data throughout your business and with key stakeholders and even your customers to ensure more collaboration, enhanced communication, and proactive solutions to today’s most common supply chain challenges.

Interested in learning more about emerging (software) technologies that will revolutionize the supply chain? Read our 2024 State of the Third-Party Logistics Industry Report for insights into this year’s top logistics trends!

FREE REPORT Proven Ways to Improve Warehouse Profitability Get the guide for a five-point warehouse tune-up.  

Supply Chain Predictive Analytics FAQs

What is an example of predictive analytics in logistics?

An example of predictive analytics in logistics is using data to proactively set inventory levels based on historical data, patterns, and trends.

How does predictive analytics improve demand forecasting?

Predictive analytics goes beyond sales data to analyze trends, historical data, patterns, real-time order and inventory information, as well as external factors to accurately predict usage and needs and peaks/dips, so you can optimize inventory to avoid stockouts or excess stock.

What are the key steps to implement predictive analytics in supply chains?

Some key steps to implement predictive analytics in your supply chain include setting goals and assessing data sources, implementing a WMS or inventory software with built-in analytics that securely integrates your data, and continuously improving your data analytics processes.

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