Building a data-driven warehouse: Moving from manual tracking to AI-driven optimization

There was a time when traditional warehouse management was back-breaking and mind-bending. Warehouse staff endured manual labor storing, picking, and packing all kinds of heavy items around. Warehouse managers, lead hands, clerks, and other specialists drowned in piles of inventory paperwork that drained their mental energy.

But thanks to modern technology, warehouse and inventory management systems came about and ignited a digital transformation in the industry. Collection, storage, and analysis of data became much easier, giving rise to data-driven warehouses. Today, data is a central pillar of warehouse management and has paved the way for more sophisticated technologies like AI to streamline the fulfillment center and facilitate high-volume, high-velocity fulfillment. This has taken fulfillment to a whole new level, where same-day shipping is possible 99% of the time. 

Let’s look at the drawbacks of manual tracking and how leveraging big data and AI help optimize fulfillment. We’ll also discuss how Logiwa addresses modern warehousing challenges and share actionable tips for transitioning to a data-driven warehouse.

 

The limitations of manual tracking in warehousing

Fulfillment providers who still use manual inventory management bear the following challenges. 

Recurrent inventory inaccuracies due to human errors

Human errors, including counting and recording errors in manual warehouse processes, are among the leading causes of inventory fulfillment issues. It costs between $20-$60 to correct an error, and businesses spend about 20% of their total budget to correct logistical errors. 

These are the financial consequences for ecommerce retailers and 3PLs using manual inventory tracking. Worse still, human errors naturally rise as the intensity or amount of manual labor increases. This means a scaling company will incur more financial losses due to inventory inaccuracies. 

Besides the associated financial losses, inventory inaccuracies, such as packing and delivering the wrong products, will dent your customer experience and lower your reputation among consumers. Repairing reputation damage is particularly difficult for ecommerce businesses, considering that over 50% of customers abandon a brand and switch to a competitor after only one bad experience. 

Order processing and fulfillment inefficiencies

Order processing is intricate, even when automated. You can imagine how complex and exhausting it gets when processed manually, moreso, bulk orders of assorted items. Manually recording all stock-keeping units (SKUs) and double-confirming them when processing orders leaves room for errors. Such errors lead to order fulfillment failures, such as late or missed deliveries. 

Difficulty in scaling operations to meet increasing demand

As a company grows and the orders snowball, manual fulfillment becomes impossible unless it hires extra staff as needed. Hiring a ton of additional staff is expensive and eats into the company’s profit margin. This makes it exceedingly difficult for a retailer to scale organically and enjoy decent returns.

Embracing big data in warehouse operations

Big data is the compilation and analysis of vast amounts of warehouse data to gain insights that help you optimize and streamline warehouse fulfillment operations. There are numerous sources of big data in a warehouse setting, including: 

• Product information derived from warehousing documentation such as waybills, goods release notes, goods received notes, stock cards, and inventory ledger. 
• Sales transactions and customer behavior data derived from the customer relationship management system (CRM) and the order management system. 
• Internet of Things (IoT) devices like cameras, sensors, and RFID tags that record and store inventory information and warehouse temperature data. 
• Past, existing, and forecasted market trends derived from data analytics and retrieval.

The benefits of leveraging big data analytics in fulfillment centers

The top advantages of utilizing big data to facilitate AI-driven warehouse optimization include:

Enhanced real-time inventory tracking

Big data helps you review and track inventory automatically to know when to restock, what items to replenish, and the sufficient amounts to stock. It helps you avoid common inventory problems such as overstocking or understocking. 

Improved decision-making capabilities

With the hyper-competition in the ecommerce sector, fast and accurate decision-making can make or break a retailer. Leveraging big data from your inventory-tracking IoT devices and other big data sources helps you make informed decisions, such as when to scale your business and the technologies to invest in. 

Identification of operational bottlenecks

Analyzing big data for consecutive months shows you performance patterns. By analyzing low-performing periods, you can establish and resolve the bottlenecks behind the lulls and resolve them to enhance operational efficiency. 

Integrating AI for advanced warehouse insights

It seems everyone has jumped on the artificial intelligence band wagon. And lots of the talk is just that. But in the world of high-volume fulfillment AI is making real contributions. These are some prominent roles of AI in warehouse management: 

• Predictive analytics for demand forecasting: AI and machine learning models allow you to analyze historical warehouse data to predict inventory demand. Such insights guide your inventory ordering frequency, thereby optimizing your inventory carrying costs. 
• Automated inventory tracking systems: They feature high-tech tracking technology like barcode scanners and IoT devices to track inventory across all locations. They help you perform crucial roles like tracking multiple warehouses, setting reorder points, and synchronizing numerous sales channels.  
• AI-driven robotics for picking and sorting tasks: AI facilitates the operation of robotics that replace human pickers and packers on the warehouse floor. 

Here are just two examples of how AI isn’t just talk, but has contributed to dramatic changes for leading companies:

• Amazon: Deployed over 750,000 robots to streamline warehouse operations. Amazon’s Shreveport, Louisiana, fulfillment center is one of the world’s largest robot-operated warehouses.
• Butterball: Leveraged advanced data analytics to review their 50+ years’ worth of customer data to deliver cook-from-frozen turkey.

Logiwa’s AI-driven solutions addressing warehouse challenges

Here’s how each of our specialized features helps you tackle everyday warehouse issues:

AI job optimization

AI-driven job optimization utilizes picking wave automation to set up job creation rules for smart jobs, from picking to cycle count and replenishment jobs. This smart job creation approach yields a 58% increase in operational efficiency.

Smart automation

Smart putaway algorithms help put every inventory component in the most convenient and optimized space, ensuring fast retrieval. This means less walking time, minimal congestion, and fewer packing errors and damaged products. Other smart features like automated order fulfillment and AI-optimized picking and packing streamline operations, allowing businesses to scale without adding headcount.

Real-time data analytics

Real-time 3PL data analytics allow you to accurately simulate demand forecasting, track key performance metrics, and make data-driven decisions to support your gathered inventory insights. Making smart data-backed decisions helps you leverage relevant industry data to edge competitors and increase your market share.

5 steps to transition from manual to data-driven warehouse operations

1. Assess current operations and identify pain points and inefficiencies in existing processes.
2. Invest in data infrastructure by implementing IoT devices and sensors for real-time data collection and embracing warehouse execution systems (WES).
3. Develop AI capabilities such as machine learning and robotics to automate warehouse activities.
4. Train your workforce and then equip them with the necessary tools and equipment to work effectively with AI technologies.
5. Monitor performance metrics and fine-tune where necessary to achieve optimum results.

Future proof operations with  a data-driven warehouse and Logiwa IO

Selecting the ideal tech stack for your warehouse operations can be challenging with the many choices available for building data-driven warehouses. Even when you get it right, prioritizing implementation is another challenge. Fortunately, our fulfillment experts can help.

Schedule a demo today and let one of our fulfillment experts help you kickstart your transformation journey.
 

FAQs on building data-driven warehouse operations