• Andy is tired and irritable. He drove into the warehouse in Chicago on Friday night and thought he would be first in line for an empty dock on Monday. However, he found a long queue, as other carriers had also sent trucks to catch the Monday morning slot in the first-come-first-serve facility.
  • Ken is the supervisor at a warehouse in Philadelphia. It’s Friday evening and he has let off some experienced hands, as there is a “no show” of expected trucks—drivers were out of reach and the carrier mentioned a snowstorm. Suddenly, the fleet of trucks with mixed pallets arrive and he is faced with heavy detention charges.
  • David owns a huge food processing unit. Christmas orders are pouring in. Having invested in an inventory forecasting solution, he is confident about meeting customer demands. Unfortunately, he has neglected the capacity planning of his warehouse—the yard is congested, inventory is missing, products are damaged, orders are mismanaged, and labor is overworked.

All the above scenarios are repeated across many supply chains, even though there are ready technology solutions at hand. Artificial intelligence and machine learning have the power to transform warehouse operations with data analytics. Advanced analytics of data is already being used by NextGen warehouses globally to revolutionize warehouse activities. Some solutions are:

  • Smart Appointment Scheduling
  • Predictive Inventory Management
  • Intelligent Task Allocation
  • Effective Demand Planning
  • Optimum Capacity Utilization
  • Continuous Process Optimization
  • Organized Logistics Planning


Smart Appointment Scheduling

Machine learning reviews historical data of orders, appointments and schedules at a warehouse. After sifting through the scheduled and arrival time of trucks, patterns are detected—say a chronic latecomer carrier, or weather-related delays. An algorithm derived from the pattern is refined with deeper insights gained from the analysis of the data, including various parameters like order complexity, the effort required to process it, warehouse load and so on. The ML model is trained to forecast the best schedules, with various simulations of “what if” scenarios before an intelligent appointment scheduling AI solution is formulated. 

Warehouse staff who need to schedule appointments can toggle the choices available to choose a recommendation. The “smart” scheduler not only books the appointment but also adjusts the entire schedule for the day, or the direction (inbound/outbound) of activities; it shows the number of appointments that can be fitted into the same calendar slot, reflects the picking status, indicates reasons for delays, flags inconsistencies that need attention, keeps tabs on active and inactive hours, and identifies best opportunities to service late arrivals. All of this is done without compromising the turn times of other carriers. 

Benefits: Reduction in turn-time, accuracy in scheduling, reduction in delays, automated scheduling, savings from avoidance of detention charges, optimized warehouse load through the day, labor distribution, scheduled appointment emails or phone messages to drivers/carriers, drivers’ wellbeing, safe and speedy shipment handling


Predictive Stock Inventory Management

Customer demand, raw material demand from the shop floor, orders from retailers and shelf-life of products impinge on inventory management. Even risks that surface across the supply chain, consumer sentiments traced through social media, and public perception of manufacturers, suppliers or any other stakeholder can adversely affect inventory management. So, keeping tabs on a diverse set of data and making sense of it proactively, is predictive analytics.

To implement predictive stock inventory management, very large datasets of historical and current data are aggregated, filtered, analyzed and modeled. Constraints—such as extreme weather conditions and spikes seen in point-of-sale data—are introduced to prompt machine learning algorithms to “learn” to predict based on more recent history. As the stress is on the high visibility of data, it is possible to bridge the gap between merchandising managers who make purchase decisions and warehouse managers. 

Data from many applications including warehouse management system, vendor inventory management, market basket analysis, distribution order management and supply chain planning help to simulate and predict outcomes, discover insights that enhance collaboration and develop agile inventory management strategies. 

Parameters like demand patterns, upcoming shortages, wastage, the total cost of ownership of inventory, scaling to include data from third-party suppliers, SKU counts and slotting adjustments are also employed to encourage predictive inventory building.

Benefits: Effective cost-cutting, insightful decision making, better collaboration between processes and stakeholders, strategic sourcing, prevent excess stocks, scalability for growth, demand generation, and improved customer satisfaction and experience.


Intelligent Task Allocation

A warehouse is a beehive of activity, and unless every task—small or big—is carried out safely, efficiently and in a timely manner, things can go very wrong. The manual method of task allocation is heavily dependent on supervisors’ experience, discretion and business rules. A real-time task allocation AI system can be trained to assign tasks to the right person and increase labor efficiency. ML is employed to categorize tasks based on different criteria. 

At the warehouse these criteria are typically tied to parameters like the product, distance, location, labor skills and past performance. The ML model can be trained to run historical data and simulate different task and assignee combinations to determine the best fit. Both staff productivity and time can be effectively utilized with further fine tuning of the algorithm. For instance, intelligent task interleaving recommendations are thrown up as the ML algorithm detects a worker is allocated a task in a zone of the warehouse where similar tasks await. The worker can perform multiple “similar” tasks in the same zone. Similarly, picking and delivery can be carried out simultaneously if located in proximity. 

As all the permutations and combinations of workers and tasks can be viewed on an intuitive dashboard, a supervisor can plan task allocation for the entire day based on demand and staff availability. Robots can also be assigned tasks.

Benefits: Optimize staff capacity, improve task performance, reduce operational bottlenecks, high efficiency per shift, safe and damage-free handling, avoid wasteful overtime costs, prevent understaffing and overstaffing issues, reduce costs, increase productivity.

Effective Demand Planning

Data collection and analysis is central to demand forecasting and planning. In the warehouse, it is important to accurately predict the number of products to be stored per SKU, the space needed to store, the labor required and the number of work shifts needed. It is even more important to align external suppliers to the timelines of production processes while carrying out deliveries. It is not possible to forecast all future events, but demand planning employs various parameters to cover all contingencies. For instance, demand in the short-, medium- and long-term; historical sales data are broken down into previous months, quarters, and years; seasonal demand during Christmas, Thanksgiving, Easter, and so on.

It is impossible to predict the future performance of a product or service in terms of earning profits or suffering losses as the factors that influence demand are many: economic, political, social, environmental, technological and legal.

ML applies complex mathematical algorithms to big data to detect demand patterns, establish relationships between data sets, and sense demand signals. 

A well-trained algorithm (that is fed with historical data and what-if queries) will recommend a suitable level of safety stocks to tide over contingencies. The scope of events and promotions can be determined by ML algorithms that share insights on consumer preferences, price sensitivities and other “motivational” factors that inspire buying.

Benefits: Efficient supply chain, optimal stock levels, smooth and organized production, healthy customer and supplier relations, no stockouts and obsolescence, optimization of storage space, minimization of warehouse costs.

Optimum Capacity Utilization

Physical storage capacity does not always translate to capacity utilization in a warehouse. Optimizing capacity utilization is the ability to use storage based on the height and dimensions of products, the inventory needed, and the space needed by workers to move around freely and safely while locating items speedily and fulfilling orders efficiently. In addition to WMS data, regular inspections of warehouse functions help to detect irregularities. Advanced computer vision techniques are used for inspections that yield deep insights.

An ML algorithm developed to rectify the anomalies and suggest remedies will provide insights that can be used to model optimal capacity utilization. Tracking of KPIs of inventory, receiving, picking, safety, put-away, safety and performance will throw light on the holistic improvement of warehouse capacity—not just space but also operations like storing, fulfilling and replenishing. 

Suggestions like cross-docking (moving an item straight from receiving to shipping) warehouse slotting (storing and organizing inventory based on SKU or product characteristics) and layout changes made by the algorithm can be assessed and accepted by warehouse managers. AI models are able to co-relate demand forecasting with capacity utilization and hence recommendations may be made to optimize functions like number of working hours, hours spent on picking and availability of staff etc., to bridge gaps.

Benefits: Improved work process flows, time and money savings, the safety of person and product, smooth sales, better organization, vendor confidence, lower operating expenses, inventory visibility, automation, optimized supply chain

Continuous Process Optimization

In the context of a warehouse, there is scope for optimizing the picking process which comprises 55% of the total operational expenses. Machine learning has been employed to optimize picking operations. Various parameters like product dimension, picking cart and route, storage location, and walking distance are “tested” in different combinations to arrive at an optimal solution. WMS data is tapped to group similar orders and suggest automated picking lists; as there are many innovations introduced in warehouse racks, pallet stacking and conveyor systems to sort orders, the process optimization is continuous.

Process recommendations may include productivity enhancing techniques like zone picking, cluster picking, batch picking, discrete order picking and combinations of these picking methods. In the food industry, picking can be further adapted to satisfy order profiles that cater to single-serve or mega packs. 

Master data, order lines and layout coordinates are also analyzed. New technologies like barcoding, RFID, voice picking, pick-to-light, wearables, and robotic arms can work with data analytics to improve operational efficiencies. 

For the food industry, in particular, automated storage and retrieval solution is the preferred choice to prevent touch and contamination. A combination of robotic palletizing and depalletization and automatic guided vehicles are used. 

In very large warehouses with a variety of products, the digital twinning of the entire process with a virtual new floor plan, new workflows and variations of every aspect of warehousing functions can be simulated first. Both process and asset data contribute to process optimization through a digital twin. The insights derived from the virtual simulation are translated into actions. A model algorithm based on this exercise is then introduced in the physical world process twin.

Benefits: Reduction in mistakes, improvement in cost efficiency, data-driven decision-making, improved performance, streamlined operation, better ROI, reduced downtime.


Organized Logistics Planning

Logistics involves organization, movement and management in a warehouse. As it includes policies, the flow of physical goods, and abstract time and information, it is very challenging to organize it smoothly. It means tracking trucks, pallets and products. Fortunately, data is also available from different sources.

ML uses data from ERP, route management, material management, order management systems and IoT sensors to compute new algorithms for better vehicle and cart routing. Data from vehicle diagnostics, driving patterns and location information are tracked in real time. Analysis of supplier data, including the on-time delivery, condition of goods on delivery and customer data involving the return of goods, is made. 

Together with transportation and warehousing KPIs such as cost and utilization, the quality of logistics is assessed and recommendations are made to further organize and plan logistics for improved performance.

Data is also gathered from cobots, robots and automated guided vehicles in warehouses and insights help to further automate several tasks.

Benefits: Safe, cost-effective transport, route optimization, accurate inventory placements, real-time inventory counts, reduction in returns, auto-replenishments, maximized warehouse space, stock traceability.


Conclusion: In short, the new generation warehouse will have data and machine learning algorithms as its foundation. Many new layers of technologies and hyper-automation may be built that catch attention. But remember, beyond those robots, drones, automated guided vehicles, digital twins, blockchain, driverless forklifts, and automated storage and retrieval systems, there is data. This data is continuously being collated, cleaned and mined for prescriptive and predictive insights; and the next-gen warehouse will continue to be run on remodeled iterations of machine learning algorithms that are gaining super cognitive powers.