Transforming Warehouse Operations using Low-Code Digital Twins

A strategic use for digital twins in the warehouse industry would be to simulate potential impact from internal and external variations. It helps to identify how complex customers can impact team member efficiency or how new processes could affect downstream departments or customers.

Streamlining warehouse operations using digital twins comes through real-time monitoring and actionable insights. An interesting use case would be to identify bottom performers and their impact on turn times. Analyzing lower than expected inbound volumes can result in making better staffing decisions and optimize fulfillment.

Over the last decade, the supply chain has raced through technological advances searching for untapped efficiencies and cost savings. With these advances, organizations have become more transparent and generating more data than ever before. While upgraded ERP’s, WMS, and labor management systems provide more transparency, they fail to deliver on the true potential of the data being captured. 

Utilizing a digital twin is the next step in the evolution of the modern warehouse. Streamlining warehouse operations and identifying process changes has never been easier when you have real-time monitoring across an entire facility. Whether we want to monitor individual performance at a facility or identify how new customers will impact processes, digital twin technology provides these actionable insights. In addition to monitoring internal variations, the digital twin strategy allows us to simulate external variations and identify their impact to an organization’s supply chain. 

See More: The Low-Code Buzz and How Businesses Can Avoid the Pitfalls of Automation

Challenges the Warehouse Industry Is Facing in the New Decade

Due to restricted movement, social distancing, and worldwide fear prevalent amidst the pandemic, and its associated protocols, there is a huge labor shortage being experienced across all sectors. The new age of disruption has brought transformative changes in all industries, including supply chain and logistics. Businesses and organizations are facing challenges associated with raw materials sourcing, transportation availability, environmental concerns, demand fulfillment, etc.

With consumers mostly restricted to their homes, there is a rise in the demand for digital platforms and e-commerce activities. New systems are needed to efficiently manage and serve online orders and deliver goods on time. E-commerce fulfillment now requires advanced platforms and solutions that can meet the influx of consumer demand by optimizing logistics, routing, streamlining warehouse operations, and simplifying decision-making on the go while minimizing negative ecological impacts.

The COVD-19 pandemic has drastically changed the landscape in how organizations do business. The most drastic of these changes are in the supply chain and logistics industry. Finding ways to meet customers’ new expectations and react to a constantly changing landscape has been a prevalent challenge.  

In addition to accommodating the ever-changing COVID protocols, organizations have had to deal with one of the largest labor shortages with what has been dubbed “the great resignation”. A lack of labor coupled with increased demand in all sectors has created a daunting storm for organizations to navigate through. Transportation capacity, both domestic and international, also continues to stretch manufacturers and distributors. Organizations are looking for creative, cutting-edge solutions to these issues while maintaining their sustainability target. 

Current State of Warehouse Systems

A host of warehouse tools, technologies, and solutions developed in the past decade, such as WMS, TMS, LMS, YMS, ERP, etc., are available to streamline industry operations. But do they fit the current evolving landscape of modern warehousing? Though these systems provide digitization of data collected throughout warehousing processes, they lack the power that Artificial Intelligence and Machine Learning (AI/ML) bring. A simple example of these complexities is that WMS can be integrated with management systems and used to assign tasks to the workforce. However, it is devoid of ML and so lacks the ability to prioritize the most efficient tasks first.

The Double Benefit of Low-Code Digital Twins 

Let’s understand how low code digital twins can elevate the current state of warehouses and bring the transformation necessary for business sustainability.

Utilizing a low-code digital twin can help organizations create custom simulations quickly and easily. A good low-code platform can help generate valuable insights through a digital twin. Moreover, it will also allow for easy maintenance and updates to the digital twin. Developing custom digital twins with the speed of a low-code environment can help scale from one warehouse to a network of distribution centers quickly. This enables effective ways to monitor supply chain networks actively and to avoid potential bottlenecks.

Apart from this, there are several other use cases in the warehouse industry where digital twins can benefit –

• Strategic building of new facilities – Utilizing a Digital Twin, one can conduct a center of gravity style analysis to find the best geographical location for new facilities.
• Smart Task Allocation – In conjunction with a Digital Twin, one can utilize tools such as a Smart Task Allocation solution to identify the best team member to complete each task.
• Intelligent Appointment Scheduler and Sustainability – Tools like the Intelligent Appointment Scheduler ties in great with a digital twin and can optimize inbound and outbound operations across an organization.

See More: Can Oracle’s Cloud Data Warehouse Spark Self-Service Data Warehousing Trend?

Data Governance and Compliance Practices Make a Difference

As with any data project, pipelines of valuable information must be in place and maintained to ensure the accuracy of the information provided. A robust data governance culture is recommended to ensure that data is captured consistently across an organization to harness the full power of digital twins.

How Digital Twins Can Boost Warehouse Efficiency

One of the 3PL managers was directed to cut all overtime immediately. Many factors beyond their control led to inefficiencies for their team. These issues were not going to be resolved overnight. An inability to visualize and communicate the impacts of such a drastic shift in staffing was apparent. Apparently, they were thinking about the 20 inbounds that probably wouldn’t be received without the staff. The impact at a facility level could not be assessed. In the end, this no-overtime directive led to a lack of customer service since inbound loads outgrew the outbound turn times. As a result, the following solutions were found: improving the efficiency of the mature staff, building better training processes, and ensuring that orders would not exceed projected labor requirements. Sadly, they learned this lesson the hard way by falling short of customer expectations. 

What if there was a way to accomplish this without risking the customer base?

A digital twin would help understand not just the financial impact, but also the effect on their customers while configuring their staffing to just straight hours across the week and integrating inbound data. Simulating this could help determine exactly where and how customer expectations would be met. This tool may not have changed the directive, but only modified it. In addition to identifying bottom performers, a digital twin can provide more insight into inefficiencies. 

Having all parties use the same data is the key to transparency and simulation provided by a digital twin. What if we wanted to understand the impact of external variations? What if Texas went dark or the Suez Canal was shut down temporarily? The simulation can be done using inbound information, lead times, and other segments. Having this visibility can predict actions such as identifying alternate ports and selecting alternative shipping modes of transit or finding suppliers in different geographies.   

While the example explores the simulation phase, the response to this specific issue is still reactionary. How can a digital twin provide proactive responses to variations across the supply chain? Using the example above, we can monitor warehouses in real-time using a digital twin. Identifying performance issues, bottlenecks, volume fluctuations, and complex loads ahead of time could have prevented the issue in the first place. It is often difficult to track individual user performance within labor management systems. 

By measuring team efficiency against volume, a digital twin can provide a comprehensive view of results and how they relate to the business unit. Team member performance and load complexity were driving overtime in specific areas.  It is possible to monitor the whole business unit in real-time, providing insights that would otherwise require significant resources and time. Let’s imagine implementing a digital twin for a larger network of distribution centers and a varied e-commerce customer base. Only real-time monitoring will help visualize the bottleneck at the distribution center to enable effective managerial decisions to eliminate this backlog.  

Digital twins provide two primary functions across a business organization – simulation and monitoring. Using these tools together can provide limitless pain points in the supply chain and help find the best ways to solve them without impacting current performance.

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