Using Digital Twin Capability to Improve Retail Supply Chain Transparency

Digital twins – Bridging the physical and digital. This twin would enable one to collaborate virtually, intake sensor data and simulate conditions quickly, understand what-if scenarios clearly, predict results more accurately, and output instructions to manipulate the physical world.

Bridging the physical and digital

Digital twins can simulate any aspect of a physical object or process. Digital twin capabilities are not new. Since the start of this decade, pioneering organizations have explored ways to use digital models to improve their products and processes. The connectivity, computing, data storage, and bandwidth required to process massive volumes of data involved in creating digital twins were cost-prohibitive in the past, but things have changed since then. Digital twins are poised to transform the way companies perform predictive maintenance of products. Access to larger volumes of data is making it possible to create simulations that are more detailed and dynamic than ever.

Models + data = insights and real value

Digital twins can help optimize supply chains, distribution and fulfillment operations, and even the individual performance of the workers involved. Digital twin technology enables retailers to model their supply chains as connected environments. Retailers can model assets, warehouses, logistics and material flows, inventory positions, people, and processes. Real-time sensor and equipment data, as well as ERP and other business system data, feed the model to create a live execution environment.

Use Case : Regulatory and food safety

Customer pain: Lack of a real-time view into quality and efficiency issues

Benefits : Improve output/Food safety

Optimise the entire production process, take the upstream and downstream dependencies into consideration and determine the most effective improvements. Gain deep production visibility: Track products through multiple production stages to optimize processes and avoid bottlenecks.

Machine Applications

Criteria for digital twin –

  • High volumes of data availability
  • Complex decision process with multiple varied outcomes
  • High value for the business if even small adjustments are made
  • Scalable and repeatable across multiple locations, once proven

What can help?

Simulation:

The tools for building digital twins are growing in power and sophistication. It is now possible to design complex what-if simulations, backtrack from detected real-world conditions, and perform millions of simulation processes without overwhelming systems. Finally, machine learning functionality is enhancing the depth and usefulness of insights.

New sources of data:

Data from real-time asset monitoring technologies can now be incorporated into digital twin simulations. Likewise, IoT sensors embedded in machinery or throughout supply chains can feed operational data directly into simulations, enabling continuous real-time monitoring.

Interoperability:

Over the past decade, the ability to integrate digital technology with the real world has improved dramatically. Much of this improvement can be attributed to enhanced industry standards for communications between IoT sensors, operational technology hardware, and efforts to integrate with diverse platforms.

Visualization:

The sheer volume of data required to create digital twin simulations can complicate analysis and make efforts to gain meaningful insights challenging. Advanced data visualization can help meet this challenge by filtering and distilling the information in real-time. The latest data visualization tools go far beyond basic dashboards and standard visualization capabilities to include interactive 3D, VR and AR-based visualizations, AI-enabled visualizations, and real-time streaming.

Instrumentation:

IoT sensors, both embedded and external, are becoming smaller, more accurate, cheaper, and more powerful. With improvements in networking technology and security, traditional control systems can be leveraged to have more granular, timely, and accurate information on real-world conditions to integrate with the virtual models.

Platform:

Increased availability of and access to powerful and inexpensive computing power, network, and storage are key enablers of digital twins. Having Cloud-based platforms, IoT, and analytics capabilities will enable all to capitalize on the digital twin’s trend.

Realizing digital twins’ full promise may require integrating systems and data across entire ecosystems. Creating a digital simulation of the complete customer life cycle or of a supply chain that includes not only first-tier suppliers but their suppliers, may provide an insight-rich macro view of operations, but it would also require incorporating external entities into internal digital ecosystems.

Optimization is an extraordinarily complex issue that requires volumes of real-time data to support what-if scenarios on the fly will help to make faster, smarter decisions.

Real-time visibility provided by digital twins enables retailers to implement new non-linear supply chain fulfillment models like curbside pick-up or micro-fulfillment. The digital twin also creates an umbrella across siloed data enabling critical information to be available throughout the supply chain. With the pandemic and the increase in online shopping, retailers have had to accelerate their response for on-time delivery and to enable buying online and picking up in-store. Using digital twins, we can have a better understanding of where local supply chain challenges exist across your entire store footprint.

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Thought Leadership