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Digital Twin - Rapid Iteration of Agri-food Tech Products

  The global pandemic has accelerated changes in consumer eating habits and preferences, necessitating different product designs and SKUs. Agri-food tech startups are increasing market share through product innovation, and continued competition within the food space puts pressure on brands of all sizes to rapidly iterate products and processes.

  A new technology tool is emerging that can help agri-food companies navigate all these changes while simultaneously tracking consumer demand and product details, providing actionable insights for brands in their operations as well as their supply chains. This new tool is the "digital twin".

  Digital twin, or translated as digital mapping, digital avatar, and digital twin, refers to the simulation of physical entities, processes or systems in the information platform, similar to the twins of physical systems in the information platform. With the help of digital twin technology, the state of the physical entity can be understood on the information platform, and even the predefined interface components in the physical entity can be controlled.

  The famous IBM is committed to investing in and developing digital twin application scenarios, including software as agri-food companies and manufacturers. In IBM's view, digital twins are virtual models designed to accurately reflect physical objects. Guided by large amounts of data, virtual models can be used to run simulations and study performance issues and generate possible improvements. All in order to develop valuable insights, which are then applied to real-world physical objects.

  Food transparency is becoming increasingly important as consumers seek healthier ingredients and food sources. Some brands in the industry offer details of production conditions, plans to reduce their carbon footprint, or sustainable packaging design as reasons to buy their products. Digital twins can provide deeper insights from consumer and market feedback data from many different sources, thereby increasing the responsiveness of food brands, helping them re-engineer recipes, processes or product designs in response to market signals.

  Speaking at the 15th Annual Global Farm-to-Market Conference in 2020, Mike Duffy, CEO of C&S Wholesale Grocers, the 10th largest privately held company in the U.S., pointed out that the pandemic has clearly shown that technology will play a role in efficient and flexible supply chains. Key role. Therefore, how to strengthen cooperation with all supply chain partners is very important. Or, how to achieve better connections, predictions, and faster decisions? For products, the agri-food industry supply chain has historically lacked visibility, with a severe disconnect between product demand and production decisions, resulting in huge raw material waste and financial losses. How can production cycles be shortened so that producers can better respond to changes in consumer demand so that obsolete or excess inventory does not occur? Digital twins may be the answer, providing actionable, product-specific information in response to the changing needs of consumers or supply chain partners.

  A number of leading agri-food companies, as well as emerging startups, are building an integrated response system based on digital twin technology to provide a framework for suppliers and end customers. The digital twin can make estimates and modifications based on feedback from retail, food service or consumer surveys, combined with a range of information and benchmark performance to identify potential business opportunities. This allows agri-food companies to gain insight into producing products or making corrective decisions. For example, it can simulate the impact of changes in raw materials or packaging, which might see changes in market preferences. As part of a support function for agri-food companies, digital twins can more accurately use real-world supply chain and operational information to validate the manufacture and production of new products.

  Siemens' Simcenter digital twin software, combined with production cycle management technology, relies on continuous data feedback to support testing and iteration of production lines, from supply chain logistics with source partners to food retail or food service. Simulations in digital twins help to validate prototype products through internal testing, and the virtual cost of failure is much lower compared to the physical entity. For example, UK startup TrakRap has partnered with Siemens digital twin software to reduce operational costs such as modifying packaging, and the transparency of the digital twin software builds trust throughout the agri-food value chain.

  For automation leader Siemens, digital twin software helps the food industry become more flexible, using information in a timely manner to drive continuous innovation. Combining physical and digital to enable continuous improvement of products through update processes. As a leader in manufacturing innovation and operational design, Siemens is supporting agri-food brands in responding to changing market conditions, accelerating point-to-point production and ensuring rapid response.

  New companies such as Twinthread are using digital twin applications to model small and medium food companies and more easily gain insights into optimizing production in factories. Using artificial intelligence and machine learning, Twinthread provides quick value to the agri-food industry through operational cost savings such as greater energy efficiency and quality control.

  Companies such as Twinthread, IBM and Siemens are supporting a technological revolution in agri-food, digital twins are end-to-end application tools in supply chain and product management, enabling rapid reaction and timely improvement. Perhaps one day it might predict the impact of the pandemic, or factors related to climate, society, trade and geopolitics, on food development and innovation. Digital twins put design thinking into practice to form consumer- and market-centric innovation ecosystems that help bridge the gap between demand signals and production responses in the agri-food industry.