Digital Twin: Challenges and Triumphs in Supply Chain
This article decodes the power of Supply Chain Digital Twin and provides an in-depth analysis of various Successes and Challenges associated with it.
The burgeoning interest in Digital Twin technology, a game-changer in supply chain management, is intense.
A survey conducted by Gartner reveals a striking 75% of supply chain organizations implementing AI are eager to infuse Digital Twin into the Supply Chain for effective decisions. A recent, in-depth study by McKinsey highlights the extraordinary gains available for those who master the integration of Digital Twin technology. Success stories reveal an average revenue boost of 10%, an acceleration of the time-to-market by 50%, and an impressive surge in product quality by 25%.
The above metrics underline the compelling allure of this technology. However, the road to successful implementation has been very low.
According to a Forbes article, the risk of failure in digital transformation is as high as 84%.
Before we delve deeper, let us understand what is Digital Twin.
A Digital Twin is a digital representation of a physical object or system, designed to simulate various scenarios. Its goal is to offer prescriptive solutions by analyzing potential future situations, ultimately providing optimized outcomes.
Why is there a huge risk?
The answer lies in understanding this digital transformation technology's unique challenges and rewards.
The hurdles impeding successful implementation are multifaceted.
But three pivotal factors stand out:
1) Leadership Vision
2) Data Collection, Data Quality & Data Integrity
3) Collaborative Efforts
Firstly, effective integration of Digital Twin technology requires unambiguous goals and objectives from leadership like — “What are the specific goals that the digital twin is supposed to achieve?”, “How will it be used to improve the supply chain?”
The absence of such a directive vision often results in an unclear path to unsuccessful implementation. Further, there should be a single point of responsibility for the digital twin. This person should oversee the project, define the MVP (Minimum Viable Product), build the product roadmap, and ensure the proper implementation and maintenance of the solution.
Secondly, the quality and consistency of data used in the Digital Twin model significantly impact its efficacy. Too often, supply chains suffer from a fragmented focus on separate functional areas, leading to data silos. Insufficient automation and lack of end-to-end integration can result in ignoring crucial data attributes, manual errors creeping in during data entry, and outdated data in a fast-paced supply chain environment.
Finally, the importance of collaboration cannot be overstated. Beyond breaking down internal silos and promoting integration at the enterprise level, organizations must also foster strong cross-functional communication and collaboration across the entire supply chain. Including upstream and downstream stakeholders in the strategic discussions ensures all the stakeholders align towards the organizational goal and focus on maximizing profits.
Let’s look closely at some classic Real-World examples:
In 2011, GE started its journey towards digital transformation by focusing on sensors and IoT solutions and eventually took the initiative forward in 2015 by creating a new business unit GE Digital. Despite allocating huge cash towards this new Business unit, the YoY (Year-over-Year) revenue declined and failed to attract investors. This resulted because the company tried to transform its entire business but without a clear focus and strategy.
Ford, another multinational American automobile manufacturer, attempted digital transformation in 2014 by creating a new segment, Ford Smart Mobility, to produce digitally enabled cars. Eventually, the new business segment became a standalone entity without efforts to integrate it with the rest of Ford. As the focus shifted to the new venture, the quality deteriorated in other areas, resulting in the stock prices tumble. As a result, the company CEO had to step down.
How to make it successful?
The supply chain digital twin is rooted in the essential principles of digital transformation.
It will only be successful if —
- It is adopted by all of the stakeholders in the supply chain.
- The business has a clear vision of the benefits the solution promises and appoints a Digital Lead who has a clear vision for execution.
- All the stakeholders are involved early on the digital journey to break the silos and get valuable input and support from early on in the project.
- Ensuring Data Quality and Data Integrity and enabling the correct information to flow across the supply chain to improve decision-making, reduce cost, increase productivity, and enhance the effectiveness of the supply chain digital twin model.
- Strong collaboration and partnership among the stakeholders.
- Fostering the growth of the team by building the digital transformation mindset and up-skilling them in Digital technologies like Cloud computing, IoT, Machine Learning, AI, Data Analytics, etc.
Conclusion
While the benefits of a well-implemented Digital Twin are undeniable, achieving such a feat demands a clear vision from leadership, rigorous data integrity, and an unwavering commitment to collaboration.
As organizations grapple with these challenges, those who can master these aspects stand to reap substantial rewards from this technology.
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References:
[1] Henriette, Emily; Feki, Mondher; and Boughzala, Imed, “Digital Transformation Challenges” (2016). MCIS 2016 Proceedings. 33.
https://aisel.aisnet.org/mcis2016/33
[2] Angira Sharma, Edward Kosasih, Jie Zhang, Alexandra Brintrup, Anisoara Calinescu, Digital Twins: State of the art theory and practice, challenges, and open research questions, Journal of Industrial Information Integration, Volume 30, 2022, 100383, ISSN 2452–414X,
https://doi.org/10.1016/j.jii.2022.100383.
(https://www.sciencedirect.com/science/article/pii/S2452414X22000516)
