The Digital Twin is the virtual representation of physical processes, assets or systems. Organizations build and maintain digital supply chain twins to manage their complex and global supply chain networks. Digital twins are utilized to provide horizontal visibility, identify opportunities by discovering patterns, eliminate inefficiencies by determining root causes, create responsive plans for potential disruptions, optimize current processes and available assets and so much more.
Building and maintaining a digital twin is not a simple network modeling effort. It requires cross-functional collaboration by operations, finance, IT and BI teams at the minimum.
The following steps provide you with a proven methodology for building a digital twin for your supply chain.
Step 1: Map out processes and/or assets
Organizations might prefer to start with creating an end-to-end digital twin of their supply chain or start with a section as a pilot and then scale it after testing. In either case, the teams in charge of each of the component of the supply chain operations and assets, e.g. transportation (fleets), warehousing, inventory, sourcing etc. should get together to map out the processes and assets to be virtually cloned end-to-end.
Step 2: Determine data sources
Traditional network modeling usually calls for past data and in some cases forecasts and plans. Digital twins on the other hand require real-time data. Therefore, they are generally linked hand in hand to Internet of Things (IoT). As all relevant operational and financial data is identified during the mapping process, the sources and owners of the data should also be identified.
Step 3: Choose hosting technology
There are many critical technological factors when creating and maintaining an IT architecture digital twin. The architecture should allow for connecting to multiple data sources including internal, external and real-time data, and support a wide variety of data formats. It should be scalable to run multiple simulation and optimization scenarios. Security and monitoring should be configured to provide easy access to users with diverse purposes and tasks.
Step 4: Model Supply Chain Twin
The Digital Twin should be built keeping the long-term objectives in mind in a similar fashion to when building supply chain models for optimization and simulation. The structure should allow for simulating and analyzing alternative processes, optimizing asset performances and predict occurrences.
Step 5: Connect to real time data
The two main distinctions of digital twin compared to optimization of simulation modeling is using real time data and minimal data aggregation to allow for detailed visibility. Once the digital twin is built, it is connected to internal as well as external real time data sources. This might require investment in new technologies such as sensors or RFIDs and collaboration with third parties.
Step 6: Simulate, Optimize & Analyze
Opportunities for prescriptive, predictive and advanced analytics using digital supply chain twin to drive decisions range from strategic to operational. Combined with machine learning when applicable, operations and assets can be simulated or optimized to gain insights, test alternative scenarios or become responsive to disruptions. The outputs should be shared across the organization to drive organization-wide action plans.
Step 7: Scale & Enhance
As the digital twin of the supply chain is built piece by piece and tested, it is only bound to evolve and expand. It can be scaled across organization to clone end-to-end supply chains, but when we think about the supply chain holistically, it can go even beyond the borders of the organization connecting with suppliers and customers. The twin can be enhanced with additional real-time data points from internal sources as well as third parties and industry organizations.
And as always, the whole process needs to be well documented in order to maintain, modify and scale.
- Metersky Brings Supply Chain Leadership Experience with Trusted Design and Planning Expertise
- Meterky To Push Optimization Practice To New Heights
ATLANTA, GA., Jan, 17 2020 – Agillitics, a premier provider of digital supply chain services, announced today that it has named Jeff Metersky as its Chief Solutions Strategist effective January 20th 2020. Metersky will bring his vast supply chain design, optimization and analytics experience to focus on business development, alliance management, solution development and delivery.
“It is a great honor that Jeff has decided to join the Agillitics family. Jeff is one of the most respected and influential practitioners in supply chain, and to say we are thrilled is an understatement,” says Agillitics’ President & CEO, Tim Judge. “It is very exciting to have Jeff as part of our leadership team to kickstart the new year, the new decade and the next evolution of growth at Agillitics. Jeff is a proven leader with unique industry and domain expertise to ensure we continue exceeding our customers evolving needs while continuing to innovate and challenge the status quo.”
“I am very excited to join Agillitics to help them accelerate their growth into a leader in the supply design, optimization and analytics space,” says Metersky. “I was drawn to Agillitics based upon the company’s strategy to offer both the end-to-end analytics and the data strategy services required to support sustainable processes for customers. I also appreciate their like-minded dedication to fact-based decision making, their use and support of best in class enabling technologies and their focus on enabling companies to create their own internal capabilities and competencies.”
About Jeff Metersky
Jeff Metersky’s passion is applying advanced analytics to improve a company’s ability to compete effectively through its supply chain. His 35+ year career has focused on helping organizations adopt supply chain design & planning principles and technologies.
Metersky co-founded Chainalytics, a global supply chain consulting, analytics and market intelligence firm. While there, he served as VP of the sales inventory & operations planning practice and the supply chain strategy practice. He was responsible for developing their service offerings, go-to-market strategy and team development, while also leading all business development and delivery activities. Afterwards, Metersky served as the VP of solution strategy for LLamasoft, Inc. He occupied this role for 5 years and was responsible for leading the design of high-impact, high-quality customer solutions.
Metersky holds a Bachelor of Science in Industrial Engineering from the University of Illinois at Urbana-Champaign and a Master of Business Administration in Materials and Logistics Management from Michigan State University.
Agillitics is a supply chain analytics consulting and technology firm based in Atlanta, GA. Through business intelligence, analytics and optimization, Agillitics empowers companies to make prescriptive and proactive data driven decisions to improve operational performance, enable innovation and drive competitive advantage across industry verticals.
Have you ever tried solving a jigsaw puzzle cardboard side up? While not theoretically impossible, it can be done with determination, time, and an understanding of how the puzzle is solved. Most jigsaw puzzles are rectangular, solving for at least the borders upside down does not take much effort. From there, time and perseverance is the best solution to solve the inside pieces. Building a supply chain network model without proper model strategy and design is like solving a jigsaw puzzle, upside down. Model strategy and design creates a foundation that adds color and depth to network design models that, otherwise, would be monotonous and void of true business value.
Continue reading “Network Design: The Art of Simplicity is a Puzzle of Complexity”
“Visual excellence is that which gives the viewer the greatest number of ideas in the shortest time with the least ink in the smallest space” – Edward Tufte.
Often, it is easy to aggregate data and answer clients’ needs;however, creating a concise story from the visualizations and delivering suitable insights can be more difficult. Finding appropriate, effective, and simple visuals to best represent data is a challenging task that must be approached from multiple angles. In addition to incorporating user needs, it is imperative to apply best practice UI/UX to data visualization. In particular, to truly understand and respond to the user, we have to utilize heavily researched psychological factors within our dashboards. In this article, we address these challenges and help you create impactful visuals.
We are in an ever-changing dialogue with data and we want to take you through the journey of combining these human tendencies with business intelligence and analytics to create a better finished product. Continue reading “Information Consumption and the Laws of Visualization”
You already know the best way
You are the actual domain experts who have ingrained this intuitive problem-solving ability into your very fibers through significant effort over a long and sustained period of time. Machine learning without a domain expert is just that – a machine without intuition. Continue reading “Machine Learning is the second-best way of doing anything”
Artificial Intelligence is the key to harnessing the potential of the future. Articles detailing this have been circling the media universe for a while now but what is AI and why is it useful? To explore this beyond the oversimplified definition of “smart robots”, let’s start from the early origins.
The Turing Effect
As customers demand and expect their products “anytime/anywhere,” companies are shifting focus to implementing best in class Order Management & Fulfillment software to compliment their ERP, WMS, and TMS systems.
Keith Robbins joins Agillitics Team as Director, Supply Chain Program Management
Atlanta –August 5, 2015 – Today Agillitics, LLC, announced that Keith Robbins has joined Agillitics as Director, Supply Chain Program Management.
“We are all really excited to have Keith on board at Agillitics. Keith brings a wealth of experience in managing very complex technology projects. His leadership qualities are also a perfect fit with our continuous learning culture.”
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Continue reading “Keith Robbins, Director Supply Chain Program Management”
In our last few posts we focused on the importance of bringing supply chain data into an Enterprise Data Warehouse (EDW) (http://bit.ly/SupplyChainED) and the value achieved (ROI) of doing so (https://bit.ly/2nqGv62). Staging and storing data enables essential descriptive and diagnostic analytics. Predictive analytics is a natural next step in the analytics maturity model.
Below is a visualization of the Supply Chain Analytics Maturity Model from Gartner. What type of analytics is your company currently implementing? We would love to hear from you.
Supply Chain Analytics Maturity Model