Using real-time data gathered from sensors and other sources, digital twins are virtual representations of actual systems or things. These computer simulations can be utilised to optimise performance, anticipate possible issues, and arrive at well-informed judgements by offering insightful information about how physical systems are operating. However, it is crucial to put intelligent data orchestration policies into place in order to maximise the advantages of digital twins.
Data must be gathered, arranged, and analysed intelligently in order to yield insights that may be put to use. This entails gathering high-quality data, combining data from many sources, and analysing and interpreting data using algorithms and machine learning. The objective is to transform unprocessed data into useful knowledge that can be applied to decision-making.
Increased operational effectiveness is one of the main advantages of digital twins. Organisations can pinpoint areas for improvement and implement adjustments to increase performance by using real-time data to monitor physical systems. A digital twin of a manufacturing process, for instance, might help pinpoint bottlenecks, monitor inventory levels, and enhance production plans, increasing productivity and decreasing waste.
Better predictive maintenance is another benefit of digital twins. Organisations can forecast when equipment is likely to fail by analysing data from sensors and other sources. This enables them to plan maintenance ahead of time and save downtime. In addition to reducing the possibility of equipment failure during crucial procedures, this can save a lot of time and money.
Businesses must put data management procedures in place that guarantee the reliability and accuracy of the data acquired in order to maximise the benefits of digital twins. This entails putting quality control mechanisms in place, like data validation, and creating a framework for data governance to guarantee that data is gathered, handled, and kept securely and consistently.
Additionally, in order for the data to be easily analysed and applied to guide decision-making, organisations must make sure that it is incorporated into their current systems. This necessitates the development of an extensive data management strategy, which should involve input from all relevant parties, including the IT, operations, and business teams.
Businesses need to be able to analyse and comprehend data in addition to gathering and managing it. This includes utilising machine learning and algorithms to spot patterns and trends as well as predictive analytics to generate predictions. The objective is to transform data into information that can be utilised to guide decisions.
Finally, organisations need to make sure that the right stakeholders are informed about the advantages of digital twins. This entails giving staff instruction and support so they can utilise the digital twin and comprehend the data it produces. In order for decision-makers to make well-informed choices that increase business value, it is also necessary to ensure that they have access to the data and insights produced by the digital twin.
Businesses stand to gain a lot from using digital twins, including increased operational effectiveness, proactive maintenance, and well-informed decision-making. Implementing intelligent data orchestration techniques, such as data quality control, data integration, data analysis, communication, and training, is necessary to maximise these advantages. By doing this, businesses may use digital twins to their advantage and transform data into information that can be used to make decisions.
There is an expanding chasm between data and value. An Accenture study across 190 executives in the United States found that only 32% of companies reported being able to realise tangible and measurable value from data and only 27% said that analytics projects produce insights and recommendations that are highly actionable.
In that report, Ajay Visal, Data Business Group Strategy Lead, Accenture Technology said: “Companies are struggling to close the gap between the value that data makes possible and the value that their existing structures capture—an ever-expanding chasm we call ‘trapped value.’”
This trapped value could lead to changes to legacy business processes that could unlock huge productivity. It could be the digitisation of paper-based systems and processes which would improve accuracy and support reduction in paper waste. It could be more profound, unlocking capabilities for modern organisations to out manoeuvrer the competition with greater and more actionable insight. And a central area of this trapped value will be the ability for businesses to automate processes, harness artificial intelligence and machine learning – technologies that are certain to completely transform the way we do things.
But perhaps a more discrete point would be business agility. We have seen unprecedented levels of uncertainty over the past 5 or so years. It has been one uncertainty followed by another, from Brexit to the pandemic, to the war in Ukraine, to energy prices and so on. Never have businesses experienced this kind of shock repletion for such a sustained period. Business now needs to be able to react to change like never before. This means adapting existing processes, introducing new processes, simplifying operations, building more robustness into aspects of operations.
Data of course, and business’s ability to access and use data, has a huge role to play in this. Overall, we see this data challenge in two key parts: (i) data accessibility at scale – the ability for individual organisations and operators to access the data that they needed and (ii) the ability to use that data – being able to interact and derive insight from data simply and quickly.
Data accessibility at scale
Modern business is rapidly becoming more interconnected but as a result, more fragmented. Be it departments within a single organisation or separate organisations working together. The level of interdependency is growing, and rapidly. This is driven by many things. The complexity of modern business, the market and competitive pressures, geographies, supply chains, and so on.
Today’s collaborative norm has huge advantages for those businesses and their customers. From quicker and richer supply chains to more advanced, comprehensive, and collaborative products and services that extend value to consumers.
But the interconnectivity does also lead to more fragmentation. As these businesses work more closely together, the need to share data, information, assets etc. across a network of departments or organisations increases. The businesses are no longer single domain. Data regarding their operations, their performance, ultimately, the data they need access to now resides across many different organisations or departments, in a myriad of different systems. This makes accessing the data one needs incredibly difficult. In fact, studies show data collection accounts for 20% of the time on a typical data project.
Ability to use that data
Couple the data accessibility challenge with the many different data types, the different data formats, etc. and the ability for businesses and individuals to use that data becomes nigh on impossible.
What was highlighted in the data accessibility challenge was the many different organisations involved and the number of respective systems within which data resides. These systems will likely differ in data type, data format, latency, interfaces and so on. Studies show almost 80% of all data is unstructured. This results in data and analytics professionals spend most of their time on data cleansing/processing –accounting for 50% of the time on a typical data project. Data cleaning is the process of putting data into consistent formats, freeing it from duplicate records, ensuring there are no missing values and placing it in a structured form. The protects the number one rule in data science – garbage in, garbage out.
The harder data is to use data, the more time spent cleansing and processing data as opposed to analysing it, the less time spent analysing and using that data, the bigger the gap between data and value becomes.
Crossing the data chasm
For businesses to unlock the trapped value in their data, they must cross the data chasm. Learn how Entopy can support businesses to achieve this with Intelligent data Orchestration.
Many are waking up to the fact that the data to achieve enhanced visibility across modern supply chain networks exists today. The data resides in many systems across the supply chain and if pieced together correctly, will lead to the next wave of digital transformation.
The challenge is now how the various participants in a supply chain network can access the data they require to build a complete picture across the entire supply chain network.
Security, privacy, and value
Whilst it is true that supply chains comprise many organisations working together for a common goal, they are ultimately separate organisations.
The systems that each organisation deploys are specifically designed to aid their respective operations and meet the requirements they have. For example, a telematics system may be used by a 3PL to monitor its fleet, and will be paid for, and therefore, controlled by the 3PL. A consignee/consignor will likely use an Order Management System to manage, you guessed it, orders. Again, the ownership and control of this system will sit with the consignee/consignor. The data that resides within these systems are owned and controlled by the respective organisation.
Any kind of data sharing between organisations needs careful thought. One can’t simply share all data with external organisations as this would cause all sorts of issues, not least because one organisation may work for multiple others and therefore, sharing all data with another would be a major breach of privacy.
Different needs, different visibility requirements
Additionally, the visibility requirements of each stakeholder are different. The telematics system used by hauliers/3PLs provides visibility of all vehicles, all the time. This is needed as they need to monitor vehicles all the time.
Whereas the requirement of the manufacturer and retailer in this instance is much more specific. They want to know where their consignment is. This could be achieved by capturing the GPS data of the vehicle that is moving that consignment. But this would only require visibility of a specific vehicle for a specific period.
The need for intelligent data orchestration
Value can be achieved for all stakeholders by applying intelligence to the way the data is captured, what data is captured and the ultimate picture it is combined to provide. Some have cited the use of a shared ledger for all raw data to be input and made available to all parties. However, this would cause complications with access/privacy etc. The answer is not to simply make replicas of the respective systems in a shared way as this would not overcome the key privacy challenges around data accessibility at scale.
A solution to provide a more intelligent approach is required.
Entopy’s software platform can provide that solution. Entopy uses proven techniques to capture data in a targeted way, from existing domains owned, operated, used and critically, maintained by the respective organisations that comprise the supply chain network. Starting with the ultimate picture that the various stakeholders require, it forms the framework of a ‘Digital Twin’ – often at consignment level.
The platform coordinates data across the various supply chain systems, defining parameters and rules to ensure only relevant data is captured.
The result is a rich ‘digital twin’ with which all stakeholders can interact and gain the visibility they require.
Entopy’s approach enables data to be shared across the supply chain network whilst maintaining privacy between the respective organisations.
Unlocking data accessibility at scale and using that data to deliver coherent visibility to the various participants will lead to major step changes across the supply chain network, from visibility to awareness, to communication, to automation.
We continue to innovate and push boundaries here at Entopy. This latest addition to the Entopy platform increases flexibility with regards to how the data required to create Digital Twins can be ingested and extends transparency across all users and stakeholders involved.
For the first time, users can view Digital Twins at each stage of their lifecycle within Entopy. Before, users could interact with the live Twin, but Digital Twin Portal enables users to view ‘part-baked’ Twins in Draft, invalidated Twins, and the reasons why and live Twins with full visibility of all metadata. What’s more, we have enabled manual input processes and automated population of ‘Twin’ schematics to work together as one.
Our aim is always to leverage data from existing domains, and this is for several reasons. However, in some cases, capturing data from an existing domain is not possible. This could be because the data doesn’t exist (there are different levels of system maturity across businesses), the systems and data are there but there is no way of interfacing with those systems etc.
Entopy’s ‘conductor’ role means that it is designed to sit across multiple systems across multiple stakeholders. This will always mean we have to interact with lots of different systems, of different types and ranging maturities.
The new features inside ‘Digital Twin Portal’ enable us to be much more flexible in the way we capture data to populate our ‘Digital Twins’. It allows us to work part automated, part manual. It allows Entopy to flex around existing business processes whilst maintaining a high quality of service.
Draft ‘Twins’ can be manually added to before moving to live use. To support this, we have extended notifications centre to notify relevant stakeholders when key pieces of data are required from them. So, when we need human input due to a missing piece of data, we can take that request directly to the specific person from whom we need input.
Digital Twin Portal also enables all stakeholders to log in and view the metadata for the Digital Twins created in Entopy. Of course, access is permission-based and on an individual Twin basis. This additional transparency ensures trust is maintained across the network and supports data governance to ensure the data quality remains strong.
We are super excited about this major toolset and subsequent features that have been added to the Entopy platform. And it’s already being used, helping one of our newest clients to deploy Entopy across their extensive supplier network, comprised of ranging IT literacy and system maturity.
Digital Twin Portal is an obvious addition to the evolving Entopy platform, and we are excited to see what new features, ideas and developments come off the back of it.
Supply chain visibility pioneer Entopy has signed a two-year agreement with IT services company Fujitsu UK & Ireland to underpin its smart border solution, Atamai Freight. The move follows a successful trial, using Entopy software, on routes between Great Britain and Northern Ireland.
Atamai Freight enables goods to move seamlessly by verifying the integrity of freight from the point of loading through to the point of unloading. Unifying all the different elements of the supply chain on a single digital platform means businesses can automatically complete complex customs procedures – saving time and money – and provide customers with accurate arrival times for goods.
At the heart of the novel solution is Entopy’s software stack, which co-ordinates data input by users of the service and the ‘smart’ seals fitted to trailers. The data is processed and combined to create a ‘digital twin’ of a consignment, with events captured and communicated to relevant stakeholders throughout the consignment’s lifecycle.
The Entopy technology also enables the capture of data from existing supply chain systems – such as transport management, order management and telematics systems – which will ensure Atamai Freight can be used by industry with minimal disruption.
“Our unique intelligent data orchestration technology synchronises all the various data inputs, ensuring only relevant data is captured and creating detailed consignment lifecycle records which are written to a blockchain where they are held immutably,” said Entopy CEO Toby Mills.
“This removes the need for numerous discrete connections and maintains data integrity, whilst enabling Atamai Freight to deliver complete and coherent visibility across the many stakeholders involved in modern supply chains.
“For Entopy, this deal is a milestone on the road to transforming both domestic and international trade in the years to come through the use of our intelligent data orchestration and digital twin technology.”
Christian Benson, VP and Client Managing Director at Fujitsu UK & Ireland, said: “With the help of our consortium partners, we are establishing a new level of collaboration and trust throughout the UK supply chain – making it easier to move goods. Atamai Freight provides real-time visibility of each journey and consignment, which can then be shared with participating businesses and government authorities.”
Entopy CEO, Toby Mills gave a presentation of the Entopy platform, our vision and how our Intelligent Data Orchestration and Digital Twin technology can support businesses in our collective sustainability efforts.
Alongside Entopy, were representatives from IBM’s AI and Analytics team, their Blockchain provenance platform and other ecosystem partners.
The day was filled with great conversation, the sharing of forward-thinking ideas and techniques, culminating in a drink’s reception and IBM’s new London Office on York Road.
Watch the full presentation here: Watch full presentation