Introduction

Dashboards are used both in IT and OT (Operational Technology) domains and across all industries. They have become commonplace and for many are a critical productivity tool. We’ll discuss why and explore them through a couple of case study examples that showcase what they can offer and why they are such a valuable digital solution for many roles within an organisation.

Why the need?

Dashboards can be effective productivity enhancement tool. If designed well, this information that can be easily accessed to make data-driven decisions swiftly and accurately.

A common way to capture and present the information is in the form of Key Performance Indicators (KPIs). KPIs are a quantifiable measure of performance over time for a specific objective. Information captured in this way allows users, teams, and the wider stakeholder audience to gauge progress, assess performance against set targets and gain useful insights to make better decisions.

Standardised KPIs across the organisation enable performance benchmarking that allow comparisons to be made across sites, regions, or even globally (if applicable). These comparisons allow for the sharing of best practices through Centres of Excellence (CoE) or at company and industry events that provide an opportunity to share learnings and proven methods for repeatable success.
Reduced Manual Processing

For some organisations, source data would typically live in a variety of different data sources. This could be in spreadsheets, databases, and from different IT and OT systems. This would then have to be carried out each time for each reporting frequency. If this data capture can be automated, this will greatly improve operational efficiency.
Improved Data Accuracy

As well as it being a time-consuming activity, manual processing of data which may include data aggregation and custom calculations, can be prone to errors. Data automation will also have the potential to increase data accuracy. Important steps in this activity are that data quality reviewed and improved and ‘single version of the truth’ source data.
Faster Decision-Making

Having the right information immediately available is a big productivity boost. If there is a time lag between when the data is required and when it can be made available, then decisions are then made with old data and opportunities are potentially missed. The high availability of data increases an organisations agility and provide a competitive edge.
Operational and Strategic Benefits

Dashboards offer both operational and strategic benefits. This list is not exhaustive but provides an insight into some of the benefits that can be achieved:

• Freeing up of valuable resources to work on other high value resources
• Greater speed and simplicity in the sourcing and data preparation
• The data can become available to a wider audience
• Trends can be observed over specific and longer periods of time
• The data can be used in predictive analytics and forecasting models
• The data can be leveraged for Machine Learning (ML) and AI initiatives

Case Study Examples

 

1 - Operational

From an operational standpoint, dashboards can serve host of different purposes such as performance monitoring, inventory management, to production optimisation. This list is by no means exhaustive but gives some insight into some different use cases:

• Performance Management
• Inventory Monitoring
• Reliability and Maintenance
• Trends that signify operational degradation
• Events and threshold alerts

Let’s use an Offshore Gas or Oil Production Platform as an example. Depending upon the operational user role, here are some operational KPIs that could feature on a dashboard:

• Production rates/volums vs production targets - will inform us if we are on track to meet production targets
• Safety summary - provide and track key safety related event information
• Chemicals inventory - ensure there are enough chemical injection fluids for corrosion monitoring for example and to manage ordering and re-supply
• Emissions monitoring - track and ensure environmental compliance and targets are being met
• Event monitoring - summarise key operational events or alarms

2 - Retail

Dashboards provide a good opportunity to capture metrics that can be used to track retail performance, quickly identify areas of concern that indeed may have a simple explanation or may require a deeper investigation to understand the root cause.

The metrics tracked by a dashboard will vary depending on the specific performance objectives and use cases within retail.

If we take a retail store as an example:

• Sales metrics - allows review of monthly, quarterly and year-to-date sales data
• Customer centric data - track customer satisfaction levels and feedback
• Personnel data - to track staff utlisation to optimise resource allocation
• Safety data - capture and track safety performance data to ensure HSE standards are being met and identify additional training requirements
• Mobile platform version - allows visiting retail managers to access key data remotely

A dashboard can also enable performance to be evaluated for sites regionally, nationally and globally if applicable. Capturing KPI data in this way also allows for benchmarking and the sharing of best practices both externally and within an organisation.
 

Dashboard Solution Challenges

Delivery

For many dashboard implementation projects, starting small (e.g. with a Pilot or Minimum Viable Product - MVP) helps to minimise risk and feed learnings into the next development iteration. Adopting an agile approach will provide a methodology where functionality with the highest business value is prioritised in the product backlog. An iterative feedback loop will allow learnings to be incorporated into future iterations, contributing to a successful delivery, and long-term success.

Data management plays a key part in an organisation to develop and deploy a solution that contains high quality data, data feeds (including any data processing required – e.g., Extraction Transforming and Loading of data) and a highly responsive solution that can be used on a variety of platforms. The topic of data management is covered in more detail in a white paper that can be shared upon request.

User Experience (UX) is an important part of delivering a solution that will have a high user adoption rate. Some delivery teams will have dedicated UX specialist(s) to ensure that is important requirement is met.

There are many choices available in choosing the dashboard solution, this could be using a BI solution or developing a custom dashboard. Factors that will have a bearing are things like a strategic roadmap (if there is one - it will help with ensuring a forward looking technology alignment), business requirements, and system integration considerations.

Data security is a critical consideration. With the ever-increasing amount of cybersecurity threats, this will feature heavily in the solution design. Cloud-based Master Data Management, advanced encryption technology, zero trust architectural principles may all be part of the solution design and wider enterprise architectural landscape.

Ensuring solution effectiveness

There needs to be some strategic thought as to what dashboards are to be created and the intended audience. Hence the requirements capture and having a business case that clearly demonstrates the value creation are two important activities. User Acceptance Testing which can provide valuable early feedback and User Training are also key activities to ensure value creation.

Accessibility of the solution needs to be factored into the design considerations. The user base may include remote support teams as well as onsite users who may require mobile or field devices to access the dashboard. Therefore, compatibility with different operating systems and formats will also have to be part of the delivery scope.

A big danger and something that can happen in organisations that there are many dashboards created (e.g. typically through siloed behaviour due to a lack of strategic thought and a lack of internal communication) and so the latest dashboard just becomes another solution for the user base to use. The danger in this instance is that some users then have too many tools at their disposal which can have a negative impact on their productivity.

Periodic reviews of existing dashboards, incorporating user feedback (e.g. surveys), should be conducted to ensure ongoing relevance. These reviews serve to help with the developments of future versions and ensure operational optimisation.

A recommendation to ensure effectiveness, is that best practices should be put in place by way of having dashboard standardisation frameworks and implementing a governance board.
 

Conclusion

Dashboards are essential tools in boosting productivity. If designed effectively, they bring many benefits by helping to present the right information efficiently and effectively enabling quicker and decisive decisions to be made. This will improve workflow processes which in turn will improve operational efficiency and profitability.

Considerations in the design, business requirements, and user experience are all factors in ensuring that a dashboard is a business value creation tool that makes a tangible contribution to the organisation’s operational and Continuous Improvement initiatives.

What are Digital Twins?

Digital Twins are a digital replica of a physical asset or process and are used to simulate real-time or near real-time behaviour. This can range from the whole asset to just a much smaller scope size such as a single piece of equipment. Digital Twins are developed from a combination of IT (Information Technology) and OT (Operational Technology) and use real-time data from the physical asset to replicate and simulate real-life activity and their outcomes.

Through the collection of real time data via sensors and via data management solutions (e.g. data historians), a digital model can be developed and maintained to provide a virtual replica of the physical asset. As well the physical attributes (such as the dimensions), the behavioural attributes (e.g. performance, process conditions, and environmental factors) are all captured.

Digital Twins are frequently used for carrying out investigations and exploring what-if scenarios which can all be carried out in a risk-free environment and in a cost-effective way.  Digital Twins are increasingly used in optimising and de-risking product development and supply chains.  Some Digital Twins utilise predictive analytics and artificial intelligence (AI) capabilities to forecast future events, allowing for early identification of potential issues.

These digital replicas can be run both faster and slower than real-time to provide increased functional flexibility in their use. Operationally, Digital Twins can be left to run independently or out-of-hours, with results available for later review.
There are many value creating benefits of using Digital Twins that include:

•     Personnel Training: Training in a risk-free, virtual environment.
•     Performance Improvement: Monitoring and optimising processes.
•     Faster Decision Making: Real-time data leads to quicker, more informed decisions.
•     Defect Elimination: Identifying and solving issues without physical risks.
•     Process Optimisation: Fine-tuning operations to maximise efficiency.

Some practical uses cases of Digital Twins

 
A Flight simulator is probably one of the most common examples. As part of a pilots’ training, a key part is to put theory into practice and so use a simulator to learn and to demonstrate the required competency and skill to be a pilot. A flight simulator enables a host of operational and emergency use cases to be recreated in a safe and cost-effective way. As part of the training, assessments can be carried out to review progress and be used for ongoing training and assessments.

Jet engines are another example of where having a digital replica allows the ability to monitor its operation and provide insights that will lead to increased reliability and reduce operational inefficiencies. These learnings will also provide value input into future design specifications and improvements.

Geographical Information System (GIS) is a geospatial Digital Twin that is used in many industries that include Oil & Gas, Automotive, and the Public Sector. GIS systems are digital replicas of a physical asset.  In the Process Industries, any information (e.g. technical specifications) relating to the asset (e.g. a vessel or even a component such as a valve) can be linked to the asset. Any changes will be reflected so that the digital replica mirrors the physical version. Google Maps is one of the most widely used GIS platform. It is used for maps, directions, real-time information (e.g. traffic updates). Amongst its many functional use cases, we can use it to get a 3-D perspective of a location and identify physical buildings.

In the process industries such as Oil & Gas, Chemical and Manufacturing, Operations Training Simulators (OTS) are high value creation tools and are widely used as a result.  Understanding the dynamics and operational behaviour end-to-end of an asset provides a wide range of benefits.

When training and assessing plant operators/technicians for example, real-life scenarios such as equipment failure, a vessel/pipeline blockage, or a power failure can be initiated as events in the OTS to see how the user would respond in a given situation.

For Greenfield (i.e. new) sites, an OTS provides an opportunity to validate Standard Operating Procedures (SOPs) and engineering studies to take place in addition to training activities. For Brownfield sites, they are useful for debottlenecking and feasibility studies.  An OTS also provides an opportunity to execute operating strategies and identify defects.
From my personal experience of delivering Digital Twins, these solutions can have a large Return on Investment (ROI). Using the OTS as an example, operational issues can be resolved before they could possibly occur on the physical asset. In some instances where they were to occur, they would be very costly through loss of production and incurred operational downtime to resolve the issue and to bring the asset back online to normal production levels.
 
There are many more practical uses cases that can be discussed but the examples above provide a good starting point into some practical use cases and the benefit they bring in their respective application.
 

What are some of the challenges in developing, deploying and maintaining Digital Twins?

 
In the development phase, what can sometimes be challenging is getting the technical specifications of the physical asset required to build the virtual model. Close engagement with OEMs (Original Equipment Manufacturers) and support teams is very important.  Also having a competent technical team skilled in both IT and OT domains to develop and validate and fine-tune the model through the build phase.

It’s always best to start small (e.g. a pilot study) from which learnings can be gained with reduced risk before scaling up. These early insights will help to avoid costly failures in larger-scale implementations. As an example, the pilot could be a very small part of an asset and the larger scale implementation being the whole asset.

A robust test plan needs to be developed to ensure all use cases have been thoroughly tested and validated. Taking the example of an OTS, a Factory Acceptance Testing (FAT) would be carried out after development. Site Acceptance Test (SAT) would be carried out after site installation.

A maintenance plan also needs to be developed for BAU (Business as Usual) activities to ensure the model is kept up to date with necessary software and security updates & patches.
Something that can easily be overlooked but is important is to a keep a Lesson Learned log to capture all learnings both in development and in operational use. These learnings can then be used in any future deployments and in Continuous Improvement (CI) initiatives.

Through incorporating learnings and continuously monitoring for improvement opportunities, Digital Twin implementations can become more robust, scalable, and beneficial for organisations.
 

Conclusion

It can be seen that Digital Twins are powerful transformative tools for improving efficiency, reducing risks, and facilitating innovation across many industries.  By creating virtual replicas of physical systems, they allow for training, real-time monitoring, predictive maintenance and many other real-world use cases.

As the technology continues to evolve, Digital Twins will have increasing value in their use and value creation.

What is Operational Technology?

Operational Technology (OT) are the computing and communication systems used to monitor, control, and manage physical processes and machinery in several industrial sectors that include Oil & Gas, Automotive and Manufacturing. This includes both hardware and software systems.

This will include machinery, monitoring systems and control systems. Some examples of Operational Technology are:

• Oil & Gas: Industrial Control Systems (ICS) - includes Distributed Control Systems (DCS) and Supervisory Control and Data Acquisition (SCADA) systems.

• Automotive: Automated Assembly Line in the manufacturing process of vehicles.

• Manufacturing: Internet of Things (IoT) technology allows machinery to be connected to the internet and exchange data with other devices.

How does Operational Technology (OT) differ to Information Technology (IT)?

Information technology (IT) relates to the data and the flow of digital information and how it is stored, transmitted, processed. It is the technology backbone for any organisation. The digital flow and the availability of data are key enablers for organisations to increase their profitability and create new opportunities for business growth.

Operational Technology is focused on the production side and is more associated with hardware and the physical equipment used in industrial systems and processes. OT devices are more likely to be purpose built, have specialised software and proprietary protocols.

There is increasing overlap between OT and IT that includes of enabling technology such as Internet of Things (IoT) that allows operational data to be collected throughout the asset, machine, and product life cycle. This allows intelligent insights to be created by way of analytical models, predictive maintenance, and operational dashboards.

This provides benefits that include:

• Increased Safety

• Minimising production downtimes

• Extending the life of assets

• Reduced maintenance costs

• Increasing Productivity

 

Operational Technology (OT) vs Information Technology (IT)

 

Digital solutions typically used with Operational Technology

Digital Twins

Digital Twins are a digital replica of a physical asset and are used to simulate their behaviour. This can range from the whole asset to just a much smaller scope such as a single piece of equipment. Digital twins used real-time data just like the physical asset to simulate asset behaviour and monitor operations.

Common examples are Flight Simulators used in the aviation industry to train and provide ongoing refresher training for pilots. Likewise in the process industries, Operator Training Simulators (OTS) are commonly used to primarily train plant operators and are also used for operational use cases that include engineering studies and identifying potential faults. In the Automotive industry, Digital Twins are used to create digital model that provide insights into the physical behaviour of a vehicle.

Key benefits: Digital Twins help to provide performance improvement by enabling a better understanding of the asset and by exploring operational scenarios in a safeand cost effective way. Addtional benefits include leveraging predictive capabilities enabling early detection and reducing costly downtimes.

Data Management Solutions/Data Historians

Data Management Solutions/Data Historians collect plant/asset data in Real-Time and can store several years’ worth of data. The data stored in the historian can also be leveraged by a whole host of applications would typically include:

• Predictive Maintenance solutions

• Plant Visualisation software

• Operational Dashboards

• Alarm Management software

Data Historians in particular are typically connected to the Process Control Network (PCN) and so the architectural design needs to contain the additional necessary security protocols to protect the asset from cybersecurity threats.

Key benefits: Provides real-time and secure access to plant data that can be leveraged for a wide range of operational use cases.

Advanced Process Controllers (APC)

Advanced Process Controllers are software solutions that are used to optimise plant performance. APC solutions continuously collect plant parameters and have calculation engines to determine optimal operating conditions based on current plant conditions.  By doing so they seek to minimise plant disturbances, reduce energy consumption, and optimise throughput. They are widely used in industries such as Oil & Gas, Chemical and Utilities.

These control solutions can connect to a wide array of supporting solutions to monitor controller performance and provide alerts that will include detecting signs of controller performance degradation.

Key benefits: Provide a key layer of process automation for both plant safety and improved plant performances. Also provide a foundation for other real-time plant solutions such as Real-Time Optimisers.

Operational Dashboards

Operational Dashboards leverage plant data and are effective tools for capturing key operational metrics either in Real-Time or near Real-Time. Dashboards can be configured to provide alerts when thresholds are reached and show data and trends based on custom time periods. This can be very useful in trying to understand plant behaviour prior or post events, and to detect any anomalies or any performance degradation.

Dashboards can be configured to provide Role Based Access (RBA) as an important security measure and can also be accessed remotely with only the appropriate level of information shared based on permissions. This is useful particularly in scenarios where support teams are located remotely. This can be the case for Offshore platforms in the Oil & Gas industry, for example. RBA also enables a wider stakeholder audience to access the data.

Key benefits: Provides key operational performance data that can be used to increase productivity, plant investigations and accessibility.

 

Internet of Things (IoT)

The Internet of Things (IoT) provides a bridge between OT and IT. Using devices such as sensors, physical equipment plant data can be captured and utilised by connecting and exchanging data with other devices and systems over the internet.

In do so, The Internet of Things (IoT) play a significant role in enhancing and transforming Operational Technology (OT). By integrating IoT with OT, industrial operations can increase efficiency, enable greater security, and extend asset life.

More specifically, Industrial Internet of Things (IIoT) focuses on the connection of machines and devices in the several industry sectors. This enables information to be received in real-time enabling faster decision making. Application examples that benefit from this technology include predictive maintenance, automated inventory management, and Machine Learning/Artificial Intelligence applications.

 

What are the challenges in deploying OT solutions?

Deploying Operational Technology (OT) solutions present several challenges that can significantly impact the effectiveness, security, and profitability of industrial operations.

Maintaining operations with aging infrastructure

Challenge: Older plants often lack compatibility with modern OT solutions making the integration with new technologies challenging.

Impact: The impact of upgrading or replacing aging infrastructure can be costly and time-consuming. This is because many industrial facilities have been in operation for decades. In some cases, the risks and costs associated with upgrading may lead to the prolonged use of outdated systems. 

Cybersecurity

Challenge: Industrial systems are increasingly connected to IT networks and the internet, they are coming under increasing cybersecurity threats such as ransomware, malware, and unauthorised access.

Impact: The impact of Cyberattacks on OT systems can result in severe consequences, including operational disruptions, safety-related incidents, financial losses, and reputational damage. Protecting OT systems requires specialised cybersecurity measures that can differ from traditional IT security practices.

Safety

Challenge: Industrial environments have inherent safety risks, and so deploying new OT solutions can introduce additional complexities if not properly managed. Ensuring that new technologies do not compromise the safety of personnel, asset equipment, and the environment are critical challenges that need to be managed accordingly.

Impact: The impact of safety issues during OT deployments can be severe, including injury to personnel, damage to equipment, environmental harm, and potential legal liabilities. Safety considerations must be integrated into the deployment process, with rigorous testing, adherence to safety standards, and ongoing monitoring to mitigate risks.

Minimising operational downtime

Challenge: This is an important consideration to ensure the high availability of industrial systems. Downtime, whether unplanned or scheduled, can have significant financial implications on profitability. Downtimes are typically reserved for scheduled periods of time like plant turnarounds where scheduled maintenance activities can take place.

Impact: Unexpected downtimes can disrupt production, lead to missed production deadlines, and incur significant costs. Planning for minimal disruption during OT deployment is crucial, often necessitating extensive testing, simulation, and phased rollouts. This approach aims to ensure that the deployment process is as seamless as possible, minimising any impact on ongoing operations.

Profitability

Challenge: Deploying OT solutions often requires substantial upfront investment in new technologies, training, and infrastructure upgrades. These costs can be a significant barrier, especially for companies with tight budgets or those operating in industries with low margins.

Impact: The financial impact of deploying OT solutions must be carefully managed to ensure that the benefits outweigh the costs. If not properly planned, the deployment could lead to financial strain, reducing profitability. However, successful deployment can lead to long-term gains in efficiency, productivity, and cost savings, ultimately enhancing profitability.

 

Conclusion

Operational Technology (OT) play a pivotal role in industrial operations by enhancing safety, optimising operational performance, and driving profitability. The integration of OT with Information Technology (IT) has become increasingly important as it allows organisations to leverage the latest technological advancements and create a competitive edge.

However, the strategic implementation of these solutions is not without its challenges. Organisations must carefully plan and execute their digital transformation journey to achieve benefit realisation that includes minimising operational downtime, extending asset life and increasing productivity.

 

Many organisations are looking to transform their enterprises with digital solutions. A question to senior leadership and executives in each organisation is how much are you using applications such as Microsoft Excel and Access to run your business?



The reality is that a lot of businesses still rely heavily on applications like these to manipulate data, create reports and make business decisions. These poor practices such as the heavy reliance on standalone applications also highlight poor data management practices where the manual processing of data is still commonplace in businesses.
 
 

“It’s not optimal, but we’re getting by”

 
This outdated modus operandi is a clear indicator that there are maturity gaps in important data management principles such as data access, data quality and data lineage. Data stored in spreadsheets and access databases limits the audience that can access this data to make business decisions. The wider availability of this data would allow for a greater level of self-service provision so that the right data is available to other users in the organisation.

Using siloed applications makes the job of ensuring the data is accurate and complete very difficult and leads to the creation of multiple versions of the same data. This makes the task of maintaining master data very challenging. Having a data governance model in place is essential to improving data quality and ensuring data compliance standards are met and adhered to. A high reliance on standalone applications also reveals the lack of an enterprise data model which is a type of data model that presents a view of all data consumed across the organisation.

Data lineage is important as it allows for the ability to track data from source through to how it is used. It also helps to build trust in the data being used and in investigative activities such as Root Cause Analysis, where it becomes easier to trace errors.

Data protection and having controls on how that data is accessed is of growing importance in both adhering to regulatory compliance and in cyber security. Lack of these controls increases the likelihood of data breaches. Cyber security at its core involves protecting data from cyber threats and so data governance is essential to cyber security.
 
 

“Out of interest, what am I missing out on?”

 
The manual processing of data highlights the lack of readiness for harnessing the large potential of an integrated system landscape. The lack of a joined-up approach to data management and having poor integration between your systems will hamper efforts to make effective business improvements. The benefits of an improved system landscape are discussed in another blog called ‘Are poor System Integrations slowing down your transformation efforts?’.

McKinsey’s report on digital transformation in 2018 reported that more than 70% of transformation projects fail.

Some of the greatest challenges in improving data quality highlighted in the report were:
 

• The poor quality of data entry at the system of origin
• Inefficient data architecture
• Ineffective governance model

In the same report, the key factors for successful transformations were:
 

• Tools that allow information to be more accessible throughout the organisation
• The ability for self-service for employees and business partners

Improved data management is therefore a tangible incentive to improve current practices that will vastly improve operational efficiency, provide greater adherence to regulatory compliance standards, and support your cyber security measures.
 

 

Conclusion

 
Minimising the reliance on standalone applications to make business decisions and providing greater access from integrated systems, will allow for improved data management practices. These changes are in turn a good indicator that data is being stored systematically. The automation of what were manual business processes workflows will bring greater efficiency, consistency, and data quality.

The use of data lakes where structured and unstructured data from a vast range of sources can be captured, stored, and updated instantly will greatly improve data management. These initiatives coupled with other enabling solutions such as the adoption of cloud technology, allows organisations to be more innovative, allow for improved cyber security measures, and be more adaptive to change.

Organisations that have in place strong data management practices will be in a better position take advantage of new business opportunities at a much faster rate than their competitors.

What steps are you or your organisation taking towards improved ways of working?

In many organisations digital solutions are deployed to improve operational effectiveness and to promote better ways of working. Eliminating outdated and inefficient processes and utilising new technologies allowing greater collaboration, automated workflows, and faster access to business data are some of the benefits that can be achieved.
Challenges can include mismatched data types, systems not supporting modern protocols, security inadequacies or simply cost. For example, SAP use proprietary BAPI interfaces which work well if you are integrating other SAP systems, but not well if you are attempting to use anything else. Often the challenges lead to integration being shelved in favour of dual keying data into more than one system. Once dual keying is accepted it can often be left in place for a considerable time which in turn leads to human generated errors.
 

Longer term strategic planning vs short term solution benefit

 
The system integration conundrum for many organisations lies in weighing up the cost and time required to undertake the necessary landscape/technology stack review and to develop a simplified integration strategy versus the cheaper and quicker system integration with workarounds/patches approach.



In many cases the proposed digital solution satisfies an immediate business pain-point or need which can make for a compelling case for the fastest deployment option. Workarounds and patches serve a purpose in the short-term to alleviate limitations, however inadequate process and systems integration also can increase complexity and costs over time.
The seamless connectivity of enterprise data is a key enabler for digital transformation success and requires careful planning and a staged approach.
 
System Integration planning should consider the following two-stage process:

Stage 1 – Assess the current operational landscape (AS-IS)

• System landscape review - review and identify all the systems, including legacy and middleware, identifying each systems purpose and its connectivity (or lack of) to other systems. This should only be a high-level preliminary review based as for a large organisation this type of analysis could takes many months if not years just by itself.
• Business Process Mapping – having up-to-date business process maps will identify manual processes and workarounds that are currently in place and will also highlight where operational improvements can be made.

Stage 2 – System Integration planning (TO-BE)

• Operational landscape simplification – identify improvements and eliminate the manual processes
• Data integration review – review and understand your data integration needs. This will identify the interface types required for system integration. An up-to-date data model will be a key output
• Middleware consideration- identify the appropriate middleware to enable the planned architectural changes, where possible using existing middleware if the capabilities are sufficient.
• Future vision – the output of the System Integration planning is an input to a digital transformation roadmap.

 

The benefits of a simplified systems landscape

 
The benefits of a better integration ecosystem include:

• Elimination of costly manual processes
• Automation of critical transaction systems
• Greater visibility of end-to-end data flows
• The potential replacement of costly legacy systems where integration may make systems redundant (such as when dual keying)
• Increased revenue thorough improved business processes
• Better decision making
• Adoption of new business models
• Easier integration with external systems allowing greater supply chain integration
• Ability to scale digital transformation efforts
• Maintain lower ongoing costs

 

Conclusion

 
System Integration planning is an important element in enabling digital transformations and the realisation of an integrated platform that allow for a faster realisation of your transformational goals.

An integrated system will streamline your processes, reduce costs, and ensure efficiency.

Not reviewing and simplifying your system integration landscape may lead to poor decisions being made and not being able to harness the full benefits and growth opportunities of digital solutions when deployed.


Please share your thoughts on this discussion topic which is a key enabler to successful business transformations.

 

The Data Historian is a time-stamped database that stores Real-Time operational data that can be accessed and used for a wide range of purposes such as visualisation, event tracking, production reporting, and consumption by other systems for Advanced Analytics and Asset Performance Management. Market Research Future ® report that The Global Data Historian market is estimated to reach USD 1.4 Billion by 2024.

With the onset of new and disruptive technologies, data lies at the heart of these transformation initiatives. With improved computing and network infrastructure the demand for data has grown increasing the importance and value of a data/industrial historian in Data Management.

 

Operational Excellence opportunities

Operational Excellence is the realisation of executing your business strategy effectively and reaping the benefits of Continuous Improvement initiatives. There are many focus areas that contribute to this objective such as a leadership engagement, commitment to quality, and a strategic focus.

The Data/Industrial Historian enables the capture, storage, and usage of plant data for a variety of things. The list below highlights just some of the operational focus areas that data stored in historians can be used for:
 

• Process Automation
  • Advanced Process Control
  • Real-Time Optimisation
  • Calculations (e.g. detailed operational calculations or composition estimators for inferential control of distillation columns)
• Visualisation of Operational and Production Data for analysis and troubleshooting
• Operational Dashboards and Reports
• Production and Event Tracking
• Environmental Auditing
• Asset Performance Management
• Predictive Maintenance
• Big Data and Advanced Analytics
• Digital Twin technologies
• Machine Learning and Artificial Intelligence

 

Digital Transformation Enablers

Initiatives such as Industry 4.0 and the Internet of Things (IoT) have great value potential for the process industries and manufacturers. At the core of these technology solutions are data platforms and data management. With the right strategy, technology selection, and delivery approach, lie the promise of scalable and tangible cost benefits and the unlocking of new business opportunities.

From a data architecture infrastructure perspective, we now have more options available to us to store operational data that includes, on-premise, cloud, edge, and the data lake. Each option has its own technical merit and so there needs to be careful consideration and a enterprise strategy developed that considers the data, the operational processes, business requirements and future growth plans.

This will lead to determining an optimal data architecture infrastructure potentially utilising newer technologies such as cloud and edge infrastructure. This improved architecture can allow the integration of unstructured data and IoT data opening further opportunities to improve operational performance and streamline operations.
 

 

Having a clear plan and a targeted approach

So, what are the steps we need to take on this journey?

1.    Carry out an audit of your Data Historian as this will highlight how it is being used currently.
2.    Undertake an internal assessment of your operational activities as this will highlight opportunities for improvement and identify opportunities to improve or leverage the use of the process historian
3.    Establish a roadmap for making improvements that consider remedial actions, quick wins, and longer strategic objectives
4.    Undertake a pilot project and adopt an agile and scalable approach
 

 

Conclusion

With the substantial operational and productivity improvements that other newer technologies such as IoT, Advanced Analytics and AI can provide, at the core of each is data.

By ensuring your data is collected, secure, accessible, and of high quality, it provides the foundations to making tangible and scalable digital transformation improvements to your operations.

The benefits realisation will be reduced costs, better data-driven decisions, improved cybersecurity, and the creation of new business opportunities.


Contact us to see how we can help you with improving your Data Historian usage and increase your competitive edge.

Data is at the core of every business and how organisations maintain, govern, and store their data will either hinder or fast-track their efforts to reshape their business models by harnessing the immense potential of data.

With the many changes taking place to respond to our changing current working practices lieu of the worldwide pandemic, there also lies the opportunity for a Data Management review to fully harness the benefits that digital solutions can provide to improve your operations and business performance.
 

Taking a time out to rethink your current strategy

 
Much like in several sports like basketball, a time out allows us to take stock and modify our current strategy. A review of your current data management strategy and practices in a time when we are having to make other strategic changes could provide opportunities for a co-ordinated effort.

Cross-functional collaboration will help to reduce siloed actions that may address specific needs but do not take account of the wider organisational picture. The strategy review can also help to provide clarity on feasible options for the future.
A review of current practices using a framework such as a Data Management Maturity (DMM) model for example, will allow organisations to evaluate their capabilities and build a roadmap to accelerate progress in delivering value to the business.

A data management review will allow for the identification of:

• Data quality issues and the need for improved quality rules and data governance
• Regulatory Compliance issues
• Operational inefficiencies (e.g. workarounds, lack of system and data integration) that if addressed would provide considerable benefit in reducing operational costs and improved efficiency
• Legacy systems integrated with old interfaces that are costly to maintain
• The level of readiness for strategic digital transformation initiatives

 

Now for the team talk

 
A team talk provides the opportunity for a review and active discussion. The data management review should focus on a current state assessment and a capability gap analysis. Key dependencies should also be factored as they will need to be part of the corresponding implementation plan.

Key outputs of a data management review will include:

• A vision statement
• Programme scope
• Major gaps identified in the current state resulting from a Data Management assessment
• Identification of high-level roles and responsibilities
• Success measures and metrics
• Business benefits

 

Drafting the new strategy

 
Keeping with the sporting theme, we are ready to draw up the new plan of attack. Having completed the review, an implementation plan can be drafted. This will typically contain a mixture of remedial changes and strategic implementations that all serve to improve operational performance and achieve strategic objectives.

The ensuing activities need to align to a strategic roadmap. By doing this, goals will emerge that are more specific as they represent desired achievements in quantifiable terms.

Examples of key activities in an implementation plan are:

• Establishing a new governance model
• Implementing remedial improvements in a phased and prioritised manner that eliminate manual processes and workarounds (AS-IS) to better defined business and process workflows (TO-BE)
• Carry out a pilot Master Data Management (MDM) or Data Historian implementation ahead of further phased rollouts
• Implement cloud-based data management solutions for improved data access, scalability, and cost savings
• Deploy solutions that create new market opportunities

 

Putting words into action

 
Having had the time out, the team talk and made the required adjustments, we can put to action the cumulative effort of taking stock and producing an improved strategy. The approach should be an agile one that allows benefits to be realised early and incrementally.

The cost benefits of this approach will be both immediate and long term. The immediate benefits will be realised from short term improvements and quick wins. Medium-term and long-term improvements would then follow, and having a strategic approach will have the added benefit of:

• Greater Integrated Operations with users sourcing data from trusted sources
• Improved data availability for business processes that require consolidation and aggregation such as data analytics, business intelligence and KPI reporting
• Reduced project delivery costs through simpler and optimised data and system integration
• An integrated architecture platform
• Incorporating related cyber resilience initiatives
• Improved readiness for unlocking further value by implementing Machine Learning and other Artificial Intelligence solutions

 

Conclusion

Given timely and accurate data is key for making the right business decisions, a review of your data management strategy will allow you to address operational inefficiencies and risks. Addressing the issues identified in the assessment will bring about performance improvements, increased profitability, and better operational practices.
It will also allow you to improve your readiness to implementing strategic solutions and potentially reducing the cost of these implementations and their ongoing maintenance costs by way of improved Data Architecture and System Integration capabilities.
 
Contact us to see how we can help you with your Data Management Strategy.
 

COVID-19 is having a profound impact on all our lives personally and professionally, from the need to stay at home to minimise the spread of the disease, to the limiting of social contact to minimise the spread. There is now a gradual easing of restrictions in lieu of a vaccine, to try and ensure a safe return to anywhere near how our lives were before the outbreak of the pandemic. What we do not know at present is the speed at which we will be able to return to the same levels of activity levels before the pandemic.
 

What is the immediate impact to our working practices?

The challenge of this gradual ramp up is that for many of us we cannot carry out our everyday work activities in the same way as we did before the outbreak. For some it was the ability to have in person meetings and for others it was the ability to carry out their regular day-to-day work activities.
We are already leveraging technology for our communication needs as highlighted in the increased usage of collaboration tools such as Zoom, Slack and Microsoft Teams. Adapting to having a remote workforce has forced changes to our work practices.
 

What can we do to overcome these work-based challenges?

Remote working practices calls for the increased need for digital solutions to achieve this new modus operandi. These new ways of working were a longer-term priority for organisations prior to the pandemic and now need to be brought forward.

For many organisations, there are still activities that are carried out manually involving personnel from different departmental groups. The implementation of web-based automated workflow solutions could solve the problem and provide value-added benefits in data quality, customised reminders & alerts, and timely approvals. Productivity would be significantly improved, and new solutions could facilitate the necessary collaboration required to complete the end-to-end activity.

For industrial sectors such as Energy and Manufacturing, utilising sensor technology and mobile applications to automate data capture workflows and improve work processes in maintenance routines and integrity management activities.

Implementing an Identity & Assessment Management (IAM) solution for example, would allow users to use a single log in to access their applications, rather than logging in to each system individually. Security profiles can be enhanced and the consolidation of user identities and passwords with Single-Sign-On (SSO) functionality make it easier for IT departments to audit where and how these user credentials are used.
 

How do we start on this journey of change in the workplace?

• Work within your organisation to clearly understand the pain points. For most organisations, the answers lie within their workforce carrying out these activities in dedicated teams to drive the innovation. This approach will aid in the identification potential solutions, such as a need for the increased use of mobile applications or the implementation of cloud solutions that enable remote collaboration.
• List out your strategic goals and the benefit of each. These can be ranked and prioritised so that you can go about undertaking those that can provide immediate benefit and large cost savings.
• Have a roadmap to deliver the strategy to implement these changes, and intermediate checkpoints to evaluate progress, cost savings and other benefits gained.
• Take an agile approach so that there is flexibility to make the changes required incrementally but also provides the ability to scale up having achieved an improved business formula.
• The re-skilling of your workforce and the possible creation of new roles will be an important consideration to ensure your organisation is equipped to deliver new business models.
• Clear and regular communication will be important as this pandemic has forced change upon us as a society and so there needs to be a concerted effort to help the workforce adapt to new working practices and maintain their well-being.

Conclusion

A considered and inclusive approach is required to make positive strides towards a new of working that maintains embraces a new way of working, leveraging digital technology that will facilitate the change and enhance your competitive edge.

What are your thoughts on the necessary changes your organisation needs to embark upon to address this immediate challenge of our time?