post img

An Overview of the Industry 4.0 Cybersecurity Risk Mitigation Process

In previous blogs in this series, we looked at one of the unintended consequences of implementing Industry 4.0 technologies and processes – the increased attack surface. In other words, the more equipment you connect, the larger the target for would-be attackers. We also looked at the challenges of dealing with this Industry 4.0 cybersecurity issue, and we outlined the main best practices.

In this final blog in the series, we outline the Industry 4.0 cybersecurity risk mitigation process. This process will help you overcome the challenges of cybersecurity and protect that ever-growing attack surface.

There are three main parts of this risk mitigation process:

  1. Assess
  2. Secure
  3. Monitor

1. Assess Industry 4.0 Cybersecurity Risks

Cybersecurity Maturity Assessment

This part of the process starts by conducting a cybersecurity maturity assessment of your organisation with the aim of identifying risks to OT equipment and systems.

Risk Evaluation and Prioritisation

Once risks are identified, they need to be evaluated and prioritised to assess the probability of occurrence and the level of harm that could be caused. Those with a high probability of occurring and a high level of harm should be the highest priority.

Remember, however, that cybersecurity risk evaluation is not just about looking at the immediate threat. You also need to look at the root cause.

For example, one area that might be identified as high risk is malware knocking systems offline or putting data at risk. The probability of this occurring is high as malware attacks are commonplace. If such an attack is successful, it is likely to have a significant impact. Therefore, it makes sense to mitigate this risk.

Further analysis might reveal there is insufficient monitoring of malware and a poorly configured firewall. There might also be security patches that have not been applied.

You should put in place mitigation measures to harden these technology and process weak points. However, this doesn’t necessarily get to the root cause of the problem. Let’s track it back in reverse order:

  • Malware isn’t spotted, so it gets control of part of the system.
  • It got through the firewall because it wasn’t configured properly.
  • It got to the firewall because a security patch wasn’t applied to part of the system.
  • The malware got into that part of the system because an individual with access used a USB device on a connected piece of equipment.
  • The use of USB and similar devices is widespread because of a general lack of understanding of the cybersecurity risks they pose.

There is definitely a technology problem in the above scenario. However, when you track the issue back to its root cause, it is also a people problem. Therefore, you need an end-to-end solution that includes technology, processes, and people:

  • Technology – improve malware monitoring and firewall configuration
  • Processes – ensure security patches are properly applied and develop a policy on the use of USB and similar devices
  • People – conduct regular training for staff on cybersecurity risks and how to mitigate them

Once you have identified, evaluated, and prioritised the risks, the next step is to identify mitigation measures.

2. Secure Your OT Equipment

In this part of the risk mitigation process, it is important to understand that while a holistic approach is essential, the practicalities and realities of IT and OT can be contradictory.

This especially applies in fields like pharmaceutical and medical device manufacturing, as there are patient safety and compliance requirements. This creates situations where IT systems can be completely locked down for cybersecurity reasons while a certain degree of openness is required for OT systems to allow data to pass through.

Another example of the practical differences is the application of security patches. In most IT systems, security patches can be applied immediately. Greater care must be taken with OT systems, however, as the patch itself must be risk assessed and qualified. This is because applying a security patch could reduce the availability of a piece of equipment, impacting essential metrics like production line output and OEE (overall equipment effectiveness).

This is before you even consider the fact there will be OT systems and equipment operating on production lines that are no longer supported by the manufacturer, so security patches are not being developed.

It is also important to take into account the nature of the equipment and systems being used, as this will also impact the steps required to make them secure. In IT security, equipment and systems are likely to be relatively new, while in the OT environment, it is not unusual to see equipment and systems that are decades old.

During the process of developing mitigation steps to secure your OT equipment against identified risks, there are some key points to consider:

Identity and Access Management

Identity and access management are essential cybersecurity components, but they are particularly important when third-party contractors are working on OT systems. If the contractor is physically present on the factory floor, they will have gone through the company’s security protocols. However, it is now increasingly possible for engineers to remotely work on manufacturing lines and equipment. Security measures for remote access are often much weaker than those in place for physical access, so there is usually room for improvement.

Use Reliable Partners and Vendors

It is also important to use trusted vendors and engineering teams that prioritise security when developing or updating systems and software. A track record of success is important too.

Reduce the Attack Surface Where Possible

A lot of the focus of Industry 4.0 cybersecurity involves securing the increasing attack surface, but there are also steps you can take to reduce potential access points for an attack. This includes removing unneeded systems and equipment

3. Monitor the Effective of Your Cybersecurity Risk Mitigation Measures

The final step in the risk mitigation process is the continuous monitoring of your mitigation measures, including through the use of automation and machine learning technologies.

You should also build in levels of redundancy wherever possible, so there are alternatives if a system or piece of equipment has to be taken offline for cybersecurity reasons.

Ensuring maximum resiliency is important, too, including ensuring you take regular system and data backups. You should also have an up-to-date disaster recovery plan, and it should be regularly tested, assessed, and reviewed.

An Ongoing Process

The process outlined above should become a constant feature of your operations, given the increasing fluidly of the manufacturing sector and the constantly changing nature of the cybersecurity threat profile. Prioritising cybersecurity and continuous vigilance are the solution.

Read More

post img

Taking a Holistic Approach to Cybersecurity in the Transition to Becoming a Smart Factory

In a recent blog, we highlighted the cybersecurity risks that are created by the increasing attack surface in manufacturing organisations. As a quick recap, as you integrate systems, platforms, and equipment, and as you connect elements of your operation to the cloud, the potential attack surface in your organisation expands, exposing you to higher cybersecurity risks.

In our previous blog, we also highlighted the key to mitigating these risks – taking a holistic approach to cybersecurity as you transition to a Smart Factory. This means integrating your IT and OT (operational technology) teams and developing cybersecurity strategies, processes, and mitigation measures that cover all aspects of your organisation’s technologies. This includes everything from your Manufacturing Execution System to the cloud applications used by your accounting team to the PLCs and SCADA systems running on the factory floor to the CRM used by your sales and marketing team.

What does this holistic approach to cybersecurity mean, though? What are the practical steps that should be taken by pharmaceutical, medical device, and technology manufacturers?

The Challenges of the Increasing Attack Surface

A good starting point is to have a clear understanding of the scale of the challenge when you integrate and connect devices and therefore increase the attack surface and potential risks. Some of the main points include:

  • Many OT legacy systems have complex cybersecurity vulnerabilities.
  • One of the reasons for the above point is the fact that OT equipment is traditionally older and less adaptable to change.
  • Software upgrade and security patching processes often lack structure.
  • The process of rolling out updates and security patches is more challenging with OT equipment. This is because OT equipment directly controls the production process. As a result, each upgrade and security patch must be risk assessed and qualified.
  • Visibility across the entire operation is limited.

Industry 4.0 Cybersecurity Best Practices

A crucial component of Industry 4.0 cybersecurity is to make sure there is correct OT/IT bridge separation to isolate and protect OT equipment from external threats. This OT/IT bridge separation will also provide protection against the internal risks that are often present in large corporate IT networks.

This protection of OT equipment requires the implementation of robust architecture during connectivity design. This architecture needs to allow data through while at the same time preventing inward threats.

Other essential Industry 4.0 cybersecurity best practices include:

  • Make cybersecurity an integral part of your smart manufacturing strategy.
  • Take an end-to-end approach to cybersecurity that includes technology, processes, and people.
  • Put in place a cybersecurity governance programme covering both IT and OT. This includes developing comprehensive cybersecurity procedures, controls, and policies. These procedures, controls, and policies should also be regularly reviewed and updated.
  • Put in place a strategy to continuously raise awareness of cybersecurity risks. This should apply at all organisational levels and should ensure constant vigilance while also providing education on new and emerging threats.
  • Implement a strategy of continuous cybersecurity skills improvement throughout the organisation.
  • Continuously focus on emerging threats as well as existing threats.

Getting it Right All the Time

There is a difficult and unfair reality about cybersecurity that is universal – those who seek to attack your organisation only have to be right once, whereas to properly protect your OT systems, you have to be right all the time.

This fact should not be a barrier to continuing on your Industry 4.0 journey as there are too many benefits to be ignored. However, cybersecurity considerations must be a core priority in everything you do and at all levels of the organisation.

Read More

post img

How Good Distribution Practice (GDP) differs from Good Manufacturing Practice (GMP)

Good distribution practice (GDP) and good manufacturing practice (GMP) are quality standards and guidelines that have the same ultimate objective – to ensure medical device and pharmaceutical products are safe, meet their intended use, and comply with regulations.

GMP focuses on manufacturing processes, while GDP covers distribution activities. There are crossovers between both manufacturing and distribution, however. So, what are the main differences between GDP and GMP?


To understand the key differences and how they impact operations, let’s first look at the definitions of GMP and GDP.

What is Good Manufacturing Practice?

Good manufacturing practice involves consistently producing products that meet quality standards. This requires the implementation of a system where the aim is to minimise risks, from incorrect labelling of products to contamination to incorrect ingredients and everything in between. GMP systems cover all parts of the production process, from raw materials through to the production of the finished product.

What is Good Distribution Practice?

Good distribution practice involves maintaining the quality and integrity of products through all stages of the supply chain. It sets out minimum standards to ensure medical device and pharmaceutical products comply with regulations. GDP applies to warehousing, storage, and transportation, and it covers everything from storing and transporting products under the right conditions. Doing so, minimises the risk of product degradation, ensuring product integrity at the correct destination on time.

Unique Aspects of GDP

There are parts of GDP that are unique, so they don’t apply to GMP guidance and standards. Those unique parts of GDP include guidance on transportation covering aspects such as temperature control, vehicle controls, and conducting risk assessments on transport routes. Guidance on brokers is also unique to GDP, i.e., guidance on those who facilitate transactions in the supply chain without ever handling the product.

Areas of Minimal Difference

While there are sections of guidance that are unique to GDP, there are also areas where there is minimal, if any, difference between GDP and GMP. These include:

  • Quality management – both focus on ensuring members of staff are properly trained, and the facilities and equipment are fit for purpose. Management review meetings also feature in both, albeit with a bit more emphasis in GDP.
  • Outsourced activities – minimal difference.
  • Self-inspections – minimal difference.
  • Complaints and returns – there are more details in GDP covering the control and management of product returns. Otherwise, there is minimal difference between the two standards.

Main Differences Between GDP and GMP

Aside from the unique aspects of GDP compared to GMP, the differences fall under four main headings:

  • Personnel
  • Equipment and premises
  • Documentation
  • Operations


GMP talks about the role of Qualified Person while the focus of GDP is the role of Responsible Person. This isn’t just a difference in words, though, as a Responsible Person has different responsibilities under the guidance. They can’t, for example, certify the release of a product batch, whereas a Qualified Person can.

Equipment and Premises

The main difference between GDP and GMP is the additional controls included in GDP to cover products and materials that are either radioactive or highly sought-after on the black market.

There can be blurred lines in this area, though. Take the thermal mapping of storage areas as an example. Many facilities working to remain in compliance with GMP will have thermal mapping systems in place, even though they are not specifically required by GMP. However, thermal mapping is a requirement under GDP.

GDP also puts more emphasis on instrument calibration, particularly those instruments involved in product traceability processes.


As might be expected, there is a reduced requirement in GDP for manufacturing and testing documentation, but an increased requirement for storage and personnel documentation.


In GMP, these guidelines are covered under production, so the heading is different. In terms of the more practical specifics, GDP places more emphasis on fake product identification, supplier and customer approval, and exporting processes.

Not Just About Regulatory Compliance

Both GDP and GMP are important components for maintaining regulatory compliance when selling medical device and pharmaceutical products. The standards also have other benefits to businesses involved in these processes, including manufacturers as well as those further down the supply chain.

This includes helping to reduce the prevalence of fake medicines, improving the quality of products produced and delivered to patients/clinicians/customers, and minimising business risk in the manufacture and distribution of healthcare products.

Read More

post img

The Cybersecurity Risks Created by Industry 4.0’s Increased Attack Surface

Businesses in all industries face an ever-increasing range of cybersecurity risks. This includes companies in the manufacturing sector. When you look at regulated industries such as pharmaceutical and medical device manufacturing, where patient safety is a top priority, cybersecurity risk levels can be even higher.

Industry 4.0 technologies and solutions almost always modify existing risks or create new ones. As a result, it is essential that cybersecurity is prioritised throughout every stage of every project that comes under the umbrella of Industry 4.0, digital transformation, smart manufacturing, and industrial automation.

It is also important to take a step back to take a wider view of cybersecurity in your organisation. This is because of one of the unintended consequences of Industry 4.0 technologies and solutions – the increased attack surface.

What is the Increased Attack Surface?

Some of the objectives of Industry 4.0 technologies include integration and the deepening of connections between systems. This can be systems on the production line, within the supply chain, or in other parts of the organisation, i.e., sales, R&D, purchasing, accounting, etc.

Even systems that previously operated in silos can be brought into the new connected structure to make better use of data and to make efficiency savings and productivity gains.

However, each integration step you take on your smart manufacturing journey increases the cybersecurity attack surface that exists in your organisation. The same applies to every new connected device, platform, or piece of equipment. Connecting to cloud services and other resources external to the organisation significantly increases the attack surface too.

In other words, each new system or machine you integrate or connect is another potential target of attack.

You might even have legacy systems that were never designed to deal with the cybersecurity challenges that currently exist, never mind those that are yet to emerge.

Secure IT is Not Secure Enough in the Smart Manufacturing Era

One of the key cybersecurity challenges as manufacturers transition to smart factories is the fact that IT and OT (operational technology) are not synced up in terms of cybersecurity.

This situation arises because the team responsible for IT security is likely to have limited input in OT decisions and processes, plus there is generally no equivalent OT team responsible for security. In many situations, this can leave organisations unprepared for the enhanced cybersecurity threat created by the increased attack surface.

Dealing with These Enhanced Cybersecurity Threats in Smart Factories

The Industry 4.0 concept of integration is a crucial part of the solution to the cybersecurity risks created by expanding attack surfaces. We are not talking about integrating systems or equipment, however, but teams.

In practice, this means approaching cybersecurity in a more holistic way across all parts of the organisation, taking into account both IT and OT.

This integrated approach to cybersecurity will mitigate the threat of the increasing attack surface. For example, dealing with the gaps that arise in maturity assessments.

Cybersecurity maturity assessments are typically performed periodically. The time between assessments can often be too long, but there is also the issue of new Industry 4.0 solutions being implemented between cybersecurity maturity assessments. These solutions can increase the attack surface further, increasing risks and rendering the assessment out of date even though it is the most recent.

Taking a Holistic Approach to Cybersecurity in Manufacturing Organisations

While integrating your cybersecurity team across IT and OT is the solution, that team will require detailed knowledge of existing and emerging cybersecurity threats. It will also need in-depth knowledge of both IT and OT assets and network architectures, in addition to knowledge of both business and manufacturing processes.

Taking a holistic approach to cybersecurity also involves real-time auditing of IT and OT assets, regular maturity assessments and risk reviews, taking mitigating actions, and continuous monitoring.

While this is a more expansive approach to cybersecurity than currently exists in many manufacturing organisations, it will ensure the maximum level of protection across the entirety of the potential attack surface.

Read More

post img

Build Once, Deploy Multiple Times in Seconds – Cloud-Based Solutions for Pharma & MedTech Manufacturers

For pharmaceutical and medical device manufacturers, repeatability is a primary goal when developing new manufacturing software solutions. The concept is simple – you select a facility or production line for the initial project, creating a software solution to deliver on your manufacturing and business objectives. Once you have that operational and delivering results, you can then look at deploying the solution in additional facilities and production lines.

While the concept is straightforward, achieving this repeatability is close to impossible using traditional on-premises architectures, as the copy of the solution never fully works on subsequent deployments. Even when developers give as much consideration as possible to repeatability, problems still arise. Those problems mean that, in subsequent deployments, code changes must be made, and fixes implemented.

This is because there are always unique features in every subsequent deployment of on-site manufacturing software solutions that aim to be repeatable. Those unique features can exist in a range of areas, including data capture and extraction, as well as the hardware and networking that exists in the facility. Server builds, function delivery, and equipment communication protocols can also have unique elements that prevent fully repeatable deployments. The hardware resources on-site can also present challenges, as can the ability of the system to handle the processes required to copy and redeploy the application.

At SL Controls, we have first-hand experience of these challenges as customers regularly ask us to deploy tried and tested manufacturing software solutions on different lines or facilities. Developing an answer to this repeatability challenge became a key priority.

Building Solutions in the Cloud

Developing cloud-based solutions is the key to overcoming the challenges of achieving full repeatability of manufacturing software solutions for companies in the pharma and MedTech industries. This is because the developer must create a solution that uses remote resources and that also fits within the architecture of the cloud.

So, for example, when a solution is developed to deliver data between a production line and the cloud, that solution must be tailored to the cloud. This makes the solution repeatable on any similar production line or facility, as it uses the architecture of the cloud rather than any on-premises architecture.

Not only are cloud-based manufacturing software solutions fully repeatable, but they can be copied and redeployed in seconds.

Benefits of the Cloud-Based, Build Once, Deploy in Seconds Model

  • Reduce resources required for subsequent deployments – with the traditional approach using on-premises architecture, subsequent deployments of manufacturing software solutions usually require highly skilled developers to implement. With repeatable cloud-based solutions, the process of copying and redeploying is automated.
  • Save time and money – the provisioning of the software solution on additional production lines or facilities takes seconds rather than days, weeks, or months. Plus, there is no requirement for further coding or development, increasing the savings further.
  • Improves agility – having the ability to deploy essential manufacturing software solutions in new environments will make your business more agile and better equipped to react and adapt as required.
  • Ensures greater consistency of deployment – the automated nature of redeployments ensures consistency, prevents configuration drift (where the software solution is changed with each deployment resulting in a situation where there are multiple versions in operation), and eliminates the potential for human error.

New technologies, systems, processes, and practices are making it possible for pharmaceutical and medical device manufacturers to transform their operations, enhance competitiveness, and improve profitability. Successfully, efficiently, and cost-effectively deploying manufacturing software solutions multiple times is one of the challenges to overcome. Cloud architectures provide the answer. To find out more, contact us at SL Controls today.

Read More

post img

The Essential Steps in Your Smart Factory Evolution

Moving your business closer to becoming a Smart Factory is widely regarded as essential to continuing competitiveness and profitability. Driving efficiency savings, making productivity gains, dealing with skills shortages – these are just some of the challenges that manufacturers face, all of which can be considerably mitigated with Smart Factory solutions.

How do you get to the Smart Factory, though? What should you do next, how far should you go now, and what are the priorities? How do you transform your manufacturing operations into a future factory?

Smart Factory Evolution


The answers to these and similar questions are not easy as there is no one-size-fits-all solution. Instead, the many different variables that exist mean the solution is different for every production environment.

Those variables include the production inefficiencies that currently exist, issues with productivity, and the specifics of the product/s being manufactured, as well as the wider business processes for procurement, distribution into the supply chain, sales, and more.

Initial Stages

We’ll look at the essential technical steps that are required to move your manufacturing facility closer to being a Smart Factory, but there are some initial stages that you need to go through before any technical work should begin.

Identifying Priorities and Goals

Wherever you are on your Smart Factory journey, it is essential to identify your current priorities and goals. Those priorities and goals could be something specific, such as introducing a new product to your production line or increasing capacity without the need to hire additional staff. These are ideal opportunities to introduce new technologies that will help with your smart factory evolution.

You might also have more generalised priorities and goals where the aim is to improve efficiency, productivity, decision-making, and/or reliability on your existing production lines. This could be, for example, increasing levels of automation to alleviate recruitment or staff retention pressures, or it could be optimising OEE to maximise profitability.

Smart Manufacturing Audit

Another initial stage you should go through is to audit your current Smart Factory status. This audit should include all aspects of your operation, such as the equipment on your production lines, the current level of systems integration, how you use data, your IT infrastructure and the level of integration with OT (operational technology), cloud vs. on-premises systems, and connectivity.

As well as giving you an overview of your current status, an audit will also tell you what can be achieved by implementing new Industry 4.0 technologies and processes. You can then use this information to identify the opportunities that best match your priorities and goals, and that will deliver the best return on investment.

Technical Steps Towards the Smart Factory

The steps typically involved to move your facility from where it is now to a Smart Factory include:

  • Enabling communication and the collection of data – this is known as Equipment Systems Integration
  • Data visualisation – to make it possible for you to access and process the data collected
  • Automated decision-making – where many manual and time-consuming decisions are automated
  • Machine learning – where the systems in your facility automatically improve by learning from real-time data and the outputs from previous decisions

Step 1 – Equipment Systems Integration

The ultimate objective of equipment systems integration projects is to connect all potential sources of data to a common system. This includes the production line as a whole as well as individual machines and equipment.

It can also include data not directly connected to manufacturing the product, both within your organisation and outside it.

Within your organisation, this includes data from other units and departments in the business, from finance to HR to purchasing to sales and marketing. This process is known as vertical integration, where the focus is on integrating the OT (operational technology) and IT (information technology) aspects of your business.

Vertical Integration Definition


Outside your organisation, the main focus should be on your supply chain. This is known as horizontal integration. It involves integrating with third parties in your supply chain both upstream and downstream to achieve real-time visualisation and communication, and to maximise efficiency.


Once systems are integrated and connected to a common system, you can start making changes that will deliver real and tangible benefits for the company. This includes automating additional processes where it wasn’t possible to do so before because the equipment and platforms couldn’t connect or communicate with each other.

Your system will also be able to collect and store data, setting you up to move to the next step.

Step 2 – Data Visualisation

Once you have gone through an equipment systems integration process, your production facility will have the capability to collect data from a vast range of sources.

This is only one step in the journey, however. The real work begins when you start putting the data you collect to good use. In other words, the data your system collects must have value.

After all, data in itself won’t improve your business. You’ll derive value from how you use that data.

Implementing data visualisation solutions is a key part of this process.

Data visualisation makes it possible for decision-makers to make sense of the data collected. Those decision-makers can then act on what the data tells them. This applies to everyone, from operatives to engineers to engineering managers to CEOs.

Again, however, this is only a step on the Smart Factory journey as the next point, automated decision-making, will improve productivity even further.

Step 3 – Automated Decision-Making Based on Data

Automated decision-making is where you move from a position where people analyse and use data to one where the system makes decisions itself. This can be rules-based decision-making, where you have if-this-then-that scenarios, or it can be data-driven decision-making.

Data-Driven Decision-Making Definition


Automating decision-making offers a range of benefits:

  • Decisions can be taken immediately any time of the day or night as there is no need to wait for a decision-maker to be available
  • Improved productivity and enhanced OEE as a result of eliminating decision-making delays
  • Eliminating the risk of human error in decision-making

One of the most straightforward examples of automated decision-making is equipment maintenance schedules.

Instead of a manager scheduling maintenance according to a timetable created by the manufacturer of the equipment, sensors collect data with that data then used to determine the best time to schedule maintenance. As a result, maintenance scheduling decisions can be based on specific business objectives, such as conducting the maintenance before a failure occurs or completing the maintenance when it will have the least impact on output.

Step 4 – Machine Learning

Following on from the above, the next step is to enable your Smart Factory to learn from the decisions it takes and the information it receives.

So, in the equipment maintenance example above, the system will learn as the machine operates in the live production environment, tailoring the maintenance scheduling decisions it takes accordingly.

Another technology that becomes important in this part of the Smart Factory journey is statistical modelling and digital twins. These technologies allow you to run data-driven simulations to improve processes, plan for production line changes (such as new product introductions), and more.

Furthermore, machine learning opportunities exist with data from all sources, not just the manufacturing element of the business. For example, a Smart Factory with machine learning capabilities can also improve areas like product development and sales forecasting as it gets better at predicting customer trends and other external factors.

Moving Forward

The above is a general guide to the steps typically required on a Smart Factory development journey. What should you do now, however?

At SL Controls, we can help you answer this question through services like digital maturity assessments, Smart Factory road-mapping, automation strategy development, and creating a business case. Find out more today.

Read More

post img

8 Essential Elements For De-Risking An Automation Project

Risks can enter an automation project at just about any stage. Those risks can result in serious consequences from budget and schedule overruns to unplanned downtime, disrupted supply chains, and more.

Therefore, it’s important to de-risk your automation project at every possible stage. This ensures errors are reduced to a minimum, as well as ensuring the project delivers on expectations.

We have a project delivery process at SL Controls that helps to reduce risks in all project phases. There are also factors relating to your organisation and operations that need to be considered. We’ll start with those.

Minimising Risks In Automation Projects – What You Can Do

There are five crucial areas you should look at to minimise risks in your automation project:

  1. Set clear goals – you should have a clear understanding of why you are embarking on the automation project and what you want to achieve. What are the objectives, what has to happen to meet those objectives, and how will you measure success?
  2. Senior leadership buy-in – getting senior leadership buy-in at the earliest possible stage will make the process smoother, as well as reducing risks.
  3. Resistance to change – are there people in the organisation who will resist the change that automation will bring? This situation is not unusual. The key is to ensure you have a well-thought-through plan of how you will deal with this resistance. Examples of things you can do include communicating effectively, addressing fears head-on, and offering training.
  4. Resources and expertise – what resources and expertise will you need for the successful delivery of the automation project? If you are not completely sure of the expertise that will be required, how will you get that information? What level of resources do you have in-house? What are the key skills and capabilities you will need from a third-party provider?
  5. Stakeholder communication – communication is a potential risk factor in automation projects as communication failures can lead to errors, delays, overruns, and other problems. Therefore, you need to develop an effective communication strategy that includes all stakeholders.

De-Risking Your Automation Project At Every Stage Of Delivery

At SL Controls, our project delivery processes focus on de-risking at every stage, from the pre-automation phase through to final commissioning and testing.

Central to this are eight essential elements to de-risking all automation projects.

1. Proof Of Principle

Proof of Principle (POP) involves proving the feasibility of achieving the desired outcomes for the project based on things like the technologies currently available, budget constraints, timelines, etc.

By going through the POP process, we can avoid pursuing goals or targets that are not feasible or achievable and, instead, make changes to achieve the desired outcome. This prevents wasted expenditure.

2. Design For Manufacturing

Design for manufacturing (DFM) is most commonly used when designing new products. It involves ensuring the design of a product makes it easy to manufacture within the cost target.

In relation to automation projects, we adapt DFM principles to ensure we don’t just create a fancy automation solution. Instead, our goal is to develop an automation solution that is reliable, and that improves several key metrics, i.e., OEE, output rate, quality, productivity, etc.

3. User Requirements Definition

A User Requirements Definition is a document we create that specifies what you, as our client, expect the automation project to deliver. It’s not a technical document but should include, among other things:

  • Required functions and features of the solution
  • Workflow of the solution
  • Integration requirements
  • Data requirements
  • Regulatory requirements
  • Life cycle requirements

In terms of the de-risking process, creating and agreeing on a User Requirements Definition is part of the project’s feasibility analysis. It also ensures everyone is on the same page.

4. Design Review Management

The design review is an essential part of de-risking automation projects. In regulated sectors, it’s also a compulsory part of the process and must be properly documented.

A design review tests and evaluates the design of the automation solution against the User Requirements Definition.

5. Factory Acceptance Testing

The goal of Factory Acceptance Testing (FAT) is to ensure the equipment, platforms, and components of the automation solution can deliver on requirements. It is a process that can highlight issues and errors, so it plays a key role in de-risking automation projects.

6. Logistics Management

Logistics management is often taken for granted in automation projects, despite the fact there are so many things that can go wrong, many of which can have significant consequences. A good example is a piece of equipment getting dropped and damaged. This could set a project back for weeks or months.

To mitigate these risks, we insist on using qualified and approved freight forwarders who take essential steps to ensure the smooth teardown, crating, and transportation of equipment and machinery. This includes:

  • Mapping every aspect of the route
  • Planning the equipment required
  • Deciding on the crate specification
  • Selecting appropriate bracing to secure equipment
  • And more

7. Site Acceptance Testing

While the aim of a de-risking strategy is to eliminate risks at the earliest possible stages of an automation project, the Site Acceptance Testing stage is still important. It’s also an essential GMP requirement.

8. Final Commissioning

De-risking in the final commissioning phase includes IQ (installation qualification), OQ (operational qualification), and PQ (performance qualification).

De-Risking Strategy

By emphasizing de-risking in the earliest stages and then focusing on the elimination of risks through each subsequent stage, we ensure the successful and smooth delivery of automation projects, maximising your ROI.

Read More

1 2 3 4 15