Digitising the factory and delivering an Industry 4.0 roadmap allows an operator to realise vast improvements, efficiencies and consistency, says Adrian Champion, director of Jakarta-based Adhinata Consulting, which provides IIoT technology and engineering software solutions within Indonesia, Singapore and South-East Asia.

Let’s Go Digital

In recent years, the buzz word in the manufacturing has been “Industry 4.0”, and there has been an ongoing trend to try and adopt Industry 4.0 in the food manufacturing industry as part of the roadmap plan for the factory of the future.

Being presented with an array of physical systems, control systems and multiple software applications can become a huge challenge as part of adopting an Industry 4.0 roadmap to the average plant or factory. Whilst a big emphasis is placed on the benefits of IIoT (Industrial Internet of Things) and Industry 4.0 for production, equally important is the management of factory engineering data.

Back To Basics

Ensuring factory reliability is critical in the food industry, as unreliable assets directly and indirectly cause unreliable processes. The failure of an asset does not just lead to unscheduled downtime and loss of production—it also has a significant impact on many other processes and can ultimately have an impact on performance and profitability.

Today’s factory engineering imposes specific requirements to remain consistent in order to be able to become the cornerstone of the digital factory, especially for the plants of the future. As such, a smooth networking of disciplines, engineers and information is required.

Mass data and manual work have been incompatible for quite some time. They are far too time-consuming and prone to errors, as is “excel engineering” which is still often practised with its self-made lists that constantly require manual revisions or import and export steps. Utilising a central data model-based software such as Aucotec’s Engineering Base as a single source of truth for all engineering data provides Operators with a Digital Twin.

Why Digital Twin? 

A digital twin is a virtual model of something in the physical world, such as a product, a process (a production line), or a facility (factory). Digital twins are frequently referred to as bridges between the physical and the digital.

What makes a digital twin different from a drawing, schematic, or other more traditional representation is that it’s dynamic. Digital twins don’t just represent the physical system itself, they also represent all of the information embedded in that physical system. As such, they change in response to contextual information, which means manufacturers can use them to assess how changing contexts (e.g., different inputs, environmental factors, the wear and tear of aging) will affect the physical product. Digital Twins also allow stakeholders across the organisation to have access to the same real-time picture of the physical asset. Digital twins provide benefits across the whole lifecycle of an asset or a project.

• During the engineering phase, a digital twin allows engineers to test different designs and expose them to different contexts in advance. This means they can find any inefficiencies, mistakes, and other opportunities for improvement before any time, energy, or money is spent on production.
• Keep costs caused by change to a minimum. We all know that engineering projects are all subject to constant change across all phases:

• • frequent design changes,
  • changing design requirements,
  • projects staffing,
  • increased product complexity, and
  • problems/errors that are found too late.

• During the buildphase, a digital twin helps improve efficiency, quality, and yield—for example, by understanding the effect of a production change.
• Finally, digital twins also facilitate the operations phase. Products, plants, and processes age, and as they age, things drift. A digital twin can drift as well, which helps manufacturers understand, adapt to, and correct the changes happening in the real world (e.g., planning maintenance schedules).

How can the food industry use digital twins and IIoT to improve operations?

Lifecycle management 

By using digital twins, one can trace and manage a project throughout its entire lifecycle. This allows operations to be optimised along the way and lessons to be learned in order to improve projects in the future.

Alerts and early detection 

Having internet-connected equipment allows the detection of any abnormalities in that equipment before they become costly problems. Having a digital twin of an entire facility allows this to be done on a much bigger scale.

Aggregating data

A single digital twin represents the information contained in a single physical object. But digital twins don’t have to stand alone. If an organisation is monitoring multiple systems of the same type of assets, it can start to learn from all of them as a cohort, find similar patterns or trends, and that analysis can lead to refining models for higher fidelity in the future.

Supply chain visibility

Supply chain visibility is both essential and challenging, where ingredients may be sourced from a wide variety of vendors, and new regulations demand complete transparency. With IIoT, manufacturers have real-time visibility into everything that happens to their products—before, during, and after the actual manufacturing process.

For example, through the use of sensors, food manufacturers can determine if their products have been exposed to temperatures, pressures, or other environmental conditions that might render the food unsafe to eat. Having this knowledge ahead of time can save companies millions of dollars in recall costs.

Cross-facility operations analysis

How does the efficiency and product quality compare to what’s happening at a sister facility?

In the past, this question was difficult to answer in any great detail. But today, manufacturers can use IIoT to gather and analyse data from multiple facilities. From this vantage point, they can make better decisions related to operations efficiency, quality control, and so on.

Automation

IIoT is, essentially, machines talking to one another, without a person interfering (and introducing errors). This enables more automation solutions, which manufacturers have been adopting in droves, resulting in both increased efficiency and higher-quality, more consistent products.

Safety

This might not be the first thing that comes to mind when IIoT is discussed. But with the recent 78% increase in OSHA penalties, companies can use all the help they can get when it comes to plant safety.

Safety in factories has always been there, but no one has ever really collected data. That could help in compliance and auditing. Tracking safety events that lead to downtime, companies may be able to identify trends and put measures in place to prevent them from happening.

Maintenance management 

Crafting better (i.e., predictive) maintenance schedules is already one of the main ways food production is using IIoT. Digital twinning takes this ability up a notch, not just providing real-time insight into device or equipment performance, but also allowing one to visualise the impact of different courses of action to minimise any losses due to downtime.

Predictive maintenance and reducing unplanned downtime

Unplanned downtime is estimated to cost process industries about 5% of production per year—that’s a huge loss. And it’s largely preventable by taking a strategic approach to maintenance. A digital twin provides the plant-wide data manufacturer’s need to make data-driven decisions about maintenance activities and craft better and more effective maintenance schedules from real-time insight into equipment and its performance. Manufacturers can assess the performance of equipment, determine what equipment has the most risk, and weigh factors including the costs of repairing or replacing equipment against the costs of downtime. Manufacturers can also find and fix small things pre-emptively before they become big problems.

Predictive maintenance and condition-based monitoring are typically implemented as part of an overall IIoT-driven predictive maintenance solution.

The 3 main elements for optimum success are:

1. Controlled Management of all Engineering Data that allows automated and real-time updates to CAD drawings, and other 2D engineering data–this is the Single Source of Truth for real time factory data and Digital Twin.
2. Control Systems interpreting operational events in the factory e.g. sensors on equipment feeding back operational data based on conditions like speed, temperature, uptime, etc.
3. A Predictive Maintenance (PDM) system that can communicate and be fed with information both from the Control systems and the Factory engineering data. The PDM system will take the localised data and perhaps other data from the internet to analyse and determine the optimum time for maintenance.

This approach will ultimately drive the improve the reliability and repeatability of the maintenance process and facilitates continuous improvement, enabling operators to spend less time analysing

data and more time taking action. The number one objective in using Industry 4.0 solutions is to gain better visibility into and control over business-critical equipment.

A common problem for all manufacturers is how much time per year is wasted by manually managing engineering data with a constant risk of documentation being incorrect or out of date.

Digitising the factory is something all operators should be considering now and the benefits are significant by using a digital twin as part of the solution:

• Save 30-40% overall in engineering time
• Better utilisation of engineering staff
• Engineering data always current, up-to-date and consistent – The Single Source of Truth
• Fix problems in Engineering and see a natural reduction in challenges for operations, production and bridge the gap between engineering data and other parts of the business processes.
• Use the digital factory as the cornerstone to drive organisational change and improvement across the whole of the business.

Conclusion

Siemens recently cited: “The potential Global Digitalisation Productivity Bonus across all manufacturing sectors is estimated to be between 6.3 percent and 9.8 percent of total annual revenue by 2025.” Digitising the factory and delivering an Industry 4.0 roadmap allows an operator to realise vast improvements, efficiencies and consistency.  Eliminating many manual processes, will deliver huge benefits as Industry 4.0 and IIoT help bring together all parts of the organisation and more importantly, the people.