Webinar: Lean and Green Food Manufacturing
Updated: Mar 5, 2021
The first of our 2021 webinar series! Get tips, solutions and real case studies from the experts to achieve energy efficiency and savings in your Food Manufacturing Enterprise.
Masterclass Recording (Read the Memories Below):
How to Increase Efficiency and Save Money in Food Manufacturing?
It can sometimes feel that manufacturing and refrigeration power consumption are an uncontrolled cost centre.
Interested in getting the right tools to achieve peak plant performance? Visit the solution's website at: https://www.polestarinteractive.com/energy-management
Or contact us for an IIoT & Energy Efficiency consultancy!
Food manufacturers are also under pressure to cut costs and save money. This has only been heightened by the uncertainty of the Covid-19 pandemic.
Through better energy efficiency, food manufacturers can lower their bills, increase their equipment uptime, and ensure peak performance. An added benefit is sustainability and a lower carbon footprint.
Along with Crowley Carbon, we explore the possibilities for plant directors, facility managers and site engineers. We’ll literally give you more energy this year! Topics to be discussed will be:
IIoT Strategy and how Industry 4.0 Technology can help to boost productivity.
Realistically achieving efficiency from Capex and production.
How to collect and understand production data.
Fast, Simple and Accurate reporting with understandable and relevant KPIs.
Understanding the flow of energy and the energy usage of your facility.
Tools for CSR and CI.
Why there is a need to connect manufacturing plants to Industry 4.0 technologies? To state the obvious, it is to drive continuous improvement. One of the ways of doing it is by generating process optimisation and energy efficiencies that help to save money and cut costs. Industry 4.0 definitely helps to make that a reality.
What is Industry 4.0?
Industry 4.0 technologies include advanced robotics; artificial intelligence; sophisticated sensors; cloud computing; the Internet of Things; data capture and analytics; digital fabrication (including 3D printing); software-as-a-service, the rise of mobile devices; platforms that use algorithms to direct objects (including industrial robots and even simple CNC machinery); and the embedding of all these elements in an interoperable global value chain.
Under the Industry 4.0 model, production can take place in simulated environments and utilise digital tools to model production models, processes, and productive outputs such as performance statistics. The networks of machinery in a smart factory become hyper-aware systems of highly flexible technology, responding rapidly not just to human commands but to their own perceptions and self-direction.
‘Industrial Internet’, IoT, or the ‘Digital Factory’, is one of the big four set of technologies that are key within a Smart manufacturing project. While Industry 3.0 was focused on the automation of single machines and processes, Industry 4.0 focuses on the end-to-end digitisation of all physical assets and integration into digital ecosystems with value chain partners. That requires, setting up stable and safe connectivity to collect, measure, and analyse data to predict and automate business processes. Its relevance is such, that, according to a survey performed by Deloitte in 2020 (The Fourth Industrial Revolution Report), 72% CXOs view IIoT as the set of technologies that will have the most profound impact on organisations.
Opportunities of Industry 4.0 for Food Manufacturers
The food industry is intrinsically built up of a combination of technology, machinery, people and ingredients. All these are variable. Effectively running a food manufacturing company means coping with change to a great extent. Therefore, being predictive and getting ahead of change is crucial, as is preparation to manage the risks, challenges and seize the opportunities.
The way we see things at Polestar is… how can we use the ‘Internet of Things’ or in other words, connecting devices and sensors to the Internet to enable a strong, resilient and productive environment?
By implementing an IIoT project, you can monitor more accurately the environmental conditions for the sourcing of food products. Being able to predict, you have the ability to create metrics which can then go to the manufacturer to enable them to optimise, streamline and calibrate their processes.
This type of implementation brings competitive advantages in your product market. First movers expect to gain significant benefits from their more advanced digital capabilities and greater levels of investment. They are far more likely to be forecasting both revenue gains of more than 30% and cost reduction of more than 30% at the same time. They’re more likely to expect efficiency gains too (Industry 4.0: Global Digital Operations Study 2018. PWC, 2018)
What is more, according to a survey by PwC in 2020 across Operation Managers in different industries, more than half of respondents expect their Industry 4.0 investments to yield a return within two years or less, given investment of around 5% p.a. of their annual revenue.
The Challenges of Implementation for Food Manufacturers
Industry 4.0 initiatives come with several challenges. These are obstacles related with management, leadership and politics, IT/OT convergence, investment rejection, and the ability to get data the right from the machines.
First. Within our experience in the Food Industry, we’ve seen as a great challenge addressing variability in production: sites can vary enormously between the very small with people making products manually, to the very large with process areas across several buildings on multi-acre sites. Additionally, implementation of new products, new machines and new lines can take time and requires a lot of planning. So, it’s important when undertaking smart factory projects to prepare the business with the right leadership, policies and set of skills beforehand.
Second. Historically, the information technology (IT) and operational technology (OT) departments within manufacturing companies have functioned mostly independently. Operations kept the plant running smoothly, and IT managed business applications from the front office. Nevertheless, for implementing an Industry 4.0 project, is important that both teams communicate and align with each other. The operational data that OT teams use to support real-time decision making can create additional value for the company. But they also need the support of their IT colleagues to make the data meaningful and accessible for use across the organisation. Their IT colleagues can also help them better align with business systems, such as enterprise resource planning (ERP) tools and manufacturing execution systems (MES).
Third. Getting data from machines can be difficult, then constructing the (right) KPIs and finally transform them into actionable information. Industry 4.0 technologies transform data and transport it to places where people can use it, understand it and make management decisions.
Today, #Brexit, #COVID19, and the need to be more agile and innovative to position products in tougher markets has made Industry 4.0 less of a trend and more of a movement. But the truth is, most food manufacturers are still back in the 1990s with Industry 3.0 technologies and if you can’t measure, you can’t optimise. The challenge is how to get the right type of networks, sensors and integration of control systems that will allow the capturing of relevant data that helps the business to achieve a better ROI.
An IIoT Strategy to Achieve Peak Plant Performance
We have developed a set of strategies with five technology adoption stages that help any manufacturing company to realistically become a Smart Factory and start reaching peak plant performance.
Assessing: Evaluation of where you are at now in terms of digital maturity and set clear targets for the next five years (assessing your IIoT Readiness and Strategy). Then, looking at the governance and management structures you have in place that assist and approve the budgets of projects, as well as the infrastructure and skills needed to implement the right Industry 4.0 technologies.
Understanding the impact: How the technology helps food and drink manufacturers decide on the best intervention. We help make sure that technology adoption is the right one and level for each type of business (wheel/star stages).
Defining IT/OT Convergence Policies: This to enable digital manufacturing transformation, including: enabling real-time decision making through edge computing, eliminating unplanned downtime through predictive maintenance, deploying wireless technology on the factory floor and ensuring cybersecurity for a new world of connected machines.
Learning the value of Data: How to build direct links between data, decision-making and intelligent systems design. How to develop the right KPIs for starting to predict and control variability, and finally generate operational improvements and ROI.
Stages of Industry 4.0 Implementation
From the technology perspective, there are many levels of maturity on the way to connected factories, smart factories or industry 4.0 and these terms mean different things to different people but essentially its digital transformation for manufacturing companies, or more precisely:
Industrial IoT
Digital Twins
Cyber Physical Systems
Companies can take advantage of ALL 3, but you need to implement them successfully in the order shown (1-5) so you can realise the fully automated future. Many companies have been dipping their toes in the water with POC’s for data analytics and its not easy to introduce control systems later on in the process – so you need to think of Industry 4.0 almost as if it is a closed loop. We break these stages down into 5 steps for successful adoption.
Basic Computerisation, Connectivity & Environmental.
Secure Integration of OT & IT layers and all departments: Engineering > Production > Manufacturing.
Connected machines maybe with additional sensors for visibility, leading to a digital model of the factory to show what is happening at any given time, which will the allow upkeep of the digital twin model at all times.
Gathering the RIGHT data transparently and in quantities sufficient for meaningful analysis – the primary requirement for predictive maintenance capability.
Smart Simulation of different future scenarios and identification of the most likely ones, allowing for accurate decision making and implementation of app