One of the most widely consumed foods in the world is bread. It has been a key sustenance for thousands of years. Although initially made in unleavened form, the modern form of bread was an accidental discovery; the first leavened version was an outcome of some passing yeast making a home in a bowl of dough.
Since that discovery, both bread and technology have come a long way and continue to morph, but it’s only now where technology can really start to unravel the mysteries of the interactions between chemistry, biology and physics as the food product makes its way to our homes.
Food producers are striving for increased productivity, efficiency and agility as the consumer demands for affordable and ever-increasing novel foods and packaging formats. But the achilles heel for many companies is coping with the interaction between the sciences causing unpredictable variability across the supply chain from Farm to Fork. Large volumes of food and drink production carried out in factories face challenges including the variability of the ingredients, the changing environmental conditions and the manufacturing processes. However, technology is increasingly providing solutions which at one point in time seemed insurmountable.
Predicting the impact of changes in crop yield
One of the unpredictable variables that affect the food industry is the raw natural ingredient like the crop which may have been affected by drought, floods or other furies of nature.
Also changes in raw material supplier can impact the final product and if these changes aren’t tracked or predicted, they otherwise go undetected. This traceability is important for the purpose of knowing how variations of crop could react differently in production. It allows manufacturers to factor these changes but increasingly to have detailed information on provenance of product and its ingredients builds consumer confidence.
Many new technologies come to the rescue. They help to get to an ideal point where variability can be tracked, predicted and then adapted to production. For example, with Siemens MindSphere, a cloud based open IoT operating system, farmers can collect data over a period of time and, using artificial intelligence (AI), process and analyse this data to find solutions to enhance the crop yield. The analysis allows farmers to use measures to manage several factors, such as soil management and ‘run-off’, which refers to rainfall that does not soak into the ground or flows into a drain or watercourse.
The most advanced technologies are seen in a factory which also extend to the value chain. Having traceability and visibility throughout the life cycle has an impact on food production. From design to fulfilment all processes can be digitalised and automated to suit the needs of the product lifecycle. Having a digital thread throughout the process allows manufacturers to predict the variability and adapt production accordingly.
Siemens’ technologies are designed to help optimise production using total integrated automation in the food production industry. Not many factories are automated to the levels required for optimum performance; and even if they are, it is only partial automation. The common denominator is a lack of connectivity across the production line which inhibits real time data flow and accurate recording and visualisation of efficiency. Understanding the performance of business is the first phase of the continuous improvement process and vision of the factory. This lack of connected factory vision does not trigger the maximum returns on investment (ROI). The ideal approach is to take a bird’s eye view of the whole production unit and derive the connected factory vision. Point solutions around process automation or machinery upgrades would then follow the integration strategy. This upgrade process is an essential part of the strategic vision, but alongside the traditional means of CapEx there are other methods of deploying new automation without huge capital investment.
With digitalisation come different business models such as pay per use – based on outcomes and performance contracts. The breadth of Siemens technology lends itself to these different forms of business models and this can be used to accelerate the deployment of technology and business improvement vision allowing for investment funds to be focused across the business without holding back on production improvement initiatives.
A great example is TrakRap, a retail packaging solutions provider. They decided to develop machines for consumer-packaged goods sector, but their new business model was going to be on a pay-per-use basis. To run the business based on this new model they had to develop a machine that was highly reliable and self-adaptive to cope with the varying atmospherics conditions. The new machine was originally designed to wrap aerosol canisters. This was a collaborative project with several partners, including The Manufacturing Technology Centre (MTC) in Coventry. Siemens technology was used to virtually develop, test and commission the machine, using a ‘digital twin’; a fully functioning 3D computer model.
TrakRap gained manufacturing predictability, time to market has been reduced by 40% and development costs have been cut by 30% as the physical prototype stage was eliminated.
One of the key enablers in this field is the Siemens’ total integrated automation portal (TIA Portal), a software solution providing manufacturers with a range of digitalised automation services including digital planning, integrated engineering, and transparent operations.
Predicting the impact of changes in the environment
Going back to the humble bread, several factors such as weather, humidity and temperature variables determine the quality of the end product. Variability, i.e. not knowing what conditions are affecting it and what can be done to manage the conditions, is the curse of all food production.
With TIA Portal, these variables can be monitored and controlled. Different stages of bread production require specific temperatures. For instance, for the yeast to ferment and the dough to be ready for baking, warm conditions are needed. The baking and finished product has other climatic requirements that give it the desired longevity and freshness when it reaches the customer.
All food manufacturers are faced with stiff competition and the pressure to take a product faster to market is greater than ever before. TIA Portal’s simulation tools help boost the productivity of a plant through additional diagnostics and energy management functions. It offers broader flexibility by connecting to the management level.
By integrating Siemens product lifecycle management (PLM) and manufacturing execution systems (MES), it’s easier to achieve adaptive production. By being closely connected to the supply chain ecosystem, activity at the field level can be monitored and then real time data can feed the scheduling system so product lines aren’t under or over compensated.
Predicting product behaviour
A revolutionary player in this field is the digital twin. Food manufacturers have benefitted from the Siemens’ digital twin even before beginning production. The virtual model of a production plant allows manufacturers to predict behaviour, optimising performance and implement insights from experiences.
The digital twin is the precise virtual model of a product or a production plant. It displays their development throughout, tracks the entire lifecycle and allows operators to predict behaviour, optimising performance, and implement insights from previous design and production experiences.
Siemens’ comprehensive concept of the digital twin consists of three forms: the digital twin of the product, the digital twin of production, and the digital twin of the performance of both product and production. Thanks to the comprehensive domain expertise and optimised tools, Siemens is the only company that offers this holistic approach.
Siemens’ digital twin’s offers tangible benefits: it can substantially reduce the number of prototypes needed; predict the performance of the production unit and the products themselves; and ensure the product is what customers expect.
The digital twin can help visualise the product as well as production, giving manufacturers a complete approach. This technology also looks at ‘what if’ scenarios allowing maximum reduction in down time. There is a connection between the virtual and physical world, enabling informed decisions throughout the product lifecycle.
New product development and recipe scale up
Food manufacturers need predictable scale up from the kitchen when developing new recipes to de-risk the process. For instance, the viscosity of chocolate must be maintained to ensure a good bar at the end of the production line.
A chocolate manufacturer that we work with has a factory that has put the digital twin to good use. It has helped formulate its mix of chocolate, wafer, nuts, sugar, etc. in the design stage.
This type of technology is great for new go to market foods. Before it goes into production it can simulate various mixes of its proposed product or percentage of ingredients to know the result. This can optimise the quality, and manufacturers can look at the details of the packaging and how it will reach its customers from boxing, transportation to the time it reaches the point-of-sale.
Bread comes and is enjoyed in various forms and shapes. A leading German maker of pretzels uses the digital twin technology with amazing results. Its machines are now ten times faster than manual processes it can precisely manage the amount of butter injected into each pretzel.
Helping food manufacturers to control – and in the not too distant future track and predict – the many variables throughout the value chain to optimise and adapt production requires a combination of a holistic vision, technology and collaboration.
To hear the recent Table Talk Podcast focused on how tech can help you manage variability, click below