We have seen how the production kitchen becomes more effective through being more efficient by applying automated and semi-automated technology. The question now is whether the foodservice industry will ever have the ability, or even the need, to move to a fully integrated and connected kitchen?
One of the main justifications for a Production Kitchen is volume, which in the case of an Industry 3 facility means more than just large batch capacity. Standard professional catering equipment already is capable of producing volume. 500 litre kettles and 40 tray combi ovens have all developed from the basic 50 litre kettle and 10 tray combi ovens seen in most professional kitchens: the controllers and software are the same and have allowed for the scaling up of capacity. But without the volume of the basic small capacity equipment required for a viable manufacture, even the relatively smaller number of high-capacity equipment would be available. This means that professional kitchen equipment innovations tend to start at the small capacity, high volume end of the market.
But few of these large capacity food machines and cooking equipment actually lend themselves to an automated application. Kettles certainly do through the application of pump systems connected to dispensing equipment, but most other equipment are batch based and require continuous labour to complete the task. When moving the production kitchen to an automated model, much of the plant, equipment and systems will come from the High Mix Production (eg: many SKUs) food processing industry. However even in the food processing industry the requirement for (and availability of) labour for portioning and handling is an issue that only now is starting to be addressed through the application of scaled down food processing equipment.
It becomes possible now for the production system to be equipped with automated equipment such as automatic Vertical form filling machines for bagging product; Chef Robotics AI-enabled flexible portioning equipment to optimise on conveyor food assembly operations and the completely automated filling of bowled items with a Prima Basic rotary filling and sealing machine. In all cases, equipment that takes up the equivalent space of a single person and processes units in the thousands per hour with minimal human intervention, rather than the space required for large scale production equipment.
Assuming that the production kitchen has now evolved to a smoothly running automated model for the majority of its throughput, is it time to deploy AI to ensure steady progress? How do you strike a balance between embracing the potential of Industry 4 and avoiding unrealistic expectations? Would integrating AI, IoT, Big Data and robotics to create a smart, integrated and self-optimising production kitchen be worth the investment?
Assuming that the scale and complexity of the operation might justify and benefit from such a significant step, it is essential to first determine the plant’s readiness to embark on the journey . The Plant Maturity Model can be used to develop a strategic framework to assess and evaluate the readiness of the existing operation. By applying the PPM to evaluate the production kitchen’s current maturity level, it is possible to identify gaps, prioritise areas for improvement and create a roadmap for the digital transformation.
To implement AI, particularly machine learning models, requires significant amounts of quality data to learn and perform effectively, and if that clean data is available, the application of AI can excel in the following areas:
Complexity: resolving multiple variables that might require pattern recognition
Repetitive tasks: automate tasks such as inspections, quality control or data analysis.
Predictive analytics: forecasting future outcomes based on historical data, demand forecasting; equipment failure; process optimisation.
Adaptability: AI models can learn and adapt to changes in variable or evolving conditions
Look before you leap!
If you consider that these are areas of the business that could have a measurable benefit on the production, it is essential that a cautious approach is taken:
Clear objectives: Be absolutely clear about the specific problems that need addressing and set measurable goals.
Data availability: AI, and particularly Machine Learning relies on large quantity of quality representative data to learn from. Investing in clean, accurate and well-structured data acquisition is essential for a successful AI implementation.
Integration with existing systems: AI technology has to integrate seamlessly with all the current manufacturing hardware, software and communications systems that are being used.
Change management: Implementing AI can cause significant changes in the way the existing production process works. The ability to deliver effective Change Management is essential.
Skills and expertise: A multidisciplinary team with expertise in AI, data science, manufacturing processes and OT and IT infrastructure will be needed and any gaps in your existing team capability will need to be filled.
Partners: Because AI is not the core business of the production kitchen management and operations teams, it is essential to involve experience partner(s) with complimentary skills and a proven track record to maximise the value of the AI transformation.
Decision and conclusion
It is now essential to realistically evaluate the benefits of moving from an existing Industry 3.0, Connected Plant, production kitchen model that is already operating efficiently with a high level of automation, integration and system standardization.
It may be that the process of analysing the production kitchen Plant Maturity model is sufficient to deliver incremental benefits to the existing operation without taking the step to an Industry 4.0 integrated plant network to achieve real time predictive analysis and a self-optimizing production process. It is also highly probable that the quality and quantity of data generated by even the Industry 4.0 existing production equipment and systems used in the kitchen operation will be far less than is required for a successful AI transition.
Realistically there will be few catering production kitchens or operations with sufficient scale to currently justify or benefit from an Industry 4.0 model when few have even developed to a connected plant Industry 3.0 model. But being aware of the possibilities when making decisions on the immediate future of an existing operation that can deliver small wins over time applying the incremental advances in digital technologies without the risk of a major disruption.
Tim Smallwood FFCSI