Opinion: Now is the time to digitally transform F&B manufacturing
IIoT and hybrid/cloud deployment models are accelerating change
In April, I presented a case for why digital transformation in the Food & Beverage industry has now come of age. Food Engineering hosted their Food Automation & Manufacturing conference in Bonita Springs, Florida. Industry leaders gathered to explore the latest trends and technologies in manufacturing, automation, sustainability and food safety. Leading brands like Maple Leaf, PepsiCo and Land O’ Frost shared strategies on driving innovation and sustainability through digital transformation strategies.
A key takeaway for me was seeing firsthand how these manufacturers are now aggressively implementing digital transformation across their operations—with great speed and urgency. Specifically, these organizations are embracing the Industrial Internet of Things (IIoT) and hybrid cloud deployments to achieve aggressive transformation targets.
There are many factors driving adoption of digital technologies. Some are based on influences in the market environment. Others are driven by margin pressures, capital expenditure constraints and the need to engage and respond to consumers. Regulatory obligations, recipe management and track and trace initiatives are also creating urgency.
For most customers, digital transformation starts as a response to the economic disruptions in the market being wrought by technology in the lives of their customers. Since the year 2000 more than 50 percent of the Fortune 500 companies have been acquired, merged or bankrupted by these changes, driving home the fact that businesses need to transform to remain competitive and thrive.
The newly minted “Chief Digital Officer” now has responsibility that covers the entire business, from sales and marketing to operations, but it is operations where value creation happens and where the foundation of a digital transformation must be built.
I see five “imperatives,” or areas that a business’ digital strategy will seek to address:
- Empowering employees
- Optimizing operations
- Engaging customers
- Transforming offerings
- Adopting a new business model to meet the needs of the customer
All with the goals to manage costs or to grow revenue.
As an example, optimizing operations is typically a cost management tactic used to bring the cost of the business down. There are a number of value propositions that can be delivered from the implementation of digital transformation initiative—such as a mobility program. This type of project is all about empowering operators on the plant floor to be more efficient, effective and accurate. By getting them out of the control room and “working while walking around,” operations data is more accessible, more actionable and delivers more results—where and when operators need it.
Here is where it gets interesting. The digital transformation initiative that began with a narrow focus on improving productivity has now morphed into a process improvement program. With operators now free to not just be focused on data collection, they can see opportunities for improvement—and take actions on those ideas, helping reap significant benefits for their company.
Traceability is another high value proposition for customers. As products are made, IIoT solutions capture relationships between raw ingredients and finished goods, which is very important in the case of recall management and the ability to accurately track where things went wrong. Traceability helps ensure consistent deliveries, faster time to market, safety and compliance, driving customer revenue and engagement.
The Industrial Internet of Things is typically linked to digital transformation as new business models and technology enabled value propositions emerge. In the past, data from production assets were typically collected to allow control and reporting of production. With new technologies like predictive analytics, the need for big data quickly surpasses what the control network was design for. This sequence of actions creates the need for low cost, but robust collection of data directly from the plant floor. Predictive analytics is just one of many use cases for IIoT, which I would group collectively in the following three applications:
- Extend existing systems with shadow sensing: IIoT has the ability to extend the lifespan of existing operational systems by allowing new capabilities to be added without a rip and replace of those systems. Performance management KPIs are a typical case. Take an existing production line, add low cost wireless sensors and send the data off to get performance metrics on how well that line is running. This can be done without any intrusive changes to the existing equipment and controls for that line.
- Enable semi-automated and manual plants: In developing economies, plants lack automation because of low labor costs, and have little in-house expertise in maintain analytics and operations management software. As businesses need to respond globally within the digital world, this is not a sustainable. IIoT can simply and accurately collect data with little need for in plant infrastructure such a servers or plant wide networks.
- Connect distributed systems: IIoT is all about collecting and connecting data and as such isn’t limited to the plant and its hardwired control networks, and can span across an entire value chain, i.e. farm to fork. As an example, in the India dairy industry, about 20% of milk waste happens in reception when milk has uncertain provenance or quality. IIoT can solve that problem by instrumenting the many small milk collection facilities across a region at low cost to verify that milk collected meets quality and safety standards and hasn’t been tampered with.
Why the cloud and why hybrid?
The Cloud is a closely related technology to IIoT, and provides businesses with a cost effective platform to collect, filter and contextualize raw data collected from the IIoT and other systems. While a pure cloud architecture is good for supporting complex analytics, as our “Things” running in the field on the edge get smarter, there is a growing need to share tasks between the edge and cloud. This explains why the hybrid cloud is where the industry is headed.
More and more plants are splitting their software systems to be both “on premise” and on the cloud, as a hybrid deployment architecture. While there are many benefits to storing information in the cloud, not every aspect of the plant can be supported there. Mission critical software needs to be on premise to ensure availability and responsiveness. Processor hungry analytics, however, are a great candidate for the cloud. The hybrid model allows for a split set of shared capabilities.
A great example of a hybrid cloud approach are traditional HMI/SCADA systems. In general, much of this technology needs to be at the edge. However, as we move to a hybrid architecture, non- mission critical applications such as long-term data storage, reporting, machine learning and advanced analytics can be safely moved to cloud, eliminating the cost of supporting those capabilities on the site. Taking it a step further, the configuration management of the “Edge” can also be moved to the cloud simplifying hookup and management of the in-plant components.
Amidst increasing competitive and market pressure, food and beverage companies had traditionally looked to acquisitions to drive growth. Today, megadeals are falling out of favor. Smaller acquisitions are increasing. This activity is putting new pressure on operational agility and performance excellence. Those organizations that can adapt faster will be tomorrow’s winners.
A successful digital transformation program can be the difference between survival and failure. More and more companies within the industry now realize that IIoT and cloud technologies are more than just buzzwords. Herein lies the roadmap that is unlocking a successful digital transformation of operations, to make the future a whole lot more attractive.
Keith Chambers is Director, Operations Management Software, AVEVA (formerly Schneider Electric Software).