Greenfield -vs- Brownfield
The Industrial Internet of Things (IIoT) takes advantage of the latest software and hardware capabilities to increase productivity. It stands to reason that greenfield manufacturing plants might be planned with native IIoT technologies. After all, today’s factory automation systems can incorporate web servers and clients, standards-based communications and substantial processing power and memory.
But the majority of manufacturers face a mandate to increase the operational efficiency of their existing brownfield assets. In a greenfield, there is no need to integrate with legacy systems. However, in a brownfield, integration can be a major challenge with assets that may be decades old. The machine control systems typically are not networked and do not communicate with each other. Once IIoT sensors, networks and edge devices are overlaid and the existing assets and analytics applied—the legacy control systems may not have the capabilities to optimise operations.
Conversely, retrofitting the existing controls has not been widely practiced. Rewriting machine control code is an investment that carries a significant potential risk, and the machinery’s electrical and mechanical components may also not be capable of executing on the new control capability.
So, how can manufacturers go from brownfields to greener pastures? The answer lies in revisiting a 1970’s retro-concept—the focused factory, or factory within a factory. Essentially, one makes a smaller yet still greenfield IIoT investment inside their existing facility. You may call it a pilot, but in fact, it is a full-scale manufacturing system, line or cell(s), fully intended to continue in profitable production indefinitely. It may even be called a ‘green patch’ in the brownfield. With a well-defined scope, it becomes a manageable, measurable proof-of-concept project that pays for itself before scaling up.
Criteria for a green patch project
Greenfield means fresh equipment, tooling and state-of-the-art Operational Technologies (OT) and Information Technologies (IT) with the bandwidth, processing power and memory needed to apply advanced software and processes. This definition also applies to a green patch, only on a more limited scale. These capabilities support Predictive Maintenance, supply chain visibility, energy management, rapid changeovers, product lifecycle management and more. The following criteria for a green patch project should be considered:
A new product line or process that requires capital expenditure (capex) investment, providing a built-in cost justification
High value, complex products where IIoT can make a significant dollar impact on yield
An application that will benefit from flexibility—high variability/low volume, batch-of-one manufacturing, short lead times, anticipated product modifications and options
An operation where maximum uptime is essential, that will benefit from Predictive Maintenance
There should be manageable scope which means that the production line or cell can effectively be self-contained and operate independently from the rest of the factory, with components or materials sourced as if the factory is an external supplier
An operation that can be isolated with MES/ERP that may be self-contained and may be hosted on premise, both to eliminate IT security issues and develop a flattened IIoT network hierarchy, providing direct connection between machine-to-cloud, edge and machine-to-machine connection that might otherwise be too ambitious.
Thus, the ‘green patch’ concept takes advantage of the required capex for a new product line or process within a brownfield plant to implement IIoT functionality with state-of-the-art manufacturing automation.
Constraints of a green patch project
The constraints are the same as for any other upgrade in a brownfield environment. The green patch must conform to the physical footprint, shared resources and other constraints of the existing facility. These can include utilities, physical obstructions, such as, columns and roof height, air filtration and even the available labour pool and zoning restrictions. Each issue and solution will need to be evaluated on the basis of potential payback and can help define project scope.
For example, does a clean room need to be built to mitigate airborne contamination from adjacent processes, or is it better to construct a new building if the land is available? Or, can critical equipment be purchased in an isolator configuration, and if so, what impact will this have on the efficiency of ongoing operations, maintenance and replenishment?
Origin of the Focused Factory
The concept of the ‘focused factory’ was introduced by Dr Wickham Skinner in the mid-1970s while serving as a faculty at the Harvard Business School. It shares some traits with our green patch. The decade had brought forth a ‘productivity crisis’.
Dr Skinner contended that competitive agility was yet another factor to be considered. In the conventional manufacturing model of the time, factories produced myriad products for different customers, propelled by the notion of the ‘economy of scale’. Dr Skinner proposed that factories that strategically concentrated their efforts on specific product lines and particular markets would have increased productivity and profitability. His counsel included establishing a succinct statement of corporate objectives and strategy that would be directly implemented in manufacturing processes. By analysing each component of the production system, organisations could ensure that all elements and processes were compatible. As in green patches, profitable production would be driven by a manageable scope of targeted innovation.
Factory within a factory
The ‘factory within a factory’ model is a way to address issues of supply chain management in a timely, cost-effective manner while preserving responsiveness to customer needs and requests. Instead of relying upon an independent manufacturer to provide a part or materials when ordered, the subordinate operation establishes a physical location within the primary manufacturing facility. There are multiple considerations that impact the feasibility of such an arrangement. As a positive, it streamlines process for procurement when time and distance is of the essence. Other complexities involving financial investment, term length, logistics, labour, protection of intellectual property and tax implications also present themselves for investigation. To truly leverage the potential benefits, all aspects must be implemented effectively.
Compared with traditional manufacturing environments, a green patch environment will introduce integrated devices, applications and tools which generate volumes of actionable data. Therefore, a green patch can unlock opportunities to explore and develop a specific strategy for integrated machine learning, predictive insights and applying analytics to show the impact of IoT optimisation to drive revenue, cut costs and innovate your operations.
Getting started with analytics
Analytics are a fundamental element of IIoT. Determining the data to acquire for analysis is a good starting point for a green patch project. A 2015 technical report from McKinsey on analytics in semiconductor manufacturing offers broad advice, with the key piece of advice to make data actionable.
In order for data to be actionable, it must be robust with all relevant details collected in a structured, targeted and consistent format. Another vital aspect is the maintenance of data integrity. Ramifications of substituting lost data with averaged content include missing patterns and generating false positives. Data must be stored effectively with an eye towards easy retrieval. Actionable data is the foundation of advanced analytics. When supported at the enterprise level as a strategic priority and not merely a managerial tactical responsibility, advanced analytics drives predictive insights into where to invest, ways to optimise productivity, keys to reduce time-to-market and other essential metrics.
In a 2014 Forbes article on data analytics in manufacturing, the author cites specific ways in which Big Data is transforming industrial processes. Highlights of the article include a discussion on the refinement of forecasting across multiple channels, the unification of daily production activity to financial metrics and the provision of preventive maintenance recommendations via sensors.
Success measurement: Overall Equipment Effectiveness
Like any capital project, the green patch needs success metrics. A significant aspect in manufacturing, in this context, is the lean Six Sigma key performance indicator of Overall Equipment Effectiveness (OEE). For many years, manufacturing enterprises have implemented different varieties of OEE measurements to different degrees of success. For example, some OEE proponents consider all downtime, including scheduled maintenance and holidays, while others exclude all but unplanned downtime. The green patch can be used to redefine a consistent OEE metric moving forward, for how and what to measure, analyse and optimise in the green patch, and therefore, serve as a framework for OEE future process improvements without impacting existing operations. Thus, OEE can do things like optimising instead of maximising line speed to avoid bottlenecking at the slowest machine in the line.
The contrast between IIoT will be most dramatic where data is still manually gathered, demonstrating the value of automated data acquisition, more frequent sampling and more consistent data through the use of new standards, such as, ISA TR88.00.02 (PackML) and MTConnect, both OPC UA enabled.
IndustryWeek’s article, ‘OEE - Learn How to Use it Right’ offers salient points pertaining to our green patch production line, including the fact that OEE is well suited to measuring the relative productivity of a single production line or system. So, it is focused, which we want. The OEE data has many practical uses. One is to determine the optimum (as opposed to fastest) production rate for a line. Running a line at its fastest can cause the bottleneck machine (there is usually one machine that limits overall line speed) to result in stoppages and actually reduce throughput compared to operating at a lower speed.
Much has been discussed about Big Data and analytics. To make use of analytics, it is necessary to close the loop and provide a mechanism to apply the data to process optimisation. Total Productive Maintenance (TPM) is one such tool that can take OEE data and improve the operation of machines and lines. TPM is a methodology for acting on and resolving issues identified by OEE. Similar to OEE, TPM is a topic familiar to manufacturing professionals and upon which IIoT can improve. It is used here as an example of ‘what to do with analytics once you have the data and you’ve analysed it’. Without IIoT’s continuous, automated monitoring and feedback, TPM might be used to set limits on line speed, based on the bottleneck.
In the IIoT-enabled green patch, process optimisation can take into consideration many more factors because more conditions can be monitored more effectively. In manufacturing and consumer packaging operations, these can include:
Variations in material lots, material deformation and recycled content
Impact of environmental factors, such as, ambient temperature, humidity and contaminants
Process factors, such as, heat build-up, ramp-up/down to/from optimum line speed, mechanical wear, inconsistencies in electrical and compressed air supply
Anomalies that can be traced back to human factors, such as, operation, lubrication, cleaning and improper use of e-stops
Logistical issues, such as, parts replenishment, coding system ink level, tooling and change part availability
A readily accessible approach
The more time you spend in a brownfield, the better a green patch looks. By focusing on a single new line or cell, it can be much easier to get started with an initial deployment, learning how to apply IIoT in a controlled environment and scale, with new technologies designed for the task. The green patch is intended to propose a readily accessible approach for mid-market manufacturing enterprises to obtain the advantages of IIoT without becoming overwhelmed by a large-scale rollout.
This white paper was originally published by the Industrial Internet Consortium in 2017. The original white paper is available at here