Buried treasure: Advanced analytics in process industries
The full power of advanced analytics
requires not only acquiring new technology and analytics solutions, but also
helping people improve their expertise and adopt new ways of working.
The Internet of Things (IoT), industry 4.0, advanced analytics, digital
technologies, and big data have all generated enormous hype for their potential
to transform every facet of business. To date, the dialogue around these tools
has focused primarily on consumer-facing industries, such as retail and
financial services; on large industrial companies, such as GE; and on leading
digital players, including Amazon, Google, and Netflix. Beyond the spotlight,
however, manufacturers in process industries—also commonly referred to as heavy
or capital-intensive manufacturing—have been early to adopt many of the latest
advances. For decades the sector has not only generated large amounts of data
but also combined science, engineering knowledge, classical statistics, and
powerful modeling into advanced-process-control (APC) systems that run key
assets efficiently.
Since APC systems are expensive to develop
and historically have been effective only for well-understood, less-complex
processes, APC usage has generally been limited to the largest, most critical
ones. Smaller, as well as secondary and more-complex processes have been left
without suitable process controls (see sidebar, “The processes of heavy
manufacturing”). And while process industries generate huge volumes of data,
their process-management and information-technology capabilities are not as
advanced as those of other industries. As a result, thus far manufacturers have
lagged behind in systematically deploying data analytics to extract the substantial
value hidden in the insights they contain.
The good news is that over the past
several decades, entirely new and more affordable manufacturing analytics
methods and solutions have emerged, and they are now reaching market maturity
as part of Industry 4.0. These solutions—which provide easier access to data
from multiple data sources, along with advanced modelling algorithms and
easy-to-use visualization approaches—could finally give manufacturers new ways
to control and optimize all processes throughout their entire operations.
But new technology is just one part of the
equation. To achieve a strong financial impact from improvements in analytics,
manufacturers must also consider the human factor. As in previous efforts to
optimize production, such as lean manufacturing or ISO quality standards,
change-management capabilities will be crucial. The new horizon in analytics
will achieve its full impact only when manufacturers enhance skills across the
entire organizations so that the new methods and solutions become a part of the
day-to-day routine.
Analytics can improve performance
tremendously while reducing costs: the value, spread over thousands of
opportunities, can be worth tens or even hundreds of millions of euros across a
company’s site network. The key challenge is deploying analytics hundreds or
thousands of times to achieve the desired outcomes. Manufacturers must
therefore embark on an analytics-transformation effort that reaches all the way
from the shop-floor operators (who steer processes) to process engineers (who
use deep insights that will drive the next wave of improvements) to managers
(who constantly oversee performance).
This course requires manufacturers to
develop the management capabilities and mind-sets that can mobilize the entire
organization to harness the new analytics technology. Heavy manufacturers that
apply this approach consistently—process by process, plant by plant, and
location by location around the world—will capture the full value of the new
optimization opportunities and more value from their physical assets and
people.
The
evolution of automation technology in heavy manufacturing
In heavy industry, current process-control
systems can run, say, entire chemical plants from a control room in fully
automated mode, with operations visualized on computer screens. Data steer
processes through proportional-integral-derivative (PID) algorithms that manage
local loops.
Certain industries, such as oil and gas
refining, have taken the process-control logic a step further by using APC
systems to run continuous-optimization models. Indeed, for the past decade, the
heavy-manufacturing sector has been increasing its profits per hour by
successfully applying APC technologies in many different processes. These
initial models, which require a static, well-described (or “deterministic”)
problem, are extremely powerful. Their main drawback, and the reason they have
been implemented only in specific settings, is the expense: APC systems can
cost €0.5 million for a distillation column or €1.5 million for a hot
strip-mill furnace. Consequently, the return on investment (ROI) typically
makes sense only in big processes.
Next-generation
analytics, optimization infrastructure, and tools
But heavy industry is now on the brink of
the digital revolution. IoT technology—which includes affordable sensors,
reliable data transmission, and powerful control software—can generate and
track information from every machine and step in a manufacturing process.
Similarly, affordable storage options (both high-performance databases and
cloud offerings) have made it possible to aggregate and manage vast amounts of
data. Last, modular, easy-to-use analytical packages, especially those that
also include artificial intelligence and machine learning, can harness and process
this information to provide new levels of insight. In addition, many algorithms
and software tools are being commoditized, making them easier for employees to
use without significant training.
Conventional APC systems have monitored
and managed large, deterministic processes. Thanks to the aforementioned
technological advances, manufacturers can now deploy new, low-cost control and
optimization solutions across midsize deterministic processes and
midsize-to-large nondeterministic processes, which until now haven’t benefited
from this kind of sophisticated technology .
Sizing
the opportunity
An evaluation of the total value of
potential performance improvements illuminates the enormous opportunity for
manufacturers that get it right. As an example, a large multinational company
typically has a couple of dozen big processes (such as a catalyst-inhibitor
addition in a chemical process) that if optimized could cut costs or generate
productivity improvements of €1 million each. However, manufacturers also have
hundreds to thousands of midsize opportunities, such as steam-superheat
controls (the benefits range up to €50,000 per boiler a year) or undersized
valve-restricting cooling (up to €70,000 a year). A chemical company with 150
sites, for example, and 10 to 100 processes to optimize could have 1,500 to
15,000 opportunities whose recurring annual cost savings could range from €50
million to €500 million.
By using analytics systematically,
manufacturers unlock savings from three sources, each delivering an equal
portion of the value. First, analytics gives process engineers greater
visibility into every process, which they can therefore steer more effectively.
Second, it supports better management—for example, by helping operators to spot
when a process deviates from defined key performance indicators (KPIs). This
visibility allows them to detect movements and take corrective action much more
quickly. Third, it uncovers hidden process restrictions that can often be
resolved with small capex investments. Besides making employees more efficient,
it can help to remove critical human bottlenecks, freeing up time and resources
that can be directed to additional improvements in plants.
Foundational
elements of analytics-driven process optimization
Manufacturers face a key challenge in
analytics: deploying it hundreds of times while assuring consistency, quality,
and the development of a continuous-improvement cycle that supports further
advances. External parties can assist in setting up the framework, delivering
the first successful pilot implementation, designing the organizational
processes, and building up the capabilities of early users. Eventually,
however, manufacturers will have to implement sustainable practices and install
experts across the global organization, much as they do for quality assurance,
energy management, hazards and safety, compliance, and other functional
practices.
Four expertise areas are crucial in
setting up the right organizational support so that the company can not only
aggregate and analyze data, but also act on the findings.
IT expertise. This category
includes employees with the knowledge and skills to aggregate data from sensors
(including their location, type, and accuracy) and to store this information in
various platforms (the cloud, on-site servers, off-site servers, the external
cloud). IT also deploys and maintains the new systems, sensors, and software
solutions.
Domain expertise. Data analytics, by
itself, can’t deliver the full range of impact and value without an
understanding of how processes behave and how local plants are set up. Frequent
interactions with local experts are essential to construct a useful
optimization model. Process engineers can collaborate with data-analytics
specialists to base models of outcomes (such as yield and energy use) on
defined inputs and can use expert knowledge to solve problems throughout the
process. Moreover, they can update value-driver trees and develop a solution
database for future reference.
Advanced analytics. Data must also be
structured by time, batch, combination of time, and batch and time delay, as
well as cleaned to identify and visualize outliers and missing data. Analytics
tools, such as value-driver trees, can help determine the critical parameters
and cluster data by tag information—and they are flexible to reprogram.
Employees—in particular, process engineers and operators—must be trained in the
new technology and be able to work with it day by day and to act correctly on
its recommendations.
Change management. Transformation
professionals define improvement measures, build optimizers for operators, and
track performance and KPIs. Implementation efforts use dashboards and apps to
visualize performance in real time, and automation embeds improvement measures
in APC software. More often than not, surprising additional findings will
emerge to challenge existing knowledge and dispositions. More real-time
information ultimately changes how decisions are made and leads to an agile way
of operating.
Methodically progressing through the
organization—process by process, opportunity by opportunity—will ensure both
consistency and maximum impact.
Driving
an analytics transformation across a global organization
A successful analytics transformation
requires manufacturers to refine a single process and then develop the
capabilities to replicate that approach to optimization hundreds or thousands
of times across the enterprise. In our experience, manufacturers typically
already have the domain and IT expertise to capture and store massive amounts
of data. To ignite and sustain an analytics transformation, executives should
focus on strengthening organizational capabilities in advanced analytics and
change management.
Piloting
the analytics approach
An analytics transformation starts by
identifying specific processes to serve as pilots. Ideally, these initial
projects can demonstrate the viability and value of analytics and realize
improvements quickly.
The next step is to implement a learning
and improvement process. A good part of optimization involves using analytics
tools to gain a better understanding of the drivers of greater efficiency and
cost savings. Normally, analytics highlights opportunities for small capex
investments to improve the entire system’s performance. Lessons from these
initial efforts can be integrated into a longer-term one to develop a more
permanent solution.
Capturing value is first and foremost
about people—from selecting the right process for collecting data to carrying
out the analytics to implementing the resulting insights on the shop floor.
Capturing that value is also about infusing analytics throughout the
organization and building the capabilities to set up more effective processes.
Manufacturers may be tempted to leapfrog the immediate, incremental approach in
an effort to seize the larger prize more quickly. Others can get stuck at an
early stage by taking too long to design and implement a customized technology
solution. In our experience, it does not make sense to spend two years and
several million euros on the optimal approach if inexpensive data lakes and
analytics can help organizations to start delivering value within the first few
months.
Heavy manufacturers and their staffs must
also embrace a new mind-set—one centered on data-driven decision making and on
the processes to gather, analyze, and apply these insights to draw the right
conclusions.
Rolling
out analytics to the enterprise
Almost all the classical
transformation-success factors are required for an analytics transformation:
setting clear aspirations, getting focus and buy-in from top management,
engaging employees through clear and well-executed communication initiatives,
identifying skill gaps, planning to build the required capabilities, defining
pivotal roles, and committing sufficient resources.
Given the large size of the individual
opportunities and the complexity of interacting with several departments and
business units, setting up a new program-management office is critical to a
successful analytics transformation. Once the pilot has been completed, the
change-management team should identify opportunities, prioritize locations,
formulate the communication plan, set up the required IT support, and select
and train experts on the job in successive waves. The preparatory phase
includes training in software tools. Experienced engineers should be ready to
teach and help other employees and to update training materials by drawing on
lessons from early waves of training.
This approach uses a learn-and-transform
model: groups of engineers follow a classic transformation journey (prepare,
diagnose, design, plan, implement, and sustain) that’s broken down into three-
to four-week sprints. Each sprint includes joint learning, on-site application,
and feedback sessions with trainers to improve retention. In our experience, a
small group of experts can administer training to 200 engineers within 18
months, achieve an impact within four to five months, and develop the rollout
plan for a site in parallel. In this way, an organization can develop enough
process engineers to cover 150 sites in just 18 months. It’s critical, in the
journey, not only to capture value from specific opportunities but also to have
engineers develop a road map of improvements for the next few years.
This phased approach enables manufacturers to lay the
groundwork to address thousands of opportunities across the organization and to
build the internal capabilities for an analytics-driven transformation.
Although such deep changes require a sustained commitment and a significant
investment, the returns could be substantial. Further, the visibility into
operations that analytics makes possible pays long-term dividends:
manufacturers that integrate this approach into every facet of their operations
will be more agile and better positioned to capitalize on the insights it
generates.
By Robert Feldmann, Markus Hammer, Ken Somers, and Joris Van Niel
http://www.mckinsey.com/business-functions/operations/our-insights/buried-treasure-advanced-analytics-in-process-industries?cid=analytics-alt-mip-mck-oth-1612
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