Pushing manufacturing productivity to the max
Advanced
analytics and lower-cost computing give companies a powerful tool for managing
profitability on an hourly basis.
Many companies do their best to optimize production processes using
established rules of thumb or incomplete data. But at the end of the month or
reporting period, they often discover sizeable gaps between actual profits and
what they had expected. In our experience, that is because they typically lack
precise-enough measures to understand the small, real-time variations in
process flows and manufacturing steps that cumulatively erode returns at
facilities such as mines, steel mills, or other manufacturing plants. This
information, moreover, is rarely shared quickly enough for managers to respond
in the tight time frames required.
Our work across a number of industries
suggests that companies can eliminate these profit-draining variations, as well
as speed up reaction times by using advanced data analytics combined with
upward cascades of data to manage performance. A metric we have termed profit
per hour—which in an earlier article we described as a way to improve resource productivity—provides a much more exact view of fluctuations in the
operating environment and a much better means of communicating the implications
to top managers.
Extending
the measurement frontier
By combining measures of value, cost, and
volume over time, profit per hour is more potent than the sort of
metrics commonly used in many industries. Using data captured from sensors,
along with advanced-analytics tools, industrial companies can deploy
self-learning models that simulate the expected value and cost of individual
processes and even entire factories on a continuous basis. From this analysis,
patterns emerge on where costs, heat levels, recovery levels, and other
variables are deviating from predicted values. Operators can then fine-tune
process procedures or adjust inputs so as to eliminate losses as much as
possible during those periods in the day when profitability falls below optimum
levels. The insights create a new information backbone, linking real-time
performance at ground level to company profitability and allowing managers time
to make the necessary trade-offs.
Until recently,
companies lacked the usable data, advanced sensors, and processing capabilities
to gauge the performance of operations with real-time precision. But increases
in lower-cost sensors, wireless connectivity, cloud data storage, and computing
power have changed the equation, as has the development of smarter analytics
tools that analyze continuous process flows and complement
advanced-process-control systems such as those found in refining, the
petrochemical industry, or major production steps of the steel industry.
Moreover, as more efficient and effective analytics emerge, there is greater scope to widen
profit-per-hour analysis beyond just a few of the most critical processes.
Meanwhile, further reduction in the cost of storing vast quantities of data
allows finer-tuned performance management to reach across entire plants and
even across companies.
Reaping
the benefits
Two examples demonstrate how profit per
hour can result in significant performance gains.
Process-level improvements at a chemical
plant.
The manufacturer had previously invested
substantially in automation and advanced process control to increase throughput
of a product line. Managers, however, knew that the external weather was
affecting the efficiency of the process and the performance of the plant: the
problem was they didn’t know to what extent. Technicians therefore identified a
list of ambient and internal conditions that tended to vary in summer, such as
wind direction, relative humidity, and temperature, among others. Armed with
the necessary data, they built an advanced neural analytics model that was able
to simulate profit per hour for the line under ideal, seasonally adjusted
conditions—enabling management to note disturbances and take remedial action.
The model further allowed the team to identify precisely the lost output and
margin effect resulting from variations in each factor, including and in
addition to the weather parameters. The team then focused on the top five that
could be controlled by process adjustments or targeted investments. The company
ultimately discovered that upgrading one piece of equipment could yield nearly
€500,000 in value annually, in an investment that paid for itself within 12
months. The model also indicated how speedy reaction to operating deviations
boosted profit per hour, a message communicated in additional training sessions
for the frontline operators charged with monitoring dashboards and adjusting
processes in real time. The newly defined parameters and rules were thereafter
included in the process-control systems with the goal of increasing profits per
hour by up to 2 percent.
Facility-wide gains at a steel mill.
A steelmaker’s most important site seemed
to be operating in the dark. Capital upgrades only intermittently resulted in
higher returns. Operating decisions were often based on historical wisdom and
personal experience, with little in the way of facts to demonstrate their potential
financial impact. Meanwhile, data gathering was substandard, and manufacturing
units within the plant often used different top-level key performance
indicators (KPIs), preventing an integrated view of performance across the
whole plant.
Senior management decided to remedy the
situation with a radically different, multistep approach. At the core was a new
KPI, which cascaded to the entire executive suite and linked operations
performance to a single plantwide daily profit standard, grounded in profit-per-hour
analytics. The goal was to give plant-level managers and frontline operators
greater visibility into production variability, as well as to offer financial
executives a surer sense of the facility’s performance. During the first phase,
the mix of operational metrics was aligned with the new profit measure. In
phase two, technicians tested the metric for insights into operating
performance across the site’s hot rolling mills, steel-making plant, ironmaking
plant, sintering plant, auxiliary power generators, and other units. In a third
phase that involved new investments in IT, the company installed dashboard
monitors that displayed the metrics both on the plant floor and in
senior-management offices. A centralized data-storage system and standardized data
analytics form the IT backbone.
The unified metric has allowed full
tracking of costs. With additional training of frontline employees and managers
alike, it has driven kaizen-like
problem solving on a real-time basis. Variations in efficiency, previously
likely to continue for days, are now eliminated within hours on average thanks
to new ways of working across the facility. Costs have fallen by 8 percent in
the two years since the new profit standard was adopted, and, coupled with
other improvement initiatives, it has resulted in close to an $80 million
cumulative increase in earnings. Additional gains are expected as better data
analytics open pathways to new process improvements and work flows.
Exploring
new horizons
With rapid adoption of process sensors and
greater capture of data, artificial intelligence (AI) is likely to figure
prominently in the next wave of gains. Analytics models will “learn” from
process variations and make adjustments automatically. Google’s DeepMind AI is
already doing this to reduce energy used for cooling its data centers by up to 40
percent. Models learn from historical data such as temperature, power
consumption, and the functioning of cooling systems. They use that information
to understand variations in data-center operating conditions and “judge” how
best to run cooling systems with minimum power use. In future AI systems such
as these, profit per hour could become the benchmark for optimizing operations.
While still in its early days, we’re
seeing instances where profit per hour can be applied across multiple company
manufacturing sites and even more broadly to supply-chain networks and
decisions about how to serve customers. A more accurate, real-time view can
help companies understand—among a growing list of possibilities—how to optimize
the supply routes to a given finished product, how to most profitably serve
customers when several production sites exist, how many products to manufacture
from a single production site, and the best combination of make-versus-buy
options. Such end-to-end systems could provide companies with unparalleled “postmortem”
analysis of where value is leaking across their operations, as well as new ways
to simulate the forward impact of strategic decisions.
With the growing capture of unstructured
data on human interactions from video and social media, profit-per-hour metrics
could soon be applied in nonindustrial settings, such as retail operations. As
the quality of IT and analytic skills improves across sectors, and as managers
learn to accelerate frontline adoption, productivity levels are likely to
increase in a wide range of economic activities.
By Robert Feldmann, Markus Hammer, and Ken Somers
http://www.mckinsey.com/business-functions/operations/our-insights/pushing-manufacturing-productivity-to-the-max?cid=other-eml-alt-mkq-mck-oth-1705&hlkid=64a432e853f340149ee83f20bdee0ffd&hctky=1627601&hdpid=9b034a4f-9bcb-4248-bae9-d3e5c5d8e818
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