Digital innovation in consumer-goods manufacturing
Consumer-goods
companies have begun to capture value by applying digital tools to
manufacturing. Here’s a look at how they’re doing this today—and how they might
do so tomorrow.
Consumer-goods
companies have been at the
forefront of digital innovation in commercial areas such as marketing and
sales. Supply chain and operations have been less of a focus for their digital
efforts, but recently, leading consumer-goods companies have started to explore
the use of digital solutions in manufacturing processes. This is a natural
development; Industry 4.0—the digitization of
the entire manufacturing value chain—is slowly becoming a reality.1
Some consumer-goods
companies, however, are unsure where to start: Which aspects of manufacturing
can benefit most from today’s digital technologies? And what should
leading-edge companies set their sights on next? In this article, we examine
the two most prevalent ways in which consumer-goods companies are using
digitization in manufacturing: applying digital tools to lean transformations
and using advanced analytics to
optimize specific manufacturing processes. We then look at the next horizon of
opportunity for digital manufacturing in the consumer-goods sector. Finally, we
discuss the organizational enablers that can help digital-manufacturing efforts
succeed.
Taking lean to a new level
Lean transformations have
already had a dramatic impact on many companies, but digital solutions are
taking lean operations to a new level. Consider the case of a
food-manufacturing company that invested in lean techniques but didn’t have a
standard process or system for collecting data, tracking performance, and
sharing information. The company’s data—sales- and operations-planning data,
machine-level data (such as those in sensors), benchmarks, operating standards
for equipment, training materials, work plans, and so on—resided in several
different databases and repositories, making it difficult for supervisors to
find and analyze information. For instance, due to ad hoc tracking of equipment
downtimes, supervisors never knew the exact quantity of goods produced until
shipping time, when shortages could disrupt the entire supply chain.
Following a practice
that has worked well in other industries, the company consolidated data and
assets into a cloud-based digital hub. The hub contains three suites of tools
to support day-to-day lean operations: a performance-tracking and management
system, a set of modules for assessing operational capabilities and planning
improvement initiatives, and a platform for best-practice sharing and real-time
collaboration.
Supervisors can now
access company-wide information on intuitive dashboards and heat maps, allowing
them to detect performance gaps and compare metrics by product, site, and
region. They can easily access detailed historical performance data or
information on specific operational topics, such as the breakdown of overall
equipment efficiency (OEE) by category. Since the hub automates data
collection, data exports, tracking of key performance indicators, and
generation of email reports, employees’ paperwork has substantially decreased.
The digital hub also
introduced a new culture of collaboration and continuous improvement. For
instance, all functions now systematically track and share equipment-downtime
information via the hub. The shared data enable more productive
cross-functional discussions about production problems, including root causes
and potential solutions. Frontline workers are thus more likely to discover and
resolve issues in real time, preventing small problems from becoming major
disruptions. Staff members can submit new best practices or improvement ideas
at any time, which makes them feel more invested in the transformation. And
scaling up is easy, with managers able to deploy the new digital tools to new
sites or business lines rapidly, using minimal resources.
After launching the
digital hub, some of the company’s factories improved OEE by as much as 20
percent within a few months.
Unlocking manufacturing insights through advanced
analytics
Leading consumer-goods
companies have already scored big wins by using advanced analytics in a number
of manufacturing processes. In our view, some of the highest-impact
developments have been in quality control, predictive maintenance, and
supply-chain optimization.
Quality control
A potato-chip
manufacturer wanted to ensure that its products had a consistent taste,
especially when it came to “hotness,” or spiciness. In the past, it had
assessed hotness by conducting taste tests in which a panel of human testers
rated various taste parameters (for example, rating the hotness level on a
scale of one to ten)—an expensive and unreliable process, since taste is
subjective. To increase accuracy, the manufacturer began using infrared sensors
to identify and measure recipe parameters associated with hotness. It then
developed customized algorithms to process the sensor data and determine how
they were correlated with the recipe. Researchers also compared the sensor data
with the results of a taste-test panel for each batch. Together, this
information allowed the company to create a quantitative model for predicting
hotness and taste consistency. Within a year of implementing the program,
customer complaints about variability in the flavor of the company’s chips
dropped from 7,000 a year to fewer than 150—a decrease of 90 percent.
A margarine producer
took a similar approach when attempting to understand how variations in
multiple process settings could change product viscosity, an important quality
parameter. During a pilot, the company tested variations of a number of
parameters (such as temperature) and used sensors to evaluate emulsion crystal
size, the primary determinant of viscosity. After analyzing data from the
pilot—much more detailed and extensive than what it would have obtained in the
past—the company was able to correlate viscosity levels with certain parameter
variations. With this information, analysts created a model that predicted the
viscosity that other parameter combinations would produce, which reduced the
need for additional testing and helped the company identify optimum operational
settings. This approach reduced the fraction of margarine tubs that had to be
discarded because of quality issues from 7 percent to almost zero.
Predictive maintenance
Consumer-goods
companies have begun to apply predictive analytics to maintenance activities,
decreasing maintenance costs by 10 to 40 percent. A diaper manufacturer had
historically replaced all cutting blades at certain intervals, regardless of
their condition. This sometimes resulted in blades being replaced too
soon—which increased costs—or too late, after their dullness had already
affected diaper quality. To address these problems, the company turned to
sensors that could detect microfibers and other debris—indications of blade
dullness—by analyzing video feeds of diapers during the manufacturing process.
After uploading the results of the analysis to the cloud, the company analyzed
them in real time, using customized algorithms to determine the optimal time
for blade replacement. By making adjustments to the maintenance schedule, the
company lowered costs while improving product quality.
Supply-chain optimization
At a leading European
dairy company, raw-milk purchases represented almost 50 percent of the cost
base. Most of the raw milk was used to produce pasteurized milk; the company
had to decide how much of the rest to use making butter, cheese, or powdered
milk. The profits associated with each of these product categories fluctuated
significantly, adding another layer of complexity. In the past, the company
gave its regional businesses the freedom to make their own raw-milk allocation
decisions, provided they followed a set of simple guidelines. In an effort to
reduce costs and optimize supply-chain planning, the company used an analytics
software solution that determined the best allocation plans for each region,
taking into account variables such as available milk supply, regional factory
capacity, and global demand. The improved allocation helped the company
increase profits by about 5 percent without changing production volumes or
capacity.
The next horizon for digital manufacturing
Consumer companies may
also soon reap greater benefits from new digital tools that are continually
being refined. Consider the following innovations:
·
Augmented-reality
tools. These tools
provide data about the user’s environment in real time and facilitate information
sharing. With smart glasses, for instance, employees can see and view new work
orders while on the factory floor, or take and transmit photos of broken
machines to offsite experts. We estimate that smart glasses could improve
productivity by 5 to 10 percent by increasing the speed of operations,
improving communication, and enabling paperless processes. Other
augmented-reality tools could provide instructions to technicians responsible
for complex changeovers or to warehouse workers searching for particular items.
·
3-D
printing. Consumer-goods
companies could use 3-D technology to facilitate product design and the
manufacture of samples. At one shoe manufacturer, 3-D technology reduced the
number of employees needed to create prototypes from 12 to 2, significantly
decreasing costs. Companies could also use 3-D printing to print
low-frequency replacement spare parts on demand at a production site rather
than keeping them in stock or having them shipped after a breakdown. This
approach would reduce the cost of holding spare parts, facilitate maintenance
processes, and reduce downtime.
·
Connected
sensors and controls. Companies
across industries have recognized the potential of the Internet of Things (IoT)and invested in connected sensors, such as those that
can detect unusual machine vibrations and transmit their findings to monitors
in a remote location, allowing offsite staff to direct corrective actions
without having to travel to the facility. In heavy industries like mining, IoT
sensors have reduced costs by 40 percent and downtime by half. While some
consumer companies (such as the diaper manufacturer mentioned earlier) have
invested in IoT sensors, most lag behind their peers in other sectors. We
believe this will change as IoT offerings become more sophisticated and
consumer companies realize the value at stake.
Organizational enablers for digital manufacturing
Some companies,
especially those in the services sector, have already made changes to their
organizational structures and strategy to support digitization efforts—for
example, by buying niche technology players or creating innovation labs in
talent-rich locations. Consumer-goods companies must now follow their example
to gain maximum benefits as they digitize their own production lines. Since few
consumer-goods companies today have the in-house capabilities needed to support the development and use of innovative
digital manufacturing tools,
they must upgrade their strategies for recruiting, training, and retaining data
scientists, software engineers, and other technology staff (exhibit).
Competition for this talent is stiff, with demand four times higher than supply
for some positions.
Corporate governance
must also become more agile to promote digital manufacturing. The technology
staff responsible for developing and testing tools should generally have the
authority to set budgets and priorities, since they will lose momentum if they
have to wait weeks for approval from upper management. When a major initiative
does require leadership support or input, local teams should have easy access
to decision makers.
Finally, large
consumer-goods companies may need to pursue partnerships with smaller players
or start-ups to gain essential digital capabilities. Many companies in other
sectors have already pursued this strategy, with good results. For instance,
Amazon acquired Kiva Systems, a small robotics company, to develop the
cutting-edge robot technology now in widespread use across its warehouses.
Partnerships among large players can also contribute to the development of
solid digital platforms. Consider the recent collaboration between SAP, the
enterprise-software giant, and UPS, a large package-delivery company. The
companies ultimately hope to create a global network that provides industrial
3-D-printing services, on-demand production capabilities, and other services.
Consumer companies are already
benefiting from the use of digital tools in marketing and sales—applying them
to manufacturing is therefore an obvious next step. What is also clear, however,
is that companies cannot simply implement digital solutions and hope to achieve
lasting impact. They must also undertake an organizational transformation that
involves acquiring new talent and capabilities, streamlining the
decision-making process, making governance more flexible, and collaborating
with external partners. This transformation touches every group within the
company and will require the full commitment of employees at all levels. But
the long-term benefits of digital solutions, which will usher in a new era of
manufacturing efficiency, more than justify the effort.
By Søren
Fritzen, Frédéric Lefort, Oscar Lovera-Perez, and Frank Sänger
http://www.mckinsey.com/industries/consumer-packaged-goods/our-insights/digital-innovation-in-consumer-goods-manufacturing?cid=other-eml-alt-mip-mck-oth-1611
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