How data is changing the pharma
operations world
Pharma
companies have a great opportunity to turn a buzzword into exponential impact.
Aircraft today can be fully
developed in a digital environment. They are designed using CAD software and
tested in a virtual flight simulator, before any physical work happens. Imagine
the same in pharma: a COO can model various product portfolios, swap out machines,
or model utilization and schedules to optimize agility and cost—all using
software and delivering quantifiable answers in seconds.
Science fiction? Yes
and no. The technology exists today—including predictive analytics,
robotic process automation, and AI-based tools, all digitally
connected via the Internet of Things (IoT)—but no pharma company has fully
leveraged it. Some companies apply point solutions and individual tools, but
most get stuck in the pilot phase and struggle to scale up digital across the
enterprise. This approach leads to limited results that might excite the CIO
but not the CEO.
Why is it so difficult
to implement these tools at scale? You need
visionaries—and not just pragmatists—to see the full potential of digital. At
the same time, organizations need to manage expectations and understand the
impact will come in successive horizons, not all at once in the next budget
cycle. Finally, companies need to think of digital not as a series of
individual tools but as a means of transformation, requiring technology and
people.
The lessons of lean manufacturing
There is an analogy in
lean manufacturing. The lean methodology was
invented by Toyota as part of the company’s famed Toyota Production System. Throughout the 1980s and 1990s, that system led to
massive success in the automobile industry, with extremely high quality
standards and lower costs, allowing Toyota to dramatically increase its global
market share. In response, other car manufacturers tried to copy the principles
of lean. Yet, their early efforts fell short, in part because they tried to
implement individual lean tools and processes without having the right
organizational elements in place.
This approach was bound
to fail. In fact, the actions of those companies were almost more disruptive
than taking no action at all. One book, The Machine that Changed the World,
recounts this struggle, “Lean-production methods on existing mass-production
systems causes great pain and dislocation.” It took years for these
manufacturers to realize that success in lean required more than merely
plugging in some new technical tools or using fancy terminology. Rather, it
required a fundamental rewiring of the organization and managerial system,
which led to a cascading set of implications from the executive suite to the
shop floor.
As with lean, the value
from digital and AI will not come from trying to bolt tools onto existing
processes. It will come through a comprehensive transformation of the entire
organization. Currently, many pharma companies are at the earliest stages of
experimentation, still limited to pilot tests, small-scale digital initiatives,
and proof-of-concept exercises (or, as a recently McKinsey & Company paper
published in conjunction with the World Economic Forum termed it, “pilot purgatory”). These are
self-limiting; they will not generate the kind of dramatic results that leaders
expect, potentially threatening future investments. If these companies are to
take advantage of digital, they need to think in more comprehensive terms, by
making systemic changes.
Why the future is different
This kind of
comprehensive approach is necessary because of five fundamental shifts in the
way pharmaceuticals will be produced in the future. Collectively, the shifts
are leading to a quantum change in manufacturing—and order-of-magnitude
improvements in processes.
1. Advanced product and
process mastery.
First, companies can
use new technologies to better understand how input parameters such as machine
settings, operator training levels, or raw material options will affect product
quality and outcomes. In practical terms, companies can build an advanced
analytical model and run historical data on chemistry, manufacturing, and
controls (CMC) through the model to determine the impact of individual changes.
By mapping outputs to inputs, companies can proactively optimize all inputs and
thus reduce variations. In addition, by documenting this level of control over
input parameters to regulators, companies can get rid of testing and thus cut throughput
times in half. This obviously also boosts efficiencies, as most of the quality
assurance and quality control tasks disappear.
2. Real-time predictive
analytics.
Production managers
start their day with a simple question: what is my biggest risk right now?
Analytical models to predict critical events can answer this question. The
risks can be deviations, quality issues, machine failures, or large changes in
demand. Pharma operations executives can leverage big data, external and
internal indicators, and machine learning algorithms to better forecast demand,
and automatically identify and mitigate supply risks.
Companies can also
build digital simulations of production processes—on the level of individual
machines, labs, factories, or entire manufacturing networks (just like the
aircraft example discussed above). These real-time digital ‘twin’ simulations allow
companies to steer processes proactively by predicting the effect of adding a
new machine, changing schedules, or changing the team allocation—all before
applying those steps to physical assets. In this way, the company can optimize
production parameters for highly complex systems, accurately and proactively,
without risk. This is a significant advance in efficiency compared to the
traditional approach of sifting through historical data manually to try to spot
trends.
How is this different
from existing planning and scheduling solutions? Real-time digital simulations
offer dramatic improvements in speed and accuracy. “Demonstrated performance”
for factors such as lead times or machine output replaces master data (which
still today is of poor quality in most companies). Manufacturers can use
real-time AI-based insights to evolve beyond simplistic rules like “frozen
periods,” rhythm wheels, or minimum order quantities. Companies can run
multiple simulations at the same time, allowing them to plan across multiple
dimensions simultaneously, moving to multi-echelon planning. All this saves
time and reduces the need for inventory buffers, so that companies can plan and
sequence runs more effectively while staying focused on customer requirements.
4. Automation of
knowledge work and robotics.
Finally, tools are
emerging to automate and improve knowledge work and administrative processes.
For example, digital robots can autonomously handle measures such as supply
planning and scheduling, or use self-learning algorithms to support decisions
such as portfolio margin optimization or corrective and preventive actions
(CAPA). In the case of a product deviation, natural language processing can
show where and what went wrong and compile this into a Pareto diagram or some
other type of visualization. All these solutions reduce time—mainly of
white-collar workers—and not by 10% but by 90% to 100%.
5. Digital operations
assistance.
The biggest weakness in
pharma production is human error. Statistically, tasks performed by humans are
about 92% accurate. This is incompatible with the compliance expectation in
pharma. Therefore, the shop floor is increasingly digital, powered by new
systems that support operators in daily tasks—particularly those that are
highly repetitive. For example, tools such as augmented-reality glasses could
show operators the checklist of steps needed to finish specific processes, or
confirm that required measures have been completed, along with gathering and reporting
data to fuel analytical models. Managers could also be given a tablet-based
dashboard with real-time performance KPIs, losses, machine status, and
potential measures to improve. If something goes wrong—or is likely to go
wrong—the managers receive an alert.
The business case
Pharma supply chains
have traditionally been characterized by long lead times (up to one year for
the median pharma company)1and high inventories
(250 days).2And although customer
service levels are relatively high (99.1%),3most companies still spend a lot of time firefighting
and balancing supply and demand issues.
So, what is the
business case for digital? The technology is in the early stages, meaning any
estimate is just that—an estimate. Yet, McKinsey research suggests that
companies aggressively digitizing their supply chains can expect to boost
annual EBIT growth by 3.2% and revenue growth by 2.3 percentage points.4This impact comes from
increasing both the efficiency and the effectiveness of supply chain planning
and decision making. By connecting the supply chain end to end through
real-time performance feedback and leveraging data from various sources
(including ERP, MES, and external data, among others), supply chain executives
can generate greater visibility and improve performance.
Ultimately, digitizing
the supply chain creates a strategic competitive advantage that boosts the
supply chain organization from an execution-focused function to the center of
business growth.
Three horizons to generate scale
Companies seeking to
capitalize on these shifts typically start by launching small, targeted
measures and then scale up across the organization. We see this process playing
out in three horizons, with increasing gains in productivity and agility as the
scope of projects grows.
In the initial
phase—the experiencing horizon—the transformation is about launching use cases
that are high impact but limited in scope, typically aimed at one specific unit
or process. The objective is to build up experience and generate momentum. Even
at this early stage, companies begin to see increases in operational
efficiency, reliability, and agility. For example, based on recent digital
plant transformation case examples, we have seen:
·
Reductions in
deviations of up to 80%
·
Increases in lab
productivity by up to 60%
·
Reductions in closures
due to deviations of up to 80%
·
Increases in OEE of
more than 40% on packaging lines
·
Reductions in
changeover times by more than 30%.
Once the company has
gained some basic experience, it is time to shift to an exploring horizon. In
this phase, companies launch “lighthouse” projects that demonstrate the full
potential of a given technology and serve as an inspiration and innovation hub
for the company as a whole. The scope covers the entire site, and the company
starts using technology to differentiate itself against the competition.
Typical achievements in the exploring horizon include prescriptive analytics
(rather than descriptive), fully standardizing data across an entire production
site, or a suite of advanced digital assistance to all operators at the site.
It is critical in this phase to use models geared to value creation. Companies
can expect an additional 50% increase in terms of efficiency, quality, and
flexibility metrics.
Finally, companies can
advance to an envisioning horizon, in which digital is rolled out across the
entire value chain. By now, the organization has predictive machine learning
models in place that can actively suggest optimization measures. Data is easily
accessible and transparent, including secure e-validation from end to end. Many
tasks have disappeared (such as testing) and regulatory and technology changes
are simulated and approved. Advanced digital assistance tools are in place
throughout all production sites (including those of suppliers and customers).
At an envisioning horizon, companies can register a 100% increase across key
operational metrics.
Key success factors
The reason many
companies fall short in digital transformation is that they focus too much on
the tools and applications, and not enough on building the right internal
foundation. A truly transformative system change can only happen if companies
have the conditions in place. To that end, there are several key success
factors that management teams should focus on.
Invest in capabilities and adjust the organization
According to some
estimates, up to 60% of manufacturing tasks can ultimately be automated by
digital. However, that does not mean that 60% of jobs will go away. Rather,
digitization will transform the way work gets done, scaling back the need for
some positions but creating new demand for others. For example, data
scientists, data engineers, and developers that specialize in user interfaces
will be in high demand. Already, companies are fiercely competing for people
with these skills, by launching dedicated internal capability-building
programs, collaborating with universities, and revamping degree and
certification programs. Moreover, companies are redrawing organizational lines
to deploy digital production—for example, creating cross-functional teams that
combine operations disciplines with IT. Others are realizing that the
elimination of tasks in planning, analyzing, training, and supervision has
implications on workloads as well as the required skills of their people, and
they are rewriting job descriptions accordingly.
Apply strong change management
Launching a “digital
transformation” can seem ambitious. But tentative half-measures will not
capture the full potential from technology. Companies need to apply strong and
deliberate change management through a comprehensive program that spans the
entire value chain. Moreover, the transformation will disproportionately impact
the middle layers and white collar workers in expert roles. Unless you have a
vision and provide support to these colleagues—starting with a story that
clearly communicates the need for change—the adoption of digital will stall.
Consider the
perspective of a planner or process engineer at your company. After the first
successful pilot of automated planning or AI used to predict batch outcomes,
people in these roles will see that the processes they have spent years
studying and improving can be executed by an algorithm in seconds, often with
greater accuracy. It is inevitable that the sheer speed of information flow and
transparency of performance data will create discomfort for many employees
throughout the organization. Change management—starting with a change story, a
strategic resource plan, and a reskilling program to build new cross-functional
teams—can address this discomfort and ensure the overall program stays on
track.
Rather than thinking
about IT projects as multi-million dollar, year-long transitions without a
clear business case, companies should set up a common platform to connect all
data sources (for example, through a data lake). The trick to connect more data
in less time? Start with business needs. Once you have created the data lake as
a foundation, you only create the 2 to 5% of connections that actually matter
for the first generation of use cases. Transformations typically require 20 to
25 use cases before they generate sufficient momentum. Achieving this level of
connection can take three to six months using a data lake—not three to four
years as with traditional IT projects. This approach—also called “agile
development” of IT infrastructure—is obviously a mindset shift and in some
cases may not mesh with the existing IT roadmap that many companies already
have in place. Unless there is clear agreement among the leadership team, this
misalignment can quickly lead to delays.
By Thibaut Dedeurwaerder, Daniele Iacovelli, Eoin Leydon,
and Parag Patel
https://www.mckinsey.com/business-functions/operations/our-insights/how-data-is-changing-the-pharma-operations-world?cid=other-eml-alt-mip-mck-oth-1808&hlkid=57de1bacbd9e4ebc9310ffd4b701a741&hctky=1627601&hdpid=ef36cd3a-0ecf-41a0-8f76-d74e8dc64199
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