Putting Value First in Digital Operations

Related Expertise: Operations, Industry 4.0, Digital Transformation

Putting Value First in Digital Operations

By Jonathan Van WyckJustin RoseJustin Ahmad, and Daniel Küpper

This is the second in a series of multimedia offerings that highlight insights derived from a recent BCG survey of digital maturity in manufacturing operations. Here, we examine the imperative to build a portfolio of use cases that address a company’s specific operational pain points. The first article, “The How-To Guide to Digital Operations,” discussed what sets apart the leaders, presented a framework for digitizing operations, and explained the importance of an upfront digital vision. The final article will examine the organizational and technical enablers.

Manufacturers are being inundated with offers from tech companies claiming to have the next breakthrough solution for digital operations. Cutting through that noise can feel like an impossible task. In our view, it’s better to tune out the sales pitches and focus instead on what you seek to achieve from a digital transformation.

It’s certainly true that tech companies are critical partners in a transformation. But our experience shows that the key to capturing value is to start by gaining a deep understanding of the pain points and opportunities in your operations. This provides the basis for defining the use cases that will deliver the most value to your company. Rather than taking a technology-first approach, use cases should be articulated in terms of addressing business needs—such as reversing the increasing cost of prototypes. You will most likely find that many of your high-priority use cases require combinations of technologies, not a single technology. And, often, the same few technologies cut across many pain points. Only once this knowledge is in hand is it time to engage with technology partners.

To help companies begin this journey, we have identified the most important value drivers and use cases, as well as their potential impact, for a broad array of manufacturing industries. The insights are based on our experience supporting digital operations transformations and an extensive survey of executives across a variety of industries. (See the sidebar, “About the Survey.”) 

About the Survey

In October 2018, BCG conducted an online survey of executives from large manufacturing companies in order to understand their progress in adopting digital technology in operations. We defined manufacturing operations to include product engineering and design, supply chain management, production, and distribution and field service. The survey’s participants consisted of 250 executives and managers from global companies representing a broad array of manufacturing industries: aerospace and defense, automotive, chemicals, consumer and retail, energy, machinery, metals and mining, and pharmaceuticals.

A Four-Step Approach

Companies can use the following four steps to identify, prioritize, test, and scale use cases.

Step 1: Identify Use Cases in Fact-Based Workshops

The team leading the digitization program can turn to multiple sources to gain insights into pain points and opportunities in operations. There’s a right way to do that, and a wrong way: Many companies mistakenly conduct a broad survey to solicit ideas from operations employees or follow an ad hoc, “first come, first served” process. The problem is that, without proper guidance about the context, employees often identify projects that are too narrow in scope or not scalable.

A better approach is to conduct facilitated workshops in which colleagues representing different operations functions and manufacturing environments discuss ideas and identify those that link to organization-wide priorities. For example, drawing on BCG’s database of approximately 270 use cases and an on-site self-assessment enabled by our mobile app, workshop participants can typically identify 15 to 20 specific use cases per site for further examination.

Additionally, an analysis of the cost base can clarify pain points, and a review of operational KPIs that are trending in the wrong direction can reveal waste that should be addressed. The team can also assess ongoing digital pilots to identify opportunities for improvement and scaling up.

Step 2: Prioritize and Refine Use Cases

Use cases should be prioritized by considering the time, effort, and cost required for implementation and the achievable impact. As noted, it is especially important to consider whether each use case is scalable. The criteria for qualifying a use case as viable should be clearly defined. In our experience, a company must be able to take the following actions for each use case:

  • Develop a description of the use case that is sufficiently detailed to enable vendors and other stakeholders to understand how it will address the particular pain point.
  • Identify the underlying technologies and assess the feasibility and scalability of implementation.
  • Identify potential technology vendors or development partners.
  • Determine the required investment and develop a high-level business case.
  • Define a plan to rapidly test the use case in proof-of-concept (POC) projects.

The optimal portfolio will vary depending on a company’s digital maturity and its industry. Even different companies in the same industry may face distinctive operational challenges. Typically, we’ve found that companies with a moderate to high degree of maturity in deploying lean manufacturing principles are best positioned to digitize operations. The process stability promoted by lean provides a strong foundation for digitization.

A central team can play a critical role in the process of identifying and prioritizing use cases. In addition to receiving ideas from the business, it can proactively provide to the business ideas on how to address the most critical pain points in operations. The central team can also provide distinctive and essential technical expertise.

Once the company has developed a prioritized list of use cases, hosting a series of “vendor days” is a valuable way to gather the right subject matter experts in one place. The experts refine the use cases and hear directly from vendors’ representatives about how they would solve the practical challenges of implementation. The company can also invite vendors to test their proposed solutions on site and compare success rates.

Step 3: Test in Proof-of-Concept Projects

To conduct POC projects, the company identifies the most critical aspect of a technology solution to test on a small scale. This approach allows the company to quickly learn about the technology with minimal investment. 

For example, a manufacturer of automotive seats investigated the possibility of developing a multifaceted robotic solution that could identify and remove wrinkles on fabric, which are the most prevalent defect in its manufacturing process. Traditional inspection technology and manual inspections were not well suited to detecting the wrinkles, owing to their varying degrees of severity and dispersal throughout the fabric.

At some companies, a central team provides physical space in which to conduct POC projects before scaling them up in the company’s actual operations. For example, Ford has invested in an Advanced Manufacturing Center to accelerate the deployment of digital operations use cases across its manufacturing network. The center features several “cells” in which agile teams use POC projects to test technologies, including additive manufacturing, collaborative robots, machine vision, and automated guided vehicles (AGVs). These concepts, once proven for the specific application, will be deployed into operations. The center provides a unique physical space for testing, as well as access to specific technical skill sets that can be best utilized centrally.

Step 4: Scale Up in Waves

A company can accelerate returns by scaling up proven use cases in waves prioritized by value. The company selects the best facilities or functions in which to scale up the first wave of use cases across its value chain. For example, automation of specific processes or inventory optimization for specific types of parts can be implemented across the network. The company then proceeds to implement successive waves as each set of initiatives is tested and validated. Because stakeholders throughout the value chain must be motivated to participate in the effort, this approach is best suited to organizations that are already committed to digitization.

For example, a metals company launched its digital transformation by deploying data and predictive analytics first, rather than automation; this choice enabled a relatively fast return on investment. The company adopted an integrated, cross-functional approach to implementation that spanned business strategy, IT, production, distribution, and sales. Flexible data lakes with data feeds across functions enabled rapid reporting, while predictive analytics drove improvements across the value chain. Interlinked use cases improved the end-customer experience and provided insights about individual asset performance across the entire supply chain. The digital initiatives succeeded in significantly reducing bias and volatility in capacity forecasting.

Scaling up in waves has multiple benefits. It simplifies the budgeting process by allowing the digital team to aggregate use cases when submitting a funding request. It also enables the sharing of resources. For each proven use case, the company designates a project manager and forms a cross-functional project team comprising technical experts from the affected departments, such as maintenance or quality. Although each use case has a dedicated team, experts are staffed on multiple teams so that they can apply their capabilities at scale. Moreover, the broad scope of the rollout promotes impact across the organization, which generates excitement and reinforces the case for additional funding.

By combining advanced vision technology and artificial intelligence, the robotic solution would be able to learn and search for the characteristics of defects, providing a more comprehensive and consistent assessment. The company determined that the key technical challenge was ensuring that an algorithm could use machine vision technology to identify wrinkles.

So, rather than trying to develop the entire solution, the company set up a machine vision camera, at relatively low cost, to take photos. The company’s technology partner is using the photos to train the algorithm to identify wrinkles. Once the technology partner achieves the desired level of accuracy, the algorithm will be integrated into the robotic solution, and the company will then test and deploy the entire solution.

Alternatively, a company can select one factory, warehouse, or region to be the proving ground in which to test use cases and scale up those that are successful. The integrated testing of multiple use cases in a single facility or region allows the company to understand and capture synergies among applications. The facility or region also supports change management by showcasing the opportunities. Executives and other stakeholders can visit it to see the use cases in action, understand the possibilities, and get excited about the opportunities. This makes the approach well suited to companies that are at an early stage in the digitization journey.

Before using this approach, however, companies should consider whether the facility’s or region’s pain points are representative of those in the broader manufacturing network. Further, the effort to digitize a facility or region and demonstrate impact can take at least one year, whereas scaling use cases in waves can yield results faster.

Foxconn has deployed a fully automated “lights out” factory as a strategic investment to capture and showcase a variety of benefits. To reduce labor costs, it uses robots for assembly, AGVs for supply replenishment, and digital tools for production planning. Machine vision improves the accuracy of quality inspections. Data transparency is enhanced by the use of an Internet of Things gateway that collects and analyzes data. Machine feeds self-correct on the basis of real-time data from the machine and quality inspections. To enhance productivity, the factory uses electronic boards to show real-time performance, data analytics to optimize processes and eliminate waste, and predictive maintenance to improve machine uptime.

Value Drivers and Top Use Cases Across Industries

In our global survey, respondents rated the savings and growth impact as well as the speed of implementation across a set of 38 use cases. Overall, use cases related to planning and production—including advanced analytics, control towers, and real-time performance management—were perceived as having the highest impact in many industries. (See Exhibit 1.) 

Use cases related to planning were most likely to have already been implemented at scale, with automotive and pharmaceutical companies leading the way. (See Exhibit 2.)

For each of the eight industries, BCG experts complement the survey findings by providing their perspectives on the sources of value in digital operations and the leading use cases.

Aerospace and Defense

Lacy Ketzner is a partner and managing director in the Philadelphia office of Boston Consulting Group.

Philippe Plouvier is a partner and managing director in the firm’s Paris office.

Aerospace and defense (A&D) companies are, surprisingly, less digitally mature than many other industrial manufacturers. Specifically, A&D companies lag behind other industrial manufacturers and automotive companies in building their talent, data infrastructure, and digital ecosystems. Even so, digital is at the top of the agenda of A&D CEOs, most of whom are increasing investments in digital technologies. Leaders have set a digital vision and are broadly looking to increase the use of digital in their operations.

However, many also worry that these investments are outpacing returns and that they are not realizing enough near-term value from their efforts. This worry is driven, in part, by the organizational challenges that companies face as they seek to scale digital use cases.

During the next five years, A&D companies must overcome these challenges in order to digitize their operations end to end—from design to service—and across the supply chain. This “digital continuity” has the potential to disrupt current processes and performance levels by enabling:

  • Drastic reduction of development cycles for major modifications, upgrades, and new platforms
  • Continuous optimization of equipment and system performance by monitoring the field and understanding how design affects performance
  • Continuous improvement of manufacturing by using data analytics to identify the root causes of deviations, whether arising from the design of the equipment or system or from its supply chain or assembly
  • Faster and more cost-efficient ability to handle customization by simulating impacts on supply, production, and support systems through the use of “digital twins” that replicate end-to-end processes
  • Continuous optimization of costs for support and service through the monitoring of each platforms’ individual parameters and the analysis of the root causes of its performance issues

Beyond traditional digital use cases, such as augmented reality and automated picking for warehouse replenishment, two use cases can potentially have a large impact in the short term.

The first use case, control towers for advanced planning and logistics optimization, can significantly improve supply chain performance in terms of quality, costs, cash, and on-time delivery. Although control towers are easy to deploy and can generate short-term benefits, they are unlikely to be a source of sustainable competitive advantage. We expect them to become table stakes in the industry within five years.

Analytics for predictive maintenance constitute the second use case; they are being deployed for engines and other systems to prevent downtime. However, this use case is still the holy grail for other applications, such as electronic systems. A leading aircraft OEM was one of the first to invest in preventive maintenance capabilities. By using advanced analytics to increase the uptime of its aerospace engines, the company reduced service costs and guaranteed uptime for its customers. Other major aircraft OEMs and many of their major tier-one suppliers are also investing heavily in their preventive maintenance capabilities and the supporting infrastructure, in order to increase their aftermarket sales.

A&D companies participating in BCG’s Digital Operations Survey identified the following top use cases and corresponding savings or growth impact in their operations to date:



Justin Ahmad is a partner and managing director in the Boston office of Boston Consulting Group.

Daniel Küpper is a partner and managing director in the firm’s Cologne office.

Automotive players are focusing on digital solutions to improve supply chain and manufacturing processes, as well as to strengthen new program launches. OEMs are developing ways to reduce disruptions across their supply chains by extending visibility beyond tier-one suppliers. They are also deploying advanced analytics to help identify the need for preventive actions and optimize inventory levels. 

Within plants, OEMs and tier-one suppliers are deploying digital solutions to help achieve reductions in conversion costs that go well beyond those achieved by traditional lean and continuous-improvement techniques. They are incorporating digital improvements into new program launches (integrated into process upgrades) and existing production processes. They are also using digital solutions to reduce the timeline and costs of testing physical products and processes prior to new program launches. Technologies used for this purpose include simulations, augmented reality applications, and 3D printing for prototyping.

Many OEMs and suppliers are also investigating the use of digital technologies to enable structural changes (such as for factory layout) and to enhance the application of lean initiatives aimed at process improvements. Such a holistic optimization of a factory can yield results that go significantly beyond what is feasible using isolated deployments of individual digital use cases.    

For example, a leading tier-one supplier with a strong cultural emphasis on lean and continuous improvement developed a “factory of the future” vision to drive step-change reductions in conversion costs. The approach focused on addressing core pain points and pursuing opportunities that could dramatically reduce costs and strengthen processes. Individual use cases focused on reducing labor costs in multiple areas, including materials receiving, kitting, and line-side delivery to assembly processes. The solutions deployed included the use of collaborative robots to perform assembly tasks, advanced analytics to power dashboards that provide supervisors with alerts on high-risk productivity- or quality-related conditions, advanced vision and scanning applications to monitor quality, and automated guided vehicles and other automation to enable delivery of parts to the line without human intervention.

Automotive companies participating in BCG’s Digital Operations Survey identified the following top use cases and corresponding savings or growth impact in their operations to date:



Jihoon Kim is a partner and managing director in the Seoul office of Boston Consulting Group.

Adam Rothman is a partner and managing director in the firm’s Chicago office.

The chemical industry generally lags behind other industrial sectors in digitizing operations, for several reasons, including companies’ inherent conservatism, their comfort in using existing advanced process control systems, and the risks associated with altering complex and highly hazardous processes. Additionally, because some companies, such as those in Asia, have already moved aggressively to achieve high levels of utilization, they typically are not motivated to pursue the relatively marginal improvements enabled by digitization. Moreover, because it is not easy to connect consumable products to the Internet of Things, there are few “killer apps” to drive digital adoption.

However, chemical companies in all regions and industry segments are increasingly exploring digitization. Raw materials and energy are typically the starting point, because they comprise the largest share of the cost structure. Although the highest-priority use case depends on the segment and process technology (batch or continuous), many companies focus first on deploying advanced analytics for process optimization. This use case helps them understand the relationships between process parameters and yield, for example, and allows them to make tradeoffs in real time.

Some companies have augmented existing visual management systems so that they can connect the impact (in terms of actual cost or opportunity costs) to specific operational choices made by the plant team. Making this connection bridges the gap between the management team and the plant team with respect to decisions on how to improve yield or change the product mix. 

For companies with large-scale assets, predictive maintenance has become an important lever to reduce unplanned downtime, maintenance spending, and spare-parts inventories. Leaders are adding low-cost sensors to critical process equipment (such as pumps and valves) to monitor subtle variations in operational performance (such as vibrations and acoustics) that are predictive of equipment failures. They are also using advanced analytics to detect early signs of failures that could not be identified on the basis of human experience.

Leading chemical companies are also optimizing their supply chains, which are often complex, hazardous, or tightly regulated and located in multiple regions. The varied objectives include improving service levels without the need for incremental inventory investment, more effectively managing grade complexity, and optimizing production schedules to squeeze excess capacity out of key assets without the need for incremental capex.

Companies typically start by enhancing their demand-sensing function. They deploy advanced analytics and machine learning to feed a more accurate demand forecast into the production schedule. By improving forecast accuracy, a leading diversified specialty chemicals company was able to shift its production strategy for a semi-finished product from make-to-order to make-to-stock, enabling radically shorter lead times and an enhanced value proposition.

In research and product development, leading companies recognize that digital use cases can augment, rather than replace, traditional approaches to getting the right products to market faster or developing better products. These companies are pursuing two complementary tracks:

  • For some product families, companies combine machine learning, computer vision, and traditional analytical chemistry to accelerate the process of developing a candidate molecule or formulation to meet a specific performance specification. Additionally, artificial intelligence can use machine learning to understand existing pain points and apply the insights to develop new classes of formulations.
  • In upstream technical development, some companies are experimenting with AI to enrich and sharpen their ability to anticipate market needs. For example, a producer of paints and coatings monitors and mines social media trends to anticipate popular colors for the upcoming remodeling season.

Chemical companies participating in BCG’s Digital Operations Survey identified the following top use cases and corresponding savings or growth impact in their operations to date:


Consumer and Retail

Olivier Bouffault is a partner and managing director in the Paris office of Boston Consulting Group.

Elfrun von Koeller is a partner and managing director in the firm’s New York office.

Consumer goods and retail companies are turning to digital operations to relieve the intense competitive and operational pressures confronting their industries. Leading consumer goods companies are losing market share to disruptive niche brands and must cope with increasing service requirements from retailers and constant cost pressure. Retailers face tough competition from online players and discounters that are taking foot traffic out of their stores. All companies in the consumer space must manage the operational complexity that arises from offering an increased product assortment with shorter life cycles and meeting demand across multiple channels. They need greater visibility and agility in supply chains and more flexibility in manufacturing processes. 

In this context, companies are applying digital use cases to integrate the entire supply chain, from suppliers to store to shelves. Techniques for advanced forecasting and demand sensing provide more accurate, granular, and flexible insights into consumer and customer demand.  Companies apply these insights to optimize service levels and on-shelf availability, as well as to reduce transportation, logistics, and production costs. The insights also enable better decisions about assortments, distribution, and pricing and promotions. Control tower solutions provide end-to-end visibility into supply chains and enable inventory optimization. Additionally, for certain categories, manufacturers can apply highly flexible, small-batch techniques that rely heavily on digital technologies. Each of these use cases can promote savings of up to 10%.

Consumer and retail companies participating in BCG’s Digital Operations Survey identified the following top use cases and corresponding savings or growth impact in their operations to date:



Paul Goydan is a partner and managing director in the Houston office of Boston Consulting Group.

Sylvain Santamarta is a partner and managing director in the firm’s Amsterdam office.

Odd Arne Sjåtil is a partner and managing director in BCG’s Oslo office.

The energy sector has embraced the challenge to digitize operations. There is broad recognition that new technologies and the power of data can deliver significant value in core operations, as well as bring new opportunities for value creation through new business models.

The oil and gas industry is a case in point. Most companies have dedicated resources and devoted management attention to digitization. They have adopted new ways of working (such as agile), implemented narrowly defined use cases, and solved challenging technical problems. They are also pursuing opportunities to apply big data to generate value in end-to-end workflows. But many companies have struggled to scale up their program and demonstrate real structural improvements in their operational performance.

Oil and gas companies typically start with a fragmented technology landscape, siloed systems, and a set of use cases developed by technical functions. As a result, they find it hard to transition to a “for the business, by the business” program that delivers impact. A common challenge is clarifying the role of each organizational unit in the execution of use cases. Additionally, companies often lack, or are slow in developing, the data governance and IT architecture require to scale up the program. Companies have only recently begun to deploy sensors to capture data in their operating environment and apply advanced analytics to generate insights. To succeed, they need to overcome the challenges of structuring the operational data, which is often fragmented and loosely organized.

Despite the pervasive challenges, we have seen successful digitization programs that deliver tremendous business value. For example, BCG supported an exploration and production (E&P) company in setting up an integrated operations center (IOC) to collocate multidisciplinary teams of engineers and provide them with new digital tools. By applying predictive analytics to identify performance issues, the teams increased the uptime of rotating equipment and removed production constraints, thereby increasing the maximum potential of production.

As the foundation for digitization, the company’s IT function modernized the architecture of the corporate data platform by using a cloud-based solution to connect the existing legacy systems. The modernization was completed incrementally to meet the needs of the use cases that the platform was supporting. The company also defined the digital operating model that the IOC would enable and ensured that the supporting data, analytic modules, and interfaces were available. An effective delineation of responsibilities among the frontline, IT function, data scientists, and IOC was critical to success.

Energy companies participating in BCG’s Digital Operations Survey identified the following top use cases and corresponding savings or growth impact in their operations to date:



Jonathan Brown is a principal in the Hamburg office of Boston Consulting Group.

Ralph Lässig is an associate director in the firm’s Cologne office.

Brian Myerholtz is a partner and managing director in BCG's Chicago office.

A large share of the machinery industry’s products is customized or customer-specific, so it’s essential to find ways to flexibly and efficiently provide a low-volume, highly diverse product mix. Many companies are turning to digitization to achieve these goals. Component suppliers and automation specialists are technology specialists, so they possess capabilities that allow them to apply new digital technologies and solutions on their shop floor.

The flexible automation of production processes is a leading opportunity to transform operations through the application of advanced digital solutions. A large portion of parts manufacturing is already automated, such as through the use of machining centers—milling or drilling machines automated by computer numerical control (CNC).

Additional opportunities to automate logistics and flexibly connect work centers await digital alternatives. Assembly processes are still primarily manual, although they are automated to some extent. Efforts to further automate individual assembly steps are impeded by the need to maintain flexibility for changeovers. Companies must combine automation and manual labor along the assembly line to accommodate the complexity of individual products. This requires the deployment of equipment that can interact or collaborate with human workers. Examples include self-driving vehicles that are integrated with existing control and automation systems or collaborative robots that interact with workers in assembly processes.

Additional transformative opportunities include:

  • Implementing flexible, real-time production planning in order to adapt quickly to new customer requirements and changes
  • Fully digitizing the connection between engineering and production, to enable the efficient transfer of new products and/or customer-specific product modifications into production
  • Fully digitizing the supply chain in order to improve transparency into costs and the composition of assembled products1 
  • Using 3D printing to manufacture parts with geometries that cannot be produced via machining and cutting, as well as for the rapid, cost-efficient provision of spare parts in any location around the globe

Many machinery producers have a wide variety of machines and equipment on their shop floors, provided by a large number of vendors. To allow for efficient operation and maintenance, companies need compatible and harmonized digital platforms for all of the digital solutions they implement. 

To address the challenges of harmonization, a leading machinery producer has introduced a “smart factory” concept that combines lean manufacturing techniques, advanced and additive manufacturing, and advanced software analytics. For example, a production execution system digitizes orders, process steps, instructions, and documentation. The system pulls information directly from existing enterprise databases to get the correct information to the right people at the right time. Another system transforms real-time machine data into actionable production efficiency metrics. The impressive results include reducing machine downtime to less than 1%, a 10% to 20% reduction of unplanned downtime, and a 25% increase in productivity. The flexibility promoted by these improvements has enabled each smart factory to manufacture products for a wide variety of industries.

Machinery companies participating in BCG’s Digital Operations Survey identified the following top use cases and corresponding savings or growth impact in their operations to date:


Metals and Mining

Gaurav Nath is a partner and managing director in the London office of Boston Consulting Group.

Amit Ganeriwalla is a partner and managing director in the firm's Mumbai office.

Metals and mining companies are turning to digital technologies to enhance their ability to anticipate and mitigate risk or seize opportunities in their extremely complex supply chains.

Predictive algorithms and artificial intelligence help companies understand demand by identifying patterns in historical demand and their component drivers. Self-learning algorithms adjust themselves based on real-world results to improve their accuracy over time. A popular use case is advanced demand forecasting, which is especially critical to minimize inventory levels and improve customer service. The accuracy of forecasts, which are integrated into daily processes and decision making, can be improved by 20% to 30%.

Companies are using cloud-based data lakes to extract more value from the massive amounts of data they possess. Without the need for a major IT transformation, companies can accelerate and scale up their access to data. Companies see positive returns from their investments in year one. 

Metals and mining companies participating in BCG’s Digital Operations Survey identified the following top use cases and corresponding savings or growth impact in their operations to date:



Frank Cordes is a partner and managing director in the London office of Boston Consulting Group.

Pepe Rodriguez is a partner and managing director in the firm’s New York office.

Biopharma manufacturing and supply lag significantly behind other advanced manufacturing sectors in the adoption of digital technologies, owing, in part, to high product margins and a bias to prioritize supply availability over efficiency. It is also attributable to the challenges of adopting these technologies in the highly regulated environment of good manufacturing practices (GMPs).

Digital technologies and analytics can address several key pain points in biopharma operations, including process robustness, yield optimization, cycle time reduction, end-to-end supply chain traceability, process optimization (including for manufacturing and quality), and optimization of capacity utilization. These pain points can be relieved by digital technologies such as Internet of Things (IoT) sensors, blockchain, augmented reality, and advanced analytics. Companies have an opportunity to transform the entire biopharma value chain, increasing flexibility, quality, and speed. 

In our experience, the first digital use cases deployed in biopharma operations are focused on leveraging data and advanced analytics to deliver significant value. These include:

  • A plant control tower that better orchestrates the flow of material through the manufacturing site and proactively removes bottlenecks (a 20% to 25% increase in on-time delivery)
  • Advanced analytics to identify opportunities to increase the yield of complex chemical and biological manufacturing processes (a 10% to 25% increase in yield)
  • Artificial intelligence and natural language processing for automation and enhancement of previously manual quality processes (a 20% to 40% reduction in overall cost of quality)

Additionally, among many other applications, companies have started to experiment with:

  • IoT sensors to enable real-time process monitoring
  • Increased automation of equipment, including the creation of closed systems that isolate specific processes and minimize manual intervention 
  • Blockchain to improve traceability and thereby promote compliance with regulatory requirements for product serialization
  • Augmented reality to improve the performance of standard work across operations 

To scale these experiments and deliver the full value of digital, companies will first need to address the unique aspects of the pharma environment, including the notoriously complex processes for product development, technology transfer, manufacturing, and quality. They also need to address common challenges across industries (such as clearly demonstrating value in order to obtain funding) and implement robust change management programs to ensure adoption and impact.

Efforts to develop biopharma factories of the future have been launched by individual companies as well as industry consortiums facilitated by stakeholders, such as the National Institute for Bioprocessing Research and Training in Ireland. These factories can serve as digital demonstrators and training centers for at-scale deployment of digital capabilities.

Pharmaceutical companies participating in BCG’s Digital Operations Survey identified the following top use cases and corresponding savings or growth impact in their operations to date:


Organizing for Digital Operations at Scale

To successfully implement use cases and drive rapid returns, a company needs an effective operating model, covering issues such as organization structure and functional integration. It also must support implementation with strong technical enablers, including new capabilities in data science and analytics and a scalable infrastructure. The next article in this series will explore these prerequisites for generating value from a digital operations program.

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