The supply chain is evolving. Thanks to the latest tools provided by Industry 4.0—such as robotic process automation (RPA), machine learning, and internet-of-things (IoT) devices—processes along the entire supply chain are being overhauled and reengineered. At the same time, the supply chain weaknesses exposed by the COVID-19 crisis are inspiring companies to rethink their supply chains.
These next-generation supply chains will transform ways of working and workforce requirements: companies will need new skills, new roles and responsibilities, and new organizational structures if they want to build superior customer services and achieve a competitive advantage.
Against this background, the objectives of this article are twofold. First, we highlight the talent and workforce requirements for future digital supply chain organizations and their implications for new job profiles and skills. Second, we recommend a process that will help companies to begin transforming their supply chain workforce and prepare for upcoming challenges.
Digital supply chains require different talent
Many companies have seen success with their recent digital pilots and flagship projects. These initial efforts have shown that new digital tools and processes can boost supply chain visibility, increase agility, and accelerate execution. But while these proofs of concepts, digital quick wins, and pilots were successful, they have often not been scaled. Why are so many companies still facing the risk of “pilot purgatory”? We believe it is due to a lack of talent. Without having a skilled workforce that is able to effectively use and scale these advanced systems and tools, digital transformations are likely to fail.
A McKinsey Global Institute survey1 showed that 87% of global leaders think their companies are not ready to address the expected digital skills gap. McKinsey believes that about 50% of current activities theoretically could be automated by 2030, which would fundamentally change the nature of supply chain jobs. However, according to McKinsey’s internal data, approximately 45% of the global supply chain workforce has a skill set that is too traditional to meet new expectations. If a company does not have employees who can optimally use its new digital technologies, then both its supply chain performance and its overall success will be put at risk.
Companies need to put the topic of the future of work in supply chain at the top of their agendas. To do so, executives will need a more detailed understanding of how the increasing adoption of automation technology, like artificial intelligence (AI) and robotics, could affect the future workplace, roles, and skills required.
In the past, many supply chain roles and skill sets were driven by decentralized supply chain setups that consisted of siloed operations that lacked integration, relied on manual processes, and had difficulty quickly adapting to change. For example, demand planners often had to reinvent and fine tune planning processes for themselves. They had to gather information from other functions via phone and email and had to manually retrieve data from different systems to ultimately run calculations in Excel spreadsheets. As a result, the job profile might have called for “an all-round employee, creative in reconciling data from colleagues and systems to best guesstimate future demands.”
Today companies are working toward a new reality of integrated digital supply chains. Many current job profiles, however, have not been updated to reflect this new reality and are lacking important skills, such as an end-to-end supply chain mindset, cross-functional communication skills, data mastery, and analytical abilities.
Research shows that investing in these new skills can provide a competitive advantage. A McKinsey study identified those companies that were ahead of their peers in terms of creating an end-to-end supply chain strategy, handling order and demand management in an end-to-end fashion, and managing inventory across the entire supply chain. Market data shows that these more mature companies achieve superior performance across the key dimensions of service, cost, and capital (Figure 1).
[Figure 1] Impact of superior end-to-end capabilities on supply chain metrics
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The future digital supply chain organization
The next generation of supply chains will be based on advanced technologies that have the potential to transform repetitive, manual tasks into highly automated processes. However, this digital transformation will only be successful if a company has the right roles embedded in a supply chain that is organized and managed from end to end. In the supply chain of the future, we will need to have the following new organizational units supporting end-to-end supply chain management (see Figure 2):
[Figure 2] Outlook for future digital supply chain organization
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• The predictive demand management team will provide the right demand signals to the rest of the supply chain by optimizing the algorithms used by their forecasting software. The team will also handle exceptions where human input is required for creating a demand plan and forecast, such as for new product introductions (NPI) or for products at the end of life (EOL).
• End-to-end (E2E) supply planning and execution teams will maintain models for production planning and scheduling and take actions to resolve any upcoming supply exceptions.
• No-touch order management teams will maintain the automated (or “no touch”) order-to-invoice process and manage any exceptions.
• Operational logistics teams will design, operate, and improve automated (or semi-automated) warehouses. They will also manage and execute various operational tasks (such as implementing new procedures, conducting training, maintaining documentation, and reviewing work schedules). These teams will include a broad set of roles, such as logistics managers and logistics service provider (LSP) managers, to handle external providers.
• Advanced network configuration teams will handle more strategic tasks related to network design such as deciding on the production footprints for new products, optimizing the network for tariff and exchange rate fluctuations, and conducting regular supply chain risk reviews.
• The data mastery unit will provide dedicated resources to support data analytics as well as master data management. The unit’s focus will be on ensuring a high level of data availability and quality—both of which are prerequisites for a digitally enabled supply chain.
As Figure 2 shows, these organizational units should include a range of roles: both tech-focused positions that combine AI and data knowledge as well as jobs with a strong interpersonal and management focus.
Let’s take a closer look at each of these units.
Predictive demand management
Predictive analytics and other new technologies are transforming demand management and making it possible to automate more of the demand planning process, leading to an even better outcome in terms of accuracy. In the demand management unit of the future, roles that possess strong market knowledge will work with roles that have the latest data science expertise to generate demand forecasts. (See Figure 3.)
[Figure 3] Predictive demand management roles of the future
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In this new setup, the market expert would serve as the link to the commercial organization. People in this position would need to understand both worlds—commercial and supply chain. They would be responsible for gathering all the market- and customer-related input—for example, information about promotions, product launches, and new points of sale. This information would then be integrated into the forecast. These market experts will need to be able to apply market intelligence to the demand signals that they are seeing and understand competitor dynamics.
The demand planning data scientist would then combine the internal and external data inputs and use predictive analytics to form a high-quality basis for future demand. People in this role will need: statistical expertise, experience in coding and in querying databases, knowledge of common programming languages, and an understanding of machine learning techniques. They will also need an understanding of demand planning and a strong continuous improvement mindset.
A third role, the demand manager, would be needed to define how demand is segmented and facilitate demand consensus discussions in the sales and operations planning/integrated business planning (S&OP/IBP) process. Demand managers would also handle exceptions where human input/validation is required (for example, for new product introductions or for a product at the end of its life). The necessary skills for this role would include commercial and supply chain knowledge and strong cross-functional problem-solving skills (for example, within the integrated business planning process). Demand managers will also need good interpersonal, communication, and performance management skills (for example, measuring key performance indicators such as forecast accuracy/bias). Finally, the demand manager must have a good understanding of the AI system that provides the forecasts so that they can understand where and when overrides and interventions would be valuable.
This new setup enables the demand planning for the majority of stock-keeping units (SKUs) to be automated using algorithms based on basic and advanced analytics. Only a fraction of the remaining SKUs (5% to 30%, depending on the industry) would need to be managed manually by a demand manager in close collaboration with a market expert. For automatically planned SKUs, manual interventions would still be possible, but the performance of these manual interventions would need to be tracked to avoid negative effects. Recent modeling approaches have even gone so far as to include the manual input for selected SKUs as an additional feed-in to the forecasting model, making it just one more influencing factor for the AI system among many (with the possibility to fully consider this information or—at the other extreme—to ignore it). This way, the demand forecast accuracy, bias, and forecast-value-add of each involved planner are viewed as neutral inputs into the overall forecasting engine.2
End-to-end supply planning and execution
Increased analytical maturity and emerging technologies—such as digital twins—are also revolutionizing supply planning and the role of the supply planner. Because of these automated solutions, the planning cycle can be shortened and companies can react more flexibly to changing demand and/or supply situations. And, supply planning will become more integrated around the end-to-end supply chain, rather than the current sequential approaches for individual stages.
Due to increased automation of the supply planning process and a shift towards more predictive planning (considering the probabilities of different events and outcomes), end-to-end supply planners will be mainly responsible for evaluating the supply scenarios created by the automated systems. Further, they will be responsible for managing exceptions, such as deciding on priorities if no feasible supply chain plan can be created, allocating inventories if customers report short supply, or identifying alternatives if external suppliers fail to provide promised volumes. They would also facilitate supply discussions in sales and operations execution (S&OE) and S&OP/IBP meetings and make trade-off decisions (such as whether to leverage new contract manufacturers, use strategic safety stocks, or decline customer requests) in close collaboration with other cross-functional stakeholders. Hence, future supply planners should have experience working with advanced planning/digital-twin enabled supply planning processes. They should also have a strong exception management, communication, and problem-solving skills.
Companies will also need to have employees who can implement and maintain advanced planning technologies, such as digital twins for production scheduling and planning. This need will create even more new roles, such as the digital twin engineer. This person would be responsible for implementing and maintaining the digital twin, continuously improving the optimization logic of the planning system, and making sure that the data used by the systems are up-to-date. People filling these types of positions would need a continuous improvement mindset, basic supply chain knowledge, and a deep understanding of advanced planning systems. They should also be familiar with optimization algorithms, machine learning, coding, and statistical computer languages.
No-touch order management
Another great example of the shift occurring in processes, technology, and capabilities is order management. In the past, order management was characterized by transactional and repetitive tasks that required significant manual effort (or “touches”) from order capture to order invoicing. Now, the new normal is “no-touch” order management, which uses technologies such as customer self-service ordering, electronic data interchange (EDI), and RPA. To enable this more automated form of order management, companies will need to have two key roles: the automation architect and the order manager.
The automation architect implements and maintains process automation solutions across the order management landscape. The person filling this role should be familiar with state-of-the-art process automation applications, such as smart optical character recognition (OCR), and leading RPA technologies, such as UiPath, and Blue Prism. They should also have experience with coding statistical computer languages (such as R, Python, and SQL); process analysis, design, and implementation; process mining; and agile development. Finally, it’s important that they have a continuous improvement mindset.
The order manager provides the link to key customers. This person will interact with customers based on real-time alerts and manage any exceptions to the normal order process. Even, in the most advanced organizations, order managers will need to work with customers to solve issues caused by inaccurately reported inventory levels, missed production deadlines, or lost or delayed deliveries. People best suited for this role will have strong problem-solving skills as well as strong communication and interpersonal skills. They should also have experience working with enterprise resource planning (ERP) systems (such as SAP or Oracle), be customer-service oriented, and have both commercial and supply chain knowledge.
Advanced network configuration
The recent COVID crisis has demonstrated the connection between a company’s physical footprint and its supply chain resiliency. Now more than ever, companies are cognizant of how integral their network configuration is to their business strategy. As a result, many companies are rethinking how they plan and optimize their supply chain network to increase supply chain robustness and meet overall future business requirements. For example, some companies are exploring whether they need to diversify their external supply base, while others are looking at localizing or regionalizing their manufacturing networks. In the future, strategic network design will only become more complex as companies seek to incorporate and balance factors such as their carbon footprint, tariffs and taxes, and lead times to customers.
As strategic network design grows in importance, it has become clear that an agile and resilient supply chain requires having dedicated network planning capabilities and roles. For example, companies will want to have a strategic network planner who can define, run, and evaluate complex trade-offs among supply chain components for different footprint scenarios. This person should also have experience with the network planning tools commonly used to run the simulations.
One area where we have already seen new roles established is in the operational logistics department. Over the last decade, companies have created roles such as LSP manager and customs and trade manager. These roles will be still very relevant in the future. Companies will continue to see an increasing need to integrate third-party providers, leverage modern integration technologies and platforms, and operate in a global trade environment that is constantly shifting, with evolving trade compliance requirements.
As companies implement AI and autonomous logistics technology (such as, self-driving vehicles, warehouse robots, automated storage systems, and smart picking technologies), they will need to create even more new operational logistics roles and positions. For example, logistics automation engineers will be needed to drive digitization. These employees will implement transportation management systems (TMS) and control towers and design and operate (semi-) automated logistics and warehouse processes. On top of warehouse and logistics expertise, they will need a decent knowledge of state-of-the-art warehouse automation technologies and transportation IT solutions. Design experience and an improvement mindset will also be required.
As more activities and processes get automated, a dedicated supply chain data mastery team will become a key enabler of the future supply chain. This unit will be responsible for structuring, managing, and cleaning all the underlying data that feeds the different visualization and application layers across the value stream.
This team will consist of several different roles. The data engineer will design and align all internal and external data exchanges and establish procedures for managing data gaps and errors in order to ensure data availability and quality. These employees will need to have a particular focus on automated data verification and continuous data cleaning. Meanwhile, a data visualization engineer will be needed to develop new data visualizations, dashboards, reports, and customized user interfaces to enable complex data to be understood by all stakeholders, such as planners and sales teams. Data visualization engineers will need to be able to present this feedback in an appropriate and understandable way, at the right level of aggregation and frequency. They will also need to create visual explanations that illustrate underlying relationships—for example, the important factors that affect a particular forecast or sale.
An advanced skill set
As the descriptions of these new and developing roles show, we strongly believe that the supply chain talent of the future will need a much more advanced set of skills than what is required today. Thus, the supply chain will need to attract different personalities, with a range of educational backgrounds and experiences, to create a more diverse workforce in the future. While we expect to see fully dedicated digital roles, not all supply chain employees have to become full technophiles. General supply chain employees should have a digital mindset and know how to apply state-of-the-art technologies and solutions, such as AI or machine learning, in a digital supply chain. However, there is no need for them to know all the technical details involved in the algorithms or models they use. A good analogy might be a taxi driver, who needs to know how to steer in different road conditions (including rain and heavy snow) and read the dashboard but doesn’t necessarily need to know everything about the car’s powertrain.
In general, supply chain employees will need to be comfortable with data and have a data analytics mentality rather than solely relying on the experience and knowledge they have acquired over the years. This mentality will enable them to use big data, machine learning techniques, and automated root-cause analysis to make data-driven decisions.
Yet at the same time, companies will still need to have supply chain employees with deep functional expertise and understanding. Experienced leaders will be needed to set the right direction for the supply chain of the future and continuously shape and improve it. There will still be a need for executives that possess the right innovative mindset and have expertise that spans across industries.
Building a high-performance workforce
If we only change job titles to more “digital sounding” names, such as “digital twin engineer,” then our digital transformation will be condemned to fail. This error was often made in the early 2000s, when logistics functions were frequently rebranded as “supply chain management” without actually changing the roles and responsibilities. To avoid this mistake, supply chain organizations must define the digital skills, capabilities, and roles that they will need in a very intentional way.
When companies are redesigning their supply chain roles and organizational structures, it can be helpful to follow the process shown in Figure 4, which consists of three phases: assess, architect, and act. The assess phase starts with the translation of the company strategy into a supply chain vision, which includes the processes and technology to be used. Based on this vision, the company should define the organizational structure needed to drive this future state. Part of this process is identifying the number of fulltime employees needed to efficiently run the future-state supply chain. At this point, the redesign team should identify what drives this decision. For example, the number of stock-keeping units (SKUs) per demand manager may help you decide how many demand managers you need.
[Figure 4] The three phases of creating new supply chain skills
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In parallel, the team should assess the capability levels of the current supply chain organization so that it has a clear baseline to work from. It can also be helpful to assess which skills key competitors possess. This benchmarking exercise can be based on web research and can indicate whether your competitors possess skills that your company doesn’t and/or if they are looking for the same talent and skills.
As a last step, the skills gap between the current and future organization is defined. The outcome of this gap assessment provides a holistic list of defined skills both for existing roles and for new ones within that future-state organization. It also creates transparency on what skills are already established and which skill profiles the current workforce is lacking. This will be the crucial starting point for creating a concrete action plan to close any gaps.
In the architect phase, you create a portfolio of initiatives that aims to close the skills gap by either building up or selectively reducing the current workforce (see Figure 5). Core initiatives include: borrowing talent internally (taking over spare capacity from other functions), building up needed skills (upskilling), acquiring new talent (recruiting), renting part of the workforce (contracting), redeploying talent (renting workers out), and releasing talent (divesting). Due to the scarcity of available talent in the market for selected roles (such as qualified data scientists) capability building and upskilling are key levers to building the workforce of tomorrow.
[Figure 5] Example of a workforce evolution portfolio
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One way to upskill your employees is to create dedicated programs to build up an array of foundational supply chain management knowledge and/or advanced functional expertise. For example, some smart companies have set up comprehensive data science programs via in-house training modules conducted over five-to-eight months or through intensive boot camps. Other companies have leveraged content from MOOCs (massive open online courses) like Coursera to avoid making huge investments in developing their own training materials. All these programs allow them to develop their own people with the core skills required for data science, such as modeling, machine learning techniques, or programming skills with Python. This not only saves money but also reduces the time and effort spent hiring candidates with the required skill.
In addition to creating the portfolio of initiatives, companies should institutionalize a process for projecting future talent needs and employee requirements and for monitoring developments in the job market. Top digital pioneers are even using predictive modeling based on machine learning techniques to predict employee attrition, number of new hires, and mobility for different employee profiles based on internal and external data feeds. These results are then used to build organization-level forecasts by business unit, site, or job family. The architect phase should also establish a change management roadmap and communications plan.
The act phase is fully focused on making the identified initiatives operational and creating a roadmap and governance model to start rapidly building the target talent capabilities. During this phase, initiatives are prioritized and the sequence of change is laid out. The resulting implementation and scale-up plans should include a detailed timeline to realize the prioritized initiatives. The last step is assigning team members to the initiatives, creating a governance model, and identifying how the success of the transformation effort will be tracked and measured.
Following this approach will help companies with their first steps toward closing the skill gap as they scale up their digital supply chain. Starting to act today will empower organizations to be readily equipped and reap the benefits of tomorrow.
Seize the opportunity
The COVID-19 crisis has put supply chain planning at the top of C-suite executive agendas around the globe, making them realize that it is so much more than just moving boxes from point A to B. Supply chain is now front and center, as companies realize that successfully managing supply chain risk and creating supply chain resilience provides massive benefits in terms of customer service and cost to serve. However, firms need the right talent to make all of this work and just hiring outside talent is not easy if you are not Amazon. Therefore, the time is now to reimagine the talent needed to make this possible.
1. James Manyika, Susan Lund, Michael Chui, Jacques Bughin, Jonathan Woetzel, Parul Batra, Ryan Ko, and Saurabh Sanghvi, “Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages,” McKinsey Global Institute, 2017: https://www.mckinsey.com/featured-insights/future-of-work/jobs-lost-jobs-gained-what-the-future-of-work-will-mean-for-jobs-skills-and-wages
2. For more information on how the planner role may be transformed in the future see Knut Alicke, Kai Hoberg, and Jürgen Rachor, “The supply chain planner of the future,” Supply Chain Management Review, May/June 2019.
Knut Alicke (email@example.com) is a partner at McKinsey & Company in its Supply Chain Management practice.
Kai Hoberg (firstname.lastname@example.org) is a professor of supply chain and operations strategy at Kuehne Logistics University.
Julian Fischer (email@example.com) is an expert Associate Partner at McKinsey & Company.
Sophia Welter (firstname.lastname@example.org) is a Specialist at McKinsey & Company.