Advances in supply chain 4.0 technologies have recently triggered thoughts and visions of managers across the globe towards driving automation to the next level. For example, Richard Liu, founder of Chinese retail giant, expressed the hope for full automation in his company with “no human beings anymore, 100% operated by AI and robots.” This is what we call a true no-touch supply chain. For physical flows, technologies that enable dramatic automation are currently widely piloted: Think lights-out factories, picking robots and AGVs, self-driving trucks or drone delivery. Similarly, ordering processes also advance quickly and move away from fax or phone to internet, EDI, personal assistants and IoT with automation degrees of over 90% possible even in B2B environments. Automation benefits are obvious: 24/7 operations, lead time reduction, higher process consistence, instant optimization and human error reduction. Accordingly, the outlooks for many simple blue and white collar jobs are rather dull.

When it comes to automation of information flows and decision making, we see many interesting technologies on the rise: cloud platforms, robotic processes automation (RPA), negotiation bots and artificial intelligence. Automation will affect jobs that have so far been deemed relatively safe. Planners (entailing demand planning, inventory planning, supply planning, production planning and order management are typically the most frequent white-collar job groups in many supply chain functions, often making up a third of SCM admin roles. Automation of planning is still in its infancy in many companies that use tools from the 1990s and deal with master data problems from the 2000s. Here, planning is very manual, time-consuming and changes are cumbersome. However, some companies are already far ahead and experience how the role of planners will be affected by new technologies. So, what will change for planners – will they become obsolete? Here are some of the ways the profession may change.

Planning tasks will shift

Planning has always been challenging as planners fill the gaps in broken business processes hindered by legacy IT systems, limited system integration and poor (master) data maintenance. Further, planners have often been overwhelmed by the number of decisions they need to make with the increasing number of products, higher product complexity, frequent changes and lack of sufficient buffers, very often having to go through a long list of products one-by-one to decide on quantities and timing. While many SC planning systems automatically generate demand forecasts and production/purchase order recommendations, these numbers are frequently not trusted and manually checked or even overruled. Planners often rely on gut feeling and the experience gathered over the years.

New technologies and solutions

RPA and predictive analytics promise to avoid the need for much manual routine work, which comes along with a task shift for planners. Instead of manual data inspection and constant fire-fighting to plan and schedule the products manually, planners will rather develop and maintain solutions that automatically check the data, identify issues and ensure a high-quality planning basis. Similarly, AI forecasting approaches are able to more accurately forecast standard demand and allow planners to focus on fewer items that really do require attention. Smart algorithms detect items where human input is required for better decision making, especially if historic demand is not a good indicator of future sales, but rather information on market developments and pending offers need to be integrated manually. The advanced planning systems point the planners towards those exceptions and focus their efforts on where they are really adding value.

New roles will be created

Currently many planning roles are structured along the planning steps of demand planning, supply planning, production planning or scheduling. In the future, planning roles will evolve from a domain-focus to be focused around tasks that require specific capabilities (and can be used across all planning). In particular, planners will specialize in data management, algorithmic optimization, exception management and partnering with the business, creating roles around the following tasks:

Data management

The right data is the basis for any planning automation, and entails both data availability and data quality. This will require dedicated data engineers who need to set up procedures and RPA protocols to automatically check for data gaps and consistency, comparing master data with actuals to identify and correct for deviations. Another crucial task is to design and align data exchange with supply chain partners. For example, Amazon uses innovative EDI systems and APIs for connecting to vendor systems and educates the vendors on how to best use these interfaces to ensure seamless connectivity and high data quality. To enable drawing the right conclusions from the data, data visualization is another core task within data management, developing customized user experiences.

Algorithmic optimization

As SC planning will be widely automated, the key differentiator will be the optimization quality of the algorithms. To build out this advantage, there is a need to continuously optimize analytics approaches using approaches around advanced modelling, machine learning, and stochastic optimization. Planners will need to combine cutting-edge research with pragmatic incorporation of learnings into the planning algorithms – similar to what leading e-commerce players already do today. For example, Zalando combines algorithms developed and described in academia and adapts these in a pragmatic trial-and-error approach for usage in their warehouses. The quality and performance of the algorithms are then tested in limited pilots within the live system.

Exception management

While algorithms and software robots will take over many routine activities, exceptions will still occur and conflicts need to be resolved. To do so, exceptions managers need to evaluate scenarios and options, and take trade-off decisions together with internal and external partners. These tasks require good communication skills to align the needs of all parties involved. The role of the exception manager is a good example of the “automation paradox” that describes how the automation of simple tasks creates new issues as employees now constantly deal with difficult cases.

SCM business partner management

The shift towards more capabilities-centered roles requires a translator-role within SCM to act as counterpart to the Business and Operations, to translate their requirements and reality into the planning process.  This “SCM Business Partner” will play a pivotal role in shaping the design of the SC planning processes and systems. Also, the SCM Business Partner will be driving and optimizing the S&OP/IBP process, to facilitate between the functions and find an overarching business optimum.

New organizational setup will be adopted. The next level of automation and the new roles in SC planning do not aim to reduce the cost for SC planners – but the new setup will cut lead times, minimize errors, optimize margins and provide a real competitive advantage. To make this work, however, it will require a new organizational setup for planning. As a large share of routine planning activities are automated, the physical proximity to operations such as locating production planners in the plants, will be less important while a knowledge exchange and best practice sharing among planners becomes even more crucial. Thus, planners will require a co-location to enable the continuous interaction and improvement.

We might therefore see a resurrection of shared service centers for SC planning – this time, however, to move closer to the right talent and to enable close collaboration, rather than to save personnel cost. While the number of planning FTEs might go down, the total cost will most likely not change dramatically, as the change of roles comes with increasing capability requirements and therefore higher salaries. The need for highly qualified talent is continuously increasing and even today already, there is a tendency to outsource such jobs into “shared service centers” with access to the right talents rather than into low cost countries. The prime decision criteria will be how to best access the top talents in data engineering and algorithmic optimization.

Future planning will not make planners obsolete, but the “Planner 4.0” will not be comparable anymore with many of today’s planners. Planners need to become much more analytical and IT-savvy, requiring significantly enhanced capabilities, and will be a core differentiator of supply chain performance.

Already, companies can prepare for future requirements in establishing the data engineering and optimization roles for creating batch jobs doing “automatic” master data checks and creating suggestions for adaptation. While doing so, it is essential that companies codify the extensive experience/knowledge of their planners and ensure that future systems can use it rather than having to re-discover core aspects of the operational reality. On top, skills in SC planning and advanced analytics need to be urgently built to not lose ground in the context of a fast evolving SC planning function.

Knut Alicke is a partner in McKinsey’s Stuttgart office and leader of the Supply Chain Management Practice in Europe. He can be reached at .(JavaScript must be enabled to view this email address).

Kai Hoberg is a professor of supply chain and operations strategy at the Kühne Logistics University in Hamburg. He can be reached at .(JavaScript must be enabled to view this email address).

Juergen Rachor is a senior expert in McKinsey’s Supply Chain Management Practice. He can be reached at .(JavaScript must be enabled to view this email address).

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