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How Smart Analytics Re-define Resource Allocation Management

An Interview with Arne Beckhaus

February 8, 2022
Data literacy
Stacked TrianglesPanel BG

Dr. Arne Beckhaus is Head of Data Services at Continental Engineering Services. In this role, which he’s had since Jan 2020, he’s responsible for guiding customers on their data journey, mostly based on Continental’s vast experience in manufacturing data analytics and establishing a data culture. Arne has had various roles at Continental since 2011, and prior to this, was at SAP. He’s passionate about digital transformation and empowering non-IT specialists in all business functions to work with state-of-the-art data tools. With this award winning, hands-on approach, users can automate their own processes every day and make better decisions based on facts.

We sat down (virtually) with Arne to ask him a few questions on Resource Allocation Management, the significance it has in manufacturing, the challenges facing Allocation Managers, and what role data science is playing in this field.

Arne, what inspired or motivated you to get into the manufacturing industry? What excites you the most about it?

Ten years ago, I switched from Germany’s largest software company to manufacturing. I wanted to see IT in action, steering value chains of physical goods.

What have been some of the biggest changes you’ve seen in manufacturing?

A decade ago, manufacturing was overly cost driven. It reminded me of the early days of the IT industry: Hardware sales were the goal and software was developed as a free add-on to sell more pieces. While the IT industry’s value proposition quickly changed towards software as the innovation and business driver, manufacturing is also slowly heading down the same road. We are now talking about Ethernet and high-performance computers in cars. Abstraction is king.

Another change is the use of data. Manufacturing and logistics processes generate tons of data. Decision makers nowadays see the value in this data. And we’re making progress in both areas: Lighthouse projects, involving artificial intelligence, for example for optical quality inspection. But also, data literacy programs designed to empower thousands of business users to automate their previously Excel-heavy data processing tasks.

There seems to be very little out there on the topic of Resource Allocation Management. What is Resource Allocation Management exactly?

Standard IT processes are designed for times of sufficient supply. From customer demand planning, raw material demand is derived via the bill-of-material (BOM). The next steps are production planning and shipment in order to fulfil customer demand. However, in times of global shock such as today’s semiconductor shortage, it’s impossible to fulfill this demand.

Hence, smart data analytics solutions are needed to fill the gap in Enterprise Resource Planning (ERP). They need to decide: Will we run into an effective shortage in this part number, or can we survive by sending small but marginally sufficient quantities to the respective plants? If a shortage is unavoidable, Resource Allocation Management needs to decide which facility, and ultimately which customer, receives what quantity of the short raw material.

Going back to the semiconductor shortage, this has considerable knock-on effects for the rest of the industry. Demand for goods requiring chips is surging and supply is not catching up with this growth. The most significant effect for manufacturers is the inability to produce goods in an unprecedented scale, which is a paradigm shift for planning processes and customer behavior at the same time.

What significance does Resource Allocation Management have in manufacturing?

Resource Allocation Management plays a crucial role in business continuity. When ERP systems can’t deliver the required answers, Allocation Managers and data experts need quick and holistic data integration to make the right decisions. This can also be important from a legal perspective to fulfil contractual requirements in a fair and transparent way.

What are the typical roles in this domain?

Historically, an Allocation Manager has been the one to undertake all Resource Allocation Management tasks. However, using the semiconductor example where the scale of the crisis is so large that handling the flood of information is impossible (even on a semi-automatic basis), we are seeing a rise in the demand and importance of data experts. These data experts are fulfilling a knowledge and skill gap - specifically in supply/demand matching and integration of various data sources for bulk data.

What are the main Resource Allocation Management challenges manufacturers face?

The first challenge is the upside-down planning philosophy: From the usual top-down planning from customer demand to raw material orders, Allocation Managers now need bottom-up planning to allocate scarce resources to production plants and customer orders. The data sources required for this task are manifold: Stock levels, bill-of-materials, vendor orders, supplier capacity, customer demand planning, shipments, etc.

From our experience, another key challenge in this context is to correctly work with bill-of-material (BOM) data. A BOM explosion from product to component level is often incredibly complex and contains pitfalls specific to the organization. These range from multi-sourced components to design alternatives, through to material number changes. With every previous manufacturing data analytics project, we learn more about the challenges in BOM explosion – which is very beneficial in dealing with the complexities of Allocation Management.

What role does data science and data analytics play in overcoming these challenges?

Data analytics has overcome the phase of lighthouse projects or mere PowerPoint presentations as results. Built on good technology stacks, data analytics solutions can fill the shortcomings of ERP systems – such as the effort required for implementing customized business processes and extending solutions easily with a low-code programming environment. The result: agility, quick delivery, a sound business understanding, and being able to change even the fundament of solutions repeatedly to match evolving management requirements and decisions.

You’re a strong advocate for self-service analytics, empowering the individual user, and moving away from tools such as Excel. Why do you believe this is so important for organizations?

Our recent experience in Resource Allocation Management is the best example of this. A central team of data experts developed a sophisticated planning solution for allocating scarce goods. Hundreds of users in functions such as controlling and sales are consumers of this data, and they needed to create specific views for their customers or management reports. We benefited highly from the fact that the respective organization started educating users in all business functions in KNIME (accompanied with Power BI for dashboarding in this scenario) years ago.

No IT standard software and no central data product for resource allocation management can fill every individual’s information need. In this case, we published our allocation calculations both as dashboard and as data APIs following an easy-to-understand data model.

There are dozens of users that post-process these results with KNIME to meet their needs. This demonstrates, in an impressive way, that there is a huge value in self-service analytics. It saves time through automating repetitive tasks and enables better decision making in all parts of the organization. Data literacy is the future core competency!


Continental Engineering Services is also a KNIME Trusted Partner offering consulting, training, project, and software services. It makes Continental's knowledge on establishing a data culture by educating and guiding business users in usage of KNIME Analytics Platform available. Learn more and contact Data Services at Continental Engineering Services.