Every journey starts with a first step, and before you know it you’re running

No-touch planning, automated generation of master data, robotic process automation. On the one hand advanced automation seems and is closer than ever.

Advances in general purpose AI (think DeepMind) or focused AI (voice assistants, photo pattern recognition) make it easy to imagine the application in supply chain planning is only a matter of time. In this light, episode 47of the ‘After On’ podcast is worthwhile the listen.

Still, we struggle on a daily basis with bad master data, erroneous or time-consuming Excel files, difficult to interpret solvers and cumbersome scenario planning. Maybe an automated supply chain is just one leap to far?

Take a step back and think about the technology we use every day. What’s the last time you’ve used physical maps to navigate? Go to the library? Remember the stock traders shouting on the exchange floor? Technology slowly advances and seamlessly intertwines with daily life. 

The same holds for applications in supply chain management. We just executed a deep dive in the level of automation we apply in analyses we execute for and in applications delivered at our customers. The degree of automation in a typical project has risen to a level we would not find say 10 years ago. 

For example, more and more steps of setting up a forecasting process are automated. Manual segmentation is not required anymore. Segmentation is automated, just like many of the steps to select the best forecast approach for each category.



Automated portfolio segmentation to differentiate forecasting approach

In our work to reduce data latency through speeding up requirements propagation, demand trend recognition and shorten planning cycles, we see ever more opportunities to automate. Take a multi node divergent supply chain with many routing choices per node, with varying lead times and yields. A detailed understanding of critical starting material requirements requires a full plan propagation, with a latency of (at least) one planning cycle. An automated ad hoc propagation using predicted lead times and material requirements – learning from past routing decisions and outcomes – delivers predicted raw material requirements with accuracy fit for planning on demand, rather than once every cycle at best.



Prediction of raw materials based on lead times and BOM simulations.

And it can work the other way around. An analysis of supplier performance giving insight of performance against planned parameter, triggered the development of an automated update and learning loop feeding supplier management, QC lab priorities, master data management and planning.



Box plot of actual delivery times versus planned delivery times

Automation based learning loop for supplier management 

Any step, no matter how small, brings the no touch supply chain a little closer. At minimum it provides answers to the questions asked. In many cases, though, it triggers efforts to significantly reduce data latency and implement true automated learning loops.

Want to learn more? Ready to take the first step and implement? Feel free to reach us through our website or read the document “Data Engineering” to familiarize with the approach EyeOn takes on making improvements in this area.

In striving for success, large companies have to continuously struggle against growing internal complexity. EyeOn helps our clients manage this complexity by designing, implementing and executing excellent planning processes as a discriminating factor for this success. In order to achieve this, we develop and share knowledge about top level planning and forecasting, with constantly demonstrable return on investment for our clients.

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