25 Oct Valuable data science in logistics (Interview)
The field of data analytics and science in logistics has been growing rapidly the last couple of years. Also, the logistical segment is making its first steps in using data to find valuable information and calculate quality predictions. Argusi, specialists in supply chain optimization, is helping organizations to search for possible improvements in their supply chain on a strategic and tactical level relying heavily on data science and analytics. Martijn Cornelissen from Wittics (data science talent academy and selection) interviews Menno Rustenburg from Argusi, to learn more about Argusi and how they support their clients in building their capability on data science.
October 2019, Menno Rustenburg on Valuable data science – interview by Martijn Cornelissen, Wittics.
Menno, before we start, can you tell a little bit about yourself and your role at Argusi?
“Yes, of course. I am a senior supply chain analyst at Argusi for almost three years now. In this role I work with several clients, like Heineken and FrieslandCampina, to find solutions on how to improve their supply chain performance based on several analytical and optimization techniques. In this process I focus on increasing the analytical capability of our customers. We believe that effective adaptation of analytics plays an important role in making supply chain improvements on a continuous basis. That is also why we started the Argusi Academy a few years ago, to share knowledge and train our clients to do these analyses themselves.”
Could you give a glimpse of how Argusi typically executes supply chain optimization projects?
We have a three-step approach:
It always starts with a good question
“We distinguish ourselves with our capability to thoroughly analyze and understand the business requirement of our customers. We like to say: It always starts with a good question. To generically optimize the supply chain would not directly result in value for the client. Normally, the client has some questions or targets on its supply chain that are interesting to have a closer look at. Topics can be certain demand patterns, (re)location choices or production related topics.
One of our biggest strengths lies within the translation of these topics into valuable scenario’s and mathematical algorithms. All our people are both talented algorithm builders, as well as skilled analysts. Combined with their enthusiasm and experience to find the best solutions, we make sure to answer the right questions for a client.
This is also why we like to work with companies like Wittics. They make sure that young data scientists and modelers are trained to ask the right questions, getting to the core of the challenge and consequently giving insights that matter. Doing so, they enable organizations to strengthen the in-house knowledge that is needed to process and implement the data of analytics projects. These talented young professionals are also the perfect contact point during the implementation of the new supply chain strategy. We ultimately share the same goal; improving the supply chain.
In the second phase we provide and calculate these scenario’s using mathematical optimization. Lastly, in the adoptation phase we educate our clients to perform their own supply chain optimizations. Via workshops (e.g. serious gaming), masterclasses, round table sessions and if required interim projects, we increase the capability of organizations so that they can truly embed supply chain analytics in their main business processes. This allows for sustainable and continuous improvement cycles and smoothens the process from the project phase to the support phase.”
What’s your view on data science in the logistical segment?
“I think data science is highly valuable to support supply chain decisions. Changes in strategy, new technology, developing new markets, competition and unexpected events. Supply chains can be under a lot of pressure. These changes drive the need for change. For network solutions, footprint studies, production allocation, inventory and transportation problems. For us it is all about data driven decision making. The logistical segment is subject to increasingly frequent and sudden changes. Both from supply and demand perspective.
Data driven decision making can help making quality decisions faster, making supply chains more flexible towards these new dynamics. We are at the beginning of the ‘Data Era’ and are, together with our clients, taking steps towards decision making with data.”
Sounds interesting, can you give an example of this?
“Argusi participates at the DALI program (Data-science for Logistical Innovation). In this program we are building our online platform called ‘Spanning Tree’. Many small to medium sized companies don’t have the resources to perform comprehensive tailor-made supply chain analytics. With this new platform, we offer standardized supply chain analytics that can be used to answer all kinds of supply chain business questions.Based on the data of the organization we deploy a visualized model of their supply chain to perform different analytics on. Visualizing the supply chain can already result in valuable insights. With certain ‘what-if’ scenarios organizations can analyze effects of changes in demand and/or supply.”
Supply chain analytics: a step-by-step approach
One of the frequently mentioned data science techniques is machine learning. What is your view on this?
“Machine learning is mostly used to make quality predictions. Quality predications lead to quality decisions, so it is an important technique in the journey towards data driven decisions making.
An example of a machine learning model we use, is helping with the questions on how to optimally load a truck. You can imagine that it is not always a known fact how much space a certain order takes in a truck. With machine learning we can predict how many pallet places a certain order will take, based on order data. This allows clients to start a loading plan directly from order, even if they do not have data on physical sizes yet. Having qualitative loading plans in advance (forecasting) helps the locations with their day-to-day operations.”
Thank you for these insights, Menno. It seems we live in exciting times now data science and supply chain optimization are coming together.
“Indeed, it is great to see companies adapting supply chain analytics and data science. They experience the advantages and embed it into their organization. Their own data reflects and predicts supply chain events, allowing them to act quickly and have less firefighting on the day of operation.”
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