The Importance of Data Science and Business Intelligence in Supply Chain Management
Data science and business intelligence are key to managing the supply chain. These gimmicks are all around us in this environment. But the reality in companies is far from being as convincing as the introduction of technologies would have us believe.
Effective supply chain management requires the use of data science and business intelligence. Despite the widespread availability of tools like Power BI, Tableau, and Qlik, many companies struggle to create analytical reports that cover their supply chain management needs in a structured and alike way.
We sometimes see Business Intelligence reports proliferate like we saw Excel sheets proliferate – there are a lot of ideas – but where do we go to get the right information to make decisions?
To make effective decisions, it is essential to have a clearly defined and understood supply chain management model. This model must be relevant to your business and clearly communicated to your teams. It should include a defined stock sizing model, target ranges, and a clear replenishment policy.
Too much can be detrimental to the process in this case. It’s wiser to create a few clear and actionable perspectives rather than attempting to cover all areas completely.
Your supply chain design and management model must be clearly defined, understood by your teams… and relevant – so that your dashboards can assess the health of your model and prompt you to make the necessary adjustments.
So, if you don’t have a defined stock sizing model – if the definition of target ranges is unclear – if too low and too high are not understood by everyone – if the replenishment policy is unclear – your business intelligence will be useless to tell you how to improve your operations. Especially since people – internal politics, power plays, and cognitive biases in the enterprise – often get in the way of building consensus on the conclusions to be drawn from a scorecard.
You must have a shared and understood supply chain management model. The Demand Driven model preferably, of course.
The blog referenced here articulates in an interesting way the building blocks to consider What is Supply Chain Analytics? | Towards Data Science
The author breaks down the need for analytics into four categories, and gives some useful examples:
· Descriptive Analytics: What happened?
· Analytics diagnosis: What are the causes?
· Predictive Analytics: What can happen in the future?
· Prescriptive Analytics: What should we do to prepare, and what adaptations should we make?
This framework allows us to ask ourselves the right questions to structure our decision-making platform and facilitate adaptation. This is the process the Demand Driven literature describes as “DDS&OP”.
We have integrated Power BI Embedded technology into Intuiflow, making it easy for teams to work with a set of pre-formatted and tailor-made analyses. Embedded Business Intelligence – Demand Driven Technologies
It includes the four components listed above:
Descriptive analytics: how did our stock, time, and capacity buffers perform in the past period?
Diagnostic analytics: By analyzing by exception the problematic situations – out of stock, overstocks, delays – what are the probable causes? The use of the defined DDOM model and process mining feeds this analysis of causes.
Predictive analytics: how do we project our business model in the weeks and months to come? Will we have disruption and capacity risks?
Prescriptive analytics: what actions should we take: should we position adjustments on our buffers, adjust our capacities, or modify our buffers?
This embedded business intelligence platform is always enriched by user feedback, for more simplicity and more assistance and automation in adaptation recommendations. We will soon share in these columns some examples of descriptive analysis, diagnoses, predictive projections, and recommendations that result from this – stay tuned!
At our upcoming User Conferences in Atlanta (May 24-25) and Bordeaux, France (June 20-21), we’ll show you how effective supply chain management requires a shared and understood supply chain management model and the use of data science and business intelligence. By utilizing tools like Intuiflow, companies can make informed decisions and adapt to changes in the supply chain. Make sure to register for either conference (or both) so we can help you achieve agility & visibility in your supply chain.