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This blog post introduces key concepts from the MapR Industry Guide for Manufacturing. To read more, you can download it here.
Digital transformation for manufacturers is driven by a variety of trends coming together, including: (i) IoT sensor miniaturization, which is becoming even smaller, (ii) faster, cheaper storage and compute, and (iii) easy-to-deploy machine learning toolkits.
The following are some types of manufacturers (not all types, though) that are up for digital transformation: automobile, medical device, aviation, chemical, food and beverage, and construction.
In order to fundamentally transform manufacturing, I believe five capabilities have to come together:
This requires a decision framework that starts with a data platform and stretches to the edge.
As shown in the figure below, manufacturers have already come a long way in capitalizing the aforementioned trends and deploying technologies to improve operational efficiencies.
Now, obviously, this is possible only when multiple technologies are stitched together and work seamlessly with one another. Moreover, it isn't easy to combine operational and analytical workloads and generate actionable business insights on-the-go without having access to the right data technology.
Let me provide a quick illustration, perhaps, to explain this better. Consider a manufacturer looking for a depletion monitoring solution. In that regard:
What does the plant owner need to know? Parts' supply levels, historical buying trends of that material and alternatives, cost-optimized procurement routes from suppliers, impact of price fluctuations, and optimization of inventory management costs.
What should the solution coexist with? Existing supply chain management (SCM) software, fleet management software, image or weight sensors measuring supply levels, and ERP software, at a minimum.
What would be some examples of outputs of such a solution? Predict part shortages, pick the most appropriate suppliers for that day, and give a timely heads-up to that supplier, taking into account the delivery time.
As one can tell, this seemingly simple business challenge can require multiple software and technologies to work with one another. But the "decision framework" should probably start with a data platform that can conveniently ingest varieties of data types, has real-time streaming capabilities, has flexibility to execute in-place analytics, and can really provide a system of record for the IT to go back in time in case disaster strikes.
Now, let's take a look at some specific pain points MapR has heard about from our manufacturing clients and how we solve for them…
Shown below is a high-level diagram of the MapR Data Platform, applied to the manufacturing industry vertical.
For more information on the MapR Data Platform and its applicability to the manufacturing industry, please read the MapR Industry Guide for Manufacturing.