IIoT, Small Data and PLM

Big Data and Internet of things is big (sorry about the pun). There is a lot of promise of golden opportunities and discussions on how to get to smart connected products and opening up new business opportunities. And a lot of anxiety among many smaller companies wondering how to approach this. A pragmatic approach and easier start can be small data within industrial IoT (IIoT).

Big Data and full blown IoT can be too intimidating and a too big step for smaller companies. Yet it is possible for them to enter the race without too high investment.

I am assuming that a PLM backbone with all product data is in place. PLM in context of IoT has been mentioned by for instance Beyond PLM and the Virtual Dutchman. There is also an excellent paper from HarvardBusiness School.

The companies that will have the easiest path here is the business to business companies that delivers physical products that are used by another company in a larger system of products. E.g. a conveyor belt that goes into a larger material transportation system. You have control of the conveyor belt while your customer manages the whole transportation system. If your conveyor belt can be a smart connected product we have IIoT.

A large portion of Big Data is unstructured information that you do not know clearly what you will use it for when you design your product. Small data is instead logical additional data sets that you define up front, you know how it is related to other information and you know what the purpose is.

“Small data connects people with timely, meaningful insights (derived from big data and/or “local” sources), organized and packaged – often visually – to be accessible, understandable, and actionable for everyday tasks”

Making a “dumb” product into a slightly smart connected product can give you very powerful information. Define up front what (small) data you want and how to manage it. You can limit the IT infrastructure investment since the amount of data is limited and as a manufacturing company you probably have an IT infrastructure that can manage the additional data. And it is quite easy to know how to relate the information to other information sources. As the small data can be well defined and structured it can easily be combined with your existing well defined and structured data. The typical information sources will be CRM, ERP and PLM. You can combine your new (small) data with your existing product data sources. This can give you new insights.

Aspects that makes IIoT and Small Data attractive

• Sensors are smaller, cheaper and more flexible than just a few years back
• As the data amount is limited it is manageable
• You have a strong product information backbone in place in PLM and/or ERP
• The small data and how it must be related to other enterprise data to give meaning is defined up front. You don’t need advanced analytics tools or experts.

Taking the conveyor belt as an example. Take your existing product and how it is delivered and operated and serviced. Think about: What business benefit do you want if you could get anything from your product in use at your customer? Is it possible to get that data somehow? Then decide what you want to achieve, what data you need and how to get it.

What do you want to achieve?

• Improved service margin – you can do better and more accurate service since you have better insights. Perhaps selling more spare parts.
• You can provide improved operational efficiency for the customer as you can optimize the usage based on the data feedback.
• You can sell a service instead of a product. E.g. you promise a certain output in a certain time period. And you make it happen.
• You can improve the customer experience with new automated functions, remote control or better interaction with other equipment.

Decide up front and design your product accordingly and extend your IT infrastructure to support this. It is still not done by itself, but taking small steps instead of diving into Big Data at once is perhaps more appealing.

Engineering.com has an interesting article about using PLM and IoT in an old industry to create new business opportunities.

Summary

The key is understanding your product, how it is used by your customer and what additional data that could give you an advantage. You have a lot of valuable data already and it can become much more valuable if you add some smart small data on top of it. Use the data and the IT infrastructure you have, add some sensors and connectors and get started.

Tore Brathaug

www.infuseit.com

PLM and PIM – what’s the difference?

Product Information Management (PIM) and Product Lifecycle Management (PLM) must surely be something similar. Yes and no. There are similarities, overlaps and they can complement each other. At the same time there are clear distinctions and you will probably need both.

Similarities and differences

From Wikipedia:

“PIM refers to processes and technologies focused on centrally managing information about products, with a focus on the data required to market and sell the products through one or more distribution channels”

“PLM is the process of managing the entire lifecycle of a product from inception, through engineering design and manufacture, to service and disposal of manufactured products”

Interestingly they say similar things. At least the product information is in focus. It can also seem from this definition that PIM covers a hole in the PLM processes. PLM jumps from Manufacture to Service. PIM covers what is in between – Sales and Marketing. This should be a perfect match as has been discussed by Tech-Clarity.

ERP is also highly relevant in this context. In some industries also CRM. The picture below explains some of the differences between PLM, ERP and PIM.

Positioning of PIM

When we look at the purpose of PIM we understand the difference better. PIM is focused on providing accurate and good quality sales and marketing information to various sales and marketing channels. Today this is often done manually or semi-automatic for each different channel. The objective is to gather all relevant sales and marketing material in one place and publish it in a controlled and efficient manner.

PLM/ERP feeds PIM

The best approach is to take product information that is controlled and released somewhere else (PLM or ERP typically) and enrich the products with additional information (such as images, video etc) and publish to different channels and markets. Which channels to publish to is controlled in PIM. Which markets to publish to might come from PLM or ERP or can be defined in PIM. The best flow is when PIM can trust that the products and their information is valid at the point it enters PIM and PIM can focus on enrichment and publishing. One reason for this is that PIM is typically poor on revisioning and approval control.

Change and status control in PIM is typically done by using catalogues to show status and control what you can do with the data. An example is shown above. Someone or something pushes the data over to another catalogue. In that catalogue it is defined what you can and cannot do on a general level. The access is not product by product, but more often market by market. PIM works typically on the latest released product information.

Can PIM replace PLM or vice versa?

I have heard PLM people saying that as PLM has most of the product information already; why not extend the data model and processes to also cover PIM? PLM is quite flexible and can be integrated for example to a web portal for product catalogues. Why not?

PLM and PIM are both focused on product information. At the same time they have very different strengths and capabilities. It is better to utilize those differences than trying to build missing functionality in PLM or PIM.

PLM is not good at the marketing side of product information. Like creating print material or publishing to various sales and marketing channels. PLM is focused on detailed control needed by engineers.

PIM on the other hand has ready-made mechanisms for print materials and publishing to different markets and channels. And it has sufficient change control from a sales and marketing point of view. On the other hand PIM is no good at detailed change control, revisioning and management of design data.

Focus from a PLM perspective

Look at the product information from start to end. Where is it born and where is it used? What you have to focus on is ensuring that the product information is structured in such a way that you CAN use it for other purposes than just a design point of view. E.g. if you need grouping of products in PIM; perhaps use the same grouping in PLM. Ensure that you have sufficient information. You might want to add more information early on to be able to streamline processes and information flow. E.g. In which countries can you sell a product?

The success of PIM will be greatly enhanced if you tie the whole information flow together. From design to procurement and manufacturing to marketing and sales and to service. You will be able to re-use information from PLM and give it additional value.

Summary

PIM plugs a hole in the product lifecycle that PLM should not. From a true lifecycle perspective it makes perfect sense to integrate PLM and PIM. They complement each other well and you get even more value out from your structured data in PLM.

Do not try to extend a PLM system to also cover PIM functionality. PLM systems are complex enough as they are.

Tore Brathaug

www.infuseit.com