PIM

Product vs Material vs Article vs Item

Henrik Gabs LiliendahlLeave a comment

One problem with having English as the lingua franca on this planet is that English have a lot of different words meaning (almost) the same. One example is product, material, article, and item. This entity is the core entity within the master data domain we usually call the product domain.

I have experienced numerous occasions where these words are used to describe different perspectives across lifecycles or granularity in different ways within the same organization operating in manufacturing, distribution, retail, construction or comparable industry.

Product

The word product is part of the product domain in Master Data Management (MDM). It is also part of the adjacent discipline called Product Information Management (PIM). Also, it is part of the upcoming Digital Product Passport (DPP).

Furthermore, the word product is part of the term Universal Product Code (UPC) which is the North American version of the Global Trade Identification Number (GTIN).

We also have the word product as part of the term finished product.

If product is not the overarching term for material, article, and item it is often seen as either:

  • The sellable thing that comes out of a manufacturing process.
  • A higher level in a product hierarchy where there are varying articles/items on a lower level.

Material

In SAP, the predominant ERP application on this planet, the core product entity is called material.

We also have the word material as part of the term raw material.

Material is often seen as a thing that goes into a manufacturing or construction process.

Article

The word article is part of the term International Article Number previously known as European Article Number (EAN) which is the European version of the Global Trade Identification Number (GTIN).

An article is often seen as the level in a product hierarchy where you can assign a GTIN, meaning that the articles with the same GTIN have the same dimensions, color, other specifications, and packing.

This is equivalent to the term Stock Keeping Unit (SKU).

Item

Item is often used as an alternative or neutral term for product, material and/or article. Item is widely used as a column header for the sold products/materials/articles on an sales order and invoice or the requested products/materials/articles on a purchase order.

Proper use

As with so many other similar terms there is no generic proper use that your organization can stick to. There may be a trend or standard in your industry you can adhere to. Anyway, your data governance framework should be able to state the proper use within your organization as part of a business glossary.

Have you formed a business glossary or similar construct that defines how the words product/material/article/item/whatever (in your official language) should be used?

The Intersection Between MDM, PIM and ESG

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As touched on in the post Three Essential Trends in Data Management for 2024, the Environmental, Social and Governance (ESG) theme is high on the data management agenda in most companies. Lately I have worked intensively with the intersection of ESG and Master Data Management (MDM) / Product Information Management (PIM).

In this post I will go through some of the learnings from this.

Digital Product Passport

The European Union concept called the Digital Product Passport (DPP) is on its way, and it will affect several industries, including textile, apparel, and consumer electronics. The first product category that will need to comply with the regulation is batteries. Read more about that in the article from PSQR on the Important Takeaways from CIRPASS’ Final Event on DPP.

I have noticed that the MDM and PIM solution providers are composing a lot of their environmental sustainability support message around the DPP. This topic is indeed valid. However, we do not know many details about the upcoming DPP at this moment.

EPD, the Existing DPP Like Concept

There is currently a concept called Environmental Product Declaration (EPD) in force for building materials. It is currently not known to what degree the DPP concept will overlap the EPD at some point in the future. The EPD is governed by national bodies, but there are quite a lot of similarities between the requirements across countries. The EPD only covers environmental data whereas the DPP is expected to cover wider ESG aspects.

Despite the minor differences between DPP and EPD, there is already a lot to learn from the data management requirements for EPD in the preparation for the DPP when that concept materializes – so to speak.

Environmental Data Management

The typical touchpoint between the EPD and PIM today is that the published EPD document is a digital asset captured, stored, tagged, and propagated by the PIM solution along with other traditional digital assets as product sheets, installation guides, line drawings and more.

The data gathering for the EPD is a typical manual process today. However, as more countries are embracing the EPD, more buyers are looking for the EPD and the requirements for product granularity for the EPD are increasing, companies in the building material industry are looking for automation of the process.

The foundation for the EPD is a Life Cycle Assessment (LCA). That scope includes a lot of master data that reaches far beyond the finished product for which the EPD is created. This includes:

  • The raw materials that go into the Bill of Materials.
  • The ancillary materials that are consumed during production.
  • The supplier’s location from where the above materials are shipped.
  • The customer’s location to where the finished product is shipped.
  • The end user location from where recycling products is shipped.
  • The recycled product that goes back into the Bill of Materials.

All-in-all a clear case of Multi-Domain Master Data Management.

It is easy to imagine that the same will apply to products such as textile, apparel and electronics which are on the radar for the DPP.

Examples of Environmental Data

CO2 (or equivalent) emission is probably the most well known and quoted environmental data element as this has a global warming potential impact.

However, the EPD covers more than twenty other data elements relating to potential environmental impact including as for example:

  • Ozone layer depletion potential – measured as CFC (or equivalent) emission.
  • Natural resource (abiotic) depletion potential – measured as antimony (or equivalent) consumption.
  • Use of fresh water – measured as H2O volume consumption.

Can I help You?

If you are in a company where environmental sustainability and data management is an emerging topic, I can help you set the scene for this. If you are at an MDM/PIM solution provider and need to enhance your offering around supporting environmental sustainability, I can help you set the scene for this. Book a short introduction meeting with me here.

2022 Data Management Predictions

Henrik Gabs Liliendahl2 Comments

On the second last day of the year it is time to predict about next year. My predictions for the year gone were in the post Annus Horribilis 2020, Annus Mirabilis 2021?. These predictions were fortunately fluffy enough to claim that they were right.

There is no reason not to believe that the wave of digitalization will go on and even intensify. Also, it seems obvious that data management will be a sweet spot of digitalization.

The three disciplines within data management focussed on at this blog are:

  • MDM: Master Data Management
  • PIM: Product Information Management
  • DQM: Data Quality Management

So, let`s look at what might happen next year within these overlapping disciplines.

MDM in 2022

MDM will keep inflating as explained in the post How MDM inflates. 

More organizations will go for enterprise wide MDM implementations and those who accomplish that will continue to do interenterprise MDM.

More business objects will be handled within the MDM discipline. Multidomain MDM will in more and more cases extend beyond the traditional customer, supplier and product domain.

Intelligent capabilities as Machine Learning (ML) and Artificial Intelligence (AI) will augment the basic IT capabilities currently used within MDM.

PIM in 2022

As with MDM also PIM will go more interenterprise wide. As organizations get a grip on internal product data stores the focus will move to collaborating with external suppliers of product data and external consumers of product data through Product Data Syndication.

In some industries PIM will start extending from the handling the product model to also handling each instance of each product as examined in the post Product Model vs Product Instance.

There will also be a term called augmented PIM meaning using Machine Learning and Artificial Intelligence to improve product data quality. In fact, classification of products using AI has been an early use case of AI in data management. This use case will be utilized more and more besides other product information use cases for AI and ML.

DQM in 2022

Data quality management will also go wider as data quality requirements increasingly will be a topic in business partnerships. More and more contracts between trading partners will besides pricing and timing also emphasize on data quality.

Data quality improvement has for many years been focused on the quality of customer data. This is now extending to other business objects where we will see data quality tools will get better support for other data domains and the data quality dimensions that are essential here.

ML and AI data quality use cases will continue to be implemented and go beyond the current trial stage to be part of operational business processes though still at only a minority of organizations.  

Happy New Year.

The Disruptive MDM/PIM/DQM List 2022: Informatica

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The next vendor to be included on The Disruptive MDM / PIM / DQM List 2022 is Informatica.

Informatica has been a leader on the Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) space for many years latest as seen in their 6th time front position on the Gartner MDM quadrant.

Their success is also apparent in the recent Earnings Report and their return to the public markets.

You can learn more about Informatica here.

Welcome Viamedici on The Disruptive MDM/PIM/DQM List

Henrik Gabs LiliendahlLeave a comment

I am pleased to welcome Viamedici on The Disruptive MDM/PIM/DQM List and thus also one of the innovative solutions to be on the 2022 version.

During the recent years I have followed Viamedici as a very interesting solution among those Product Information Management (PIM) vendors who are developing into multidomain Master Data Management (MDM) vendors.

Their PIM solution has some unique capabilities around managing complex products and real-time handling of large numbers of products, attributes, relations, and digital assets. These capabilities can be utilized to cover extended MDM where multidomain MDM goes beyond traditional customer, supplier, and product MDM.

You can learn more about Viamedici here.

How MDM Inflates

Henrik Gabs LiliendahlLeave a comment

Since the emerge of Master Data Management (MDM) back in 00’s this discipline has taken on more and more parts of the also evolving data management space.

The past

It started with Customer Data Integration (CDI) being addressing the common problem among many enterprises of having multiple data stores for customer master data leading to providing an inconsistent face to the customer and lack of oversight of customer interactions and insights.

In parallel a similar topic for product master data was addressed by Product Information Management (PIM). Along with the pains of having multiple data stores for product data the rise of ecommerce lead to a demand for handling much more detailed product data in structural way than before.

While PIM still exist as an adjacent discipline to MDM, CDI mutated into customer MDM covering more aspects than the pure integration and consolidation of customer master data as for example data enrichment, data stewardship and workflows. PIM has thrived either within, besides – or without – product MDM while supplier MDM also emerged as the third main master data domain.   

The present

Today many organizations – and the solution providers – either grow their MDM capabilities into a multidomain MDM concept or start the MDM journey with a multidomain MDM approach. Multidomain Master Data Management is usually perceived as the union of Customer MDM, Supplier MDM and Product MDM. It is. And it is much more than that as explained in the post What is Multidomain MDM?

As part of a cross-domain thinking some organizations – and solution providers – are already preparing for the inevitable business partner domain as pondered in the post The Intersection of Supplier MDM and Customer MDM.

The PIM discipline has got a subdiscipline called Product Data Syndication (PDS). While PIM basically is about how to collect, enrich, store, and publish product information within a given organization, PDS is about how to share product information between manufacturers, merchants, and marketplaces.

The future

Interenterprise MDM will be the inflated next stage of the business partner MDM and Product Data Syndication (PDS) theme. This is about how organizations can collaborate by sharing master data with business partners in order to optimize own master data and create new data driven revenue models together with business partners.

It is in my eyes one of the most promising trends in the MDM world. However, it is not going to happen tomorrow. The quest of breaking down internal data and knowledge silos within organizations around is still not completed in most enterprises. Nevertheless, there is a huge business opportunity to pursue for the enterprises who will be in the first wave of interenterprise data sharing through interenterprise MDM.

Extended MDM is the inflated next scope of taking other data domains than customer, supplier, and product under the MDM umbrella.

Reference Data Management (RDM) is increasingly covered by or adjacent to MDM solutions.

Also, we will see handling of locations, assets, business essential objects and other digital twins being much more intensive within the MDM discipline. Which entities that will be is industry specific. Examples from retail are warehouses, stores, and the equipment within those. Examples from pharma are own and affiliated plants, hospitals and other served medical facilities. Examples from manufacturing are plants, warehouses as well as the products, equipment and facilities where their produced products are used within.

The handling of all these kind of master data on the radar of a given organization will require interenterprise MDM collaboration with the involved business partners.

Organizations who succeed in extending the coverage of MDM approaches will be on the forefront in digital transformation.

Augmented MDM is the inflated next level of capabilities utilized in MDM as touched in the post The Gartner MDM MQ of December 2021 and Augmented MDM. It is a compilation of utilizing several trending technologies as Machine Learning (ML), Artificial Intelligence (AI), graph approaches as knowledge graph with the aim of automating MDM related processes.

Metadata management will play a wider and more essential role here not at least when augmented MDM and extended MDM is combined.    

Mastering this will play a crucial role in the future ability to launch competitive new digital services.

What is Collaborative Product Data Syndication?

Henrik Gabs Liliendahl1 Comment

Product Data Syndication (PDS) is a sub discipline within Product Information Management (PIM) as explained in the post What is Product Data Syndication (PDS)?

Collaborative PDS can be achieved at scale with a specialized product data syndication service where the manufacturer can push product information according to their definitions and the merchant can pull linked and transformed product information according to their definitions.

With Collaborative Product Data Syndication, you can get the best of two worlds:

  • You can have the market standard that makes you not falling behind your competitors.
  • However, you can also have unique content coming through that puts you ahead of your competitors.

The advantages of collaborative PDS versus other PDS approaches was examined in the post Collaborative Product Data Syndication vs Data Pools and Marketplaces.

The Product Data Lake solution I am involved with utilizes that data lake concept to handle the complexities of having many different data standards for product information in play within supply chains and encompass the many different preferences for exchange methods.

Our approach is not to reinvent the wheel, but to collaborate with partners in the industry. This includes:
·       Experts within a type of product as building materials and sub-sectors in this industry, machinery, chemicals, automotive, furniture and home-ware, electronics, work clothes, fashion, books and other printed materials, food and beverage, pharmaceuticals and medical devices. You may be a specialist in certain standards for product data. You will link the taxonomy in use at two trading partners or within a larger business ecosystem.
·       Product data cleansing specialists who have proven track records in optimizing product master data and product information. You will prepare the product data portfolio at a trading partner and extend the service to other trading partners or within a larger business ecosystem.
·       System integrators who can integrate product data syndication flows into Product Information Management (PIM) and other solutions at trading partners and consult on the surrounding data quality and data governance issues. You will enable the digital flow of product information between two trading partners or within a larger business ecosystem.
·       Tool vendors who can offer in-house Product Information Management (PIM) / Master Data Management (MDM) solutions or similar solutions in the ERP and Supply Chain Management (SCM) sphere. You will be able to provide, supplement or replace customer data portals at manufacturers and supplier data portals at merchants and thus offer truly automated and interactive product data syndication functionality.
·       Technology providers with data governance solutions, data quality management solutions and Artificial Intelligence (AI) / machine learning capacities for classifying and linking product information to support the activities made by other delegates and subscribers.
·       Reservoirs, as Product Data Lake is a unique opportunity for service providers with product data portfolios (data pools and data portals) for utilizing modern data management technology and offer a comprehensive way of collecting and distributing product data within the business processes used by subscribers.

The Disruptive MDM/PIM/DQM List 2022: Magnitude Software

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In the round of presenting the solutions for The Disruptive MDM / PIM / DQM List 2022 the next vendor is Magnitude Software.

Magnitude Software has two solutions on the list:

  • Kalido MDM where you can define and model critical business information from any domain – customer, product, financial, vendor, supplier, location and more – to create and manage accurate, integrated, and governed data that business users trust.
  • Agility Multichannel PIM which has the capabilities to get products to market faster with a simple-to-use, comprehensive Product Information Management solution that makes it easy to support commerce across digital and traditional channels.

Learn more about Kalido MDM here and Agility Multichannel PIM here.

The Disruptive MDM/PIM/DQM List 2022: Contentserv

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One of the recurring entries on The Disruptive MDM/PIM/DQM List is Contentserv.

Contentserv operates under the slogan: Futurize your customers’ product experience.

Using Contentserv, you will be able to develop the groundbreaking product experiences your customers expect — across multiple channels. Contentserv help you unleash the potential of your product information, using our unique combination of advanced technologies.

Contetserv has combined multiple data management technologies in a single platform for controlling the total product experience. The platform facilitates collecting data from suppliers, enriching it into high-grade content, and then personalizing it for use in targeted marketing and promotions.

Learn more about the Contentserv Product Experience Platform here.

PS: You can also find some compelling success stories from Contentserv on the Case Study List here.

Precisely Becomes a Multidomain Vendor

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Yesterday Precisely announced that they are going to acquire Winshuttle.

This acquisition comes just after that Precisely took over Infogix as reported in the post Precisely Nabs Another Old One. Also, Precisely, then named Syncsort, took over a part of Pitney Bowes not too long ago as examined in the post Syncsort Nabs Pitney Bowes Software Solutions.

The previous acquisitions have strengthened the Precisely offerings around data quality for the customer master data domain and the adjacent location domain.

The Winshuttle take over will make Precisely a multidomain vendor adding cross domain capabilities and specific product domain capabilities.

The original Winshuttle capabilities revolves around process automation for predominately SAP environments covering all master data domains and further Application Data Management (ADM).

As Winshuttle recently took over the Product Information Management (PIM) solution provider Enterworks, this will bring capabilities around product master data management and thus make Precisely a provider for a broad spectrum of master data domains.

The interesting question will be in what degree Precisely over the time will be willing to and able to integrate these different solutions so a one-stop-shopping experience will become a one-stop digital experience for their clients.