User in the Era: Big Data Supply Chains

by Lora Cecere on June 1, 2011 · 6 comments

The time is near.  The time has come.

As I return from my trip last week, I give thanks, that it is not the end of the world that we need to prepare for….  Yes, thankfully, as the Rapture witching hour approached last week, I was holding my grandson in my arms with a glass of wine and having a great discussion with my daughter.  (This is something that I hope never ends….)  With false predictions behind us, and I wing my way back from San Diego, I am thinking about the world of Big Data Supply Chains.  I think that it is time for us, as supply chain leaders, to prepare for a new era:  the big data supply chain.  Here I share the what, the so what and the now what with a focus on why you should care.

What is Big Data?

The concept is simple.  The answer is complex.

Big data is a term used to describe data sets that grow so large, and so fast that conventional reporting and analytics are insufficient.  Can you feel it?  It is coming at us like a Tsunami.  It takes different forms, but what is common is the new world of big data. Let’s examine some trends:

1) You see it in new tagging systems for safe and secure supply chains.

2) It is ever-present in demand sensing and the design of listening posts from social networks.  These technologies us the ante on the use of unstructured text and building supply chain systems that can sense just not respond.  It is one that starts from the outside-in to define the enterprise response.

3) It takes the form of mobile devices that are redefining the workplace. Mobile data has grown 8 fold in five years.

4) It is a new world of convergence of visualization, geolocation, and digital media.

5) Partner data is growing exponentially.  What we once thought was just a simple downstream data repository is now being used as the data translator and harmonizer at both ends of the supply chain. It is redefining the world of business-to-business relationships.  Trading partners are starting to share daily data daily.

This is far different than the world of five years ago when data was shared less often; and when it was, it was usually monthly data monthly or weekly data weekly. Each relationship in the global supply chain has unique requirements for revenue management, contract compliance, shipping documentation, and licensing. As we enter the world, where data is more available from trading partners, we can navigate across the supply chain into customer’s customers and supplier’s suppliers.

In this world of big data, relational databases and desktop applications – spreadsheets, statistical packages and reporting—are insufficient. Instead, it requires the use of parallel software running on tens, hundreds or even thousands of servers.  It is the world of terabytes, exabytes and zettabytes of data.

What is a Big Data Supply Chain?  Value Network?

Bear with me.  I am an old gal.  I remember the early discussions with my boss on what we could do with our supply chain when we rolled the IBM 360, down the halls of the manufacturing plant were I was the Plant Engineering Manager.  The machine was huge, but it allowed us to have localized computer capabilities that were upward compatible to future models.  A local computer and a specialized team for reporting drove step-change improvements for our organization.  We could see trends and drive continuous improvement programs that we previously only talked about.
Today’s era of big data supply chains is an even bigger step change opportunity, but to take advantage of the opportunity, we must re-wire our thoughts to see new possibilities.  It is not just about supply, it is about making tradeoffs to improve value.  It is not just about linear relationships or a chain reaction, it is about sensing networks.  It is not just about right product, right place, at the right time.  Instead, it is about the redesign of value networks that use information to reduce latency, streamline cash flow and drive profitability.

Today’s supply chain systems are not designed for the world of big data. It is coming.  The data will be colossal.  The use of data in the supply chain will differentiate.  Have I convinced you that we are facing a new world?  One that ups the ante to sense and respond?  If so, read more…..

My Magic Ball

I was a Gartner analyst for many years. In fact, if I had not jumped ship with the AMR Research purchase of AMR Research, I would be a Gartner analyst today.  So, using my past training, I share my predictions for the supply chain of big data:

#1 A One Vendor World is not the Answer. The big data supply chain will not be a one vendor world (.9 probability). Yes, I know that it was the promise.  I was also told that there was a Santa
Claus.  Supply chain leaders need to grow up.  Gaining competitive advantage from the big data supply chain will not be a “one throat to choke” scenario.  You cannot afford to tie your apron strings to the innovation of ERP vendors.  If you do, you will move too slowly.

However, I find the evolution of the SAP HANA platform interesting.  I think that it will redefine supply chain execution capabilities.  If nothing changes, SAP will outflank Oracle in defining supply chain capabilities.  We have defined supply chain execution too narrowly.  It is more than order to cash.  There are new opportunities in S&OP execution, demand and supply visibility and demand orchestration.  I am also excited by the focus and energy of Manhattan and Red Prairie to tackle this opportunity more holistic.

#2.  Line of Business (LOB) Meet Data. In companies where the line of business leader steps up to own the big data supply chain, there will be a 3X increase in the ROI of IT investments (.8 probability). I have done research studies over the past five years on IT investments of BI in the supply chain.  One factor is clear to me.  When projects are owned by the Line of Business Leaders, and those LOBs are knowledgeable and capable team players, there is a dramatic difference in the impact and ROI on the project. In the face of the great recession, companies that were better at demand sensing changed their supply chains five times faster.  The issue is finding leaders that are both knowledgeable and capable.

#3.  Not a Project. It cannot be solved one project at a time. Companies that approach this evolution as a program, not a project will increase speed to value by 70%. As I study the evolution of Business Intelligence (BI) in supply chain, it is clear to me.  Project-based evolution absent a program and a strategy is problematic.  Companies that have multiple projects that do not build on a consistent data model, with clear data governance, and definition of the meta-data structures, have built a bridge to nowhere.  Most supply chain leaders, as a consequence, are what were described in the 1960’s song, “I am a real nowhere man.”

#4. We must part with Tradition. It will require taking a leap of faith.   It is not the case of something new, something borrowed, and something blue.  It will require a RETHINKING of supply chains to abstract the supply chain into sensing attributes that can sense market changes quickly, easily translate these changes into the world of supply and transmit them in a meaningful way to the supplier. The design is outside-in, not inside out.  It is not longer the world of the language of SKU (item at a location.) This language gets co-opted by the language of attributes. We will have to remap supply chains, rebuild demand and supply hierarchies, and redefine BI –portals, scorecards, dashboards, and predictive analytics—to think in the world of attributes. (What do I mean by attributes?  The company that I was visiting on Friday asked me the same thing. And, then they started to talk about their new world where they have defined customers and suppliers by four attributes:  capabilities, size and importance, power position, and cultural alignment.  Each of their trading partners has been defined on a continuum on these four dimensions.  So, I said, “Now let’s define your supply chain systems to use the data.”  They nodded in agreement. They got it.)

Think about it.  How does this type of prioritization change how you manage demand shaping programs, contracts, available to promise, order management, new product launch programs, assortment and special programs, allocation, network design, etc.  My client got the message.  It is no longer about a blind SKU moving on a blind order to a customer without any definition.  Just as the body has multiple senses, the supply chain will evolve with multiple sensing mechanisms based on attributes.  This evolution will make current Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) and Advanced Planning Solutions (APS) solutions obsolete over the next five years (.9 probability). Because current analyst models are funded by these late stage technology solutions, you will find little from analysts on the rise of big data supply chains.  The reason? The shift is discontinuous.

#5 MDM will not take us to the Pearly Gates. As companies enter into the world of big data, Master Data Management (MDM) concepts that we know– and never loved– will fall by the wayside.  They will be scrapped.  New technologies will evolve to better handle MDM.  There are three technologies that I am watching closely that I think offer promise, both singularly, and together.

  • Search Engine Optimization(SEO) : Endeca is using their SEO tools to improve flexibility in parts management in the automotive industry.  The use of tagging and attributes improves flexibility.
  • Artificial Intelligence:  Enterra Solutions redefinition of security data for the Iraq war was applied successfully to Conair and Newell Rubbermaid supply chains in 2011 to sense supply issues and redefine the response.
  • Intelligent Workflow for Governance:  Kalido has introduced intelligent governance workflow for line of business users.

Within five years, the landscape of master data solutions will be redefined (.9 probability).

So, what do you do?

  1. Stabilize.Take a look at your product portfolio and stabilize traditional approaches, especially ERP projects. Focus on the use of ERP for seamless movement of transactions. Throw away the enterprise application lexicon that you have learned and get ready for a new world.
  2. Define. Map the supply chain from the outside-in focused on how customer attributes translate to service and product attributes.  Think about how and why you sense and what a decrease in information latency can mean for your supply chain.
  3. Build.Focus on building an inter-enterprise data model.  Focus on the ends of the supply chain….  Realize that there was never “R”– or relationship– in CRM or SRM applications. Think about what you could accomplish through the building of business-to-business relationships through a combination of social, sensing/listening technologies and predictive analytics to transform B2B.
  4. Will require a Team.  Invest in a BI team of excellence to look at how companies can drive insights from data. Staff it cross-functionally, but align the reporting relationships to a line-of-business thought leader that has cross-functional responsibilities.  Experiment with new master data management systems.  Develop a holistic BI strategy for your value networks.
  5. Get good at data.  Train teams on the evolving world of business intelligence and the use of trading partner data in data-driven decisions.  Reward innovation through the use of predictive analytics.  Focus on data reuse, meta-data definitions, and data enrichment strategies.  Overlay the BI team of excellence on top of sales and supplier relationships to build data-driven sensing to drive supply chain requirements.

What do you think?

Have you thought about big data supply chains?  Data for value networks?  Let me know your thoughts.

For additional articles on supply chain business intelligence
topics, reference these blog posts:

Ring in the New Year: http://www.supplychainshaman.com/altimeter-group/ring-in-the-new-year/

Three Things that I have Learned about Downstream Data: http://www.supplychainshaman.com/downstreamdata/three-things-i-have-learned-about-using-downstream-data/

A Leap of Faith?  http://www.supplychainshaman.com/demanddriven/a-leap-of-faith/

http://www.supplychainshaman.com/demanddriven/start-a-new-conversation-free-the-data-to-answer-the-questions-that-you-dont-know-to-ask/

http://www.supplychainshaman.com/new-technologies/is-this-the-future-of-downstream-data/

http://www.supplychainshaman.com/supply-chain-excellence/crossing-the-great-divide/

{ 5 comments… read them below or add one }

Keith Scovell June 2, 2011 at 5:40 am

Lora – Great article! You have put into words what most of us are sensing and experiencing – I really like this idea of outside/in – market sensing and relationship across the supply chain. With the shift to shopper power and focus, marketing through social channels, the supply chain will need to respond to market changes at speed of market change and social marketing.

I included 3 old Drucker quotes that ring true and believe that exploiting the change you have outlined will produce the next set winners!

“Trying to predict the future is like trying to drive down a country road at night with no lights while looking out the back window.
The only thing we know about the future is that it will be different.
The entrepreneur always searches for change, responds to it, and exploits it as an opportunity.”
Peter Drucker Quotes

Reply

Lora Cecere Lora Cecere June 2, 2011 at 7:35 am

Thanks Keith. Any advice, from your deep consulting background. for people that are going through this transition?

Reply

Keith Scovell June 2, 2011 at 8:36 am

Lora – thx! Your point on this not being a project is critical – it will only succeed if it is a change program – with clear objectives, mesures and a clear detailed plan.

Transformational Change requires Leadership up and down and across the organization while aligned to current market strategies. Your suggestion that old mental models must be discarded, and new mental models (outside/in) adopted will require a change in thinking and a significant challenge in moving away from years of proven experience. How much change can the organization digest while operating the current business is always the question that needs to be answered.

My second thought is that while all products can benefit from this shift to Big Data in the Supply Chain – the advantages diminish as you move away from the high volatility, price / promotion-driven categories. So getting clarity by category/brand and the potential business impact should drive the change initiative.

Last – the emerging Big Data technologies hold great promise – but will require repeated field production experience. Pilots will best serve this change program to help develop learnings and improve long term success. These are some initial thoughts – process and organizational change enabled by technology.

Reply

Steven Daugherty June 3, 2011 at 4:54 pm

When I looked at ‘medium’ sized data models a couple years back, it seemed that one of the big challenges was how to share between companies. Manufacturers will certainly want data from retailers and vice versa to augment and accelerate their BI initiatives. EDI is of course out of the question. AS2 and XML can handle a somewhat larger load such as POS for example, but how to realistically move Big Data from one trading partner to another? Has this been addressed yet? One vendor was working on a proprietary standard for customers that all used their technology, but that goes against your point on the One Vendor World.

Reply

Mike Wheeler June 6, 2011 at 12:37 pm

Great blog Lora! The notion of the need for true organizational transformation and the application of a holistic program rather than a series of projects is so important to get across. As we look at companies that are struggling with just the data governance aspect of managing Big Data, it is often due to an atomic-level focus on the data itself rather than on how data affects business performance as it flows through the business processes that produce and comsume it. We can’t swallow the elephant whole, but we need cross-functional (and cross-process) alignment and a top-down vision of the end state via a comprehensive data management strategy. Much can be learned from supply chain management disciplines to encourage a progammatic and structured approach to such a core, and fundamental process as managing the core data assets of the enterprise. Without the top-down vision and a structured, repeatable, predictable set of processes, companies are guessing their way through it.

Reply

Leave a Comment

{ 1 trackback }

Previous post:

Next post: