value networks

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/

Revenue Management: Beyond Smoke and Mirrors

by Lora Cecere on March 18, 2011 · 0 comments

Improving revenue management –which includes the management of multi-party trade settlement (sometimes dubbed bifurcated trade management) — is an equal opportunity for all supply chains.  No matter whether you are in a consumer, high tech, life sciences, or chemical supply chain it is a major source of cost, waste and frustration.  Executives often will ask, “Why can’t we get this right?”  I laugh and empathize.  What seems so simple is very complex.    

The revenue management process varies by industry.  Each value network shapes demand a bit differently and the contract terms are VERY industry specific. For example, consumer products companies lean heavily on trade promotions, high tech supply chains focus on new product introductions, life sciences on rebates and value-based outcomes and the chemical industry on price.  Despite the differences there are commonalities:

  • Traditional CRM is not the answer.  The historic footprint of CRM is sales pipeline management, customer service and call center execution and business development.  This footprint lacks the data model for either decision support (Revenue Management Optimization (RMO)) or execution (Revenue Management Execution (RME).  This CRM data model is fundamentally flawed—focused on a pipeline data model for sales effectiveness versus a product/services data model that looks at the process workflows of bifurcated trade, the inter-relationships of the demand shaping levers (price, promotion, incentives, buzz from the social web, trade and brand marketing and new product launch) and the visibility of a clear baseline forecast. As a result, the industry is forced to nurture and evolve small, industry-specific providers to augment and redefine front-office functionality.
  • Complex Workflows with Substantial Opportunity.  For the corporate fiscal year ending in 2010, the size of the prize is large. The average consumer products company spent 22% of revenue on trade promotion management (source Symphony/IRI and AMR Research/Gartner) and for the average life sciences company, rebates represented 18% of revenues (source IMS). For either industry segment this can quickly add up to over a billion dollars annually.  Yet, no company that I have interviewed in either industry (over 150 companies) believes that their processes are under control.  Uniformly, companies see revenue management as an opportunity, but do not know how to seize the opportunity.  There is no easy answer.  To understand why, read on.
  • Industry-specific Workflows.  Each industry shapes demand differently, has different contracting processes with their downstream trading partners (buy-side), and uses substantially different language/terminology to describe what they do. (Can you imagine if you substituted the acronym BOGO (Buy one Get one Free) from Consumer Products (CPG) sales cycle for Averaged Managed Price (AMP) for life sciences sales cycle?) These processes are VERY industry specific.

This leads to a problem.  When buying a solution, where do companies turn?  Who can they trust?  There is no perfect solution.  Why? Traditional Customer Relationship Management (CRM) technologies are insufficient to solve the problem.  In sales cycles, the battle lines in sales cycles quickly form.  Information Technology departments want one throat to choke and believe that this type of functionality can be sourced from a CRM or ERP provider.  Lines of Business (LOB) leaders believe that they need industry-specific functionality from industry-specific suppliers.  They are both right, they are just not good at drawing the battle lines.    Companies need traditional CRM functionality for business development and contact management, but industry-specific functionality for predictive analytics, base-line forecasting and bi-furcated trade management.  The decision on Business Intelligence needs to be based on the total IT portfolio.

  • Changing Processes.    These are not enterprise, but are inter-enterprise workflows, driven largely by the nature of the relationships in the extended value chain.  As a result, they need to be designed from the outside-in not the inside-out.   It is not easy.  The technologies lack an inter-enterprise system of record and standards.   Given the recent shifts in power and the increasing compliance/regulations of these industries, the industry processes are in flux and the need is greater with even more dollars on the table.
  • Opportunity Abounds in both Planning and Execution.  While revenue management should be a horizontal process focused on demand orchestration, the applications in the market are largely piecemeal serving organizational silos not end-to-end supply chain processes. There are no complete solutions. The choice is fraught with risk, but I have seen greater success when companies chose industry-specific best of breed providers than try to adapt the data model through custom development that is required with an ERP solution.  In short, while people want it there is no effective end-to-end solution for any industry for revenue management.

Split the Baby?

While it would be great if there was an industry roll-up strategy to consolidate the small vendors that abound in the area of revenue management to deliver an end-to end solution? The list of names is long:  Accenture/CAS Systems, Adesso, Biztech, DemandTec, MEI, Model N, ProMax, Oracle, Symphony/IRI, SAS, Synectics, Vendavo, Zilliant… 

 I fear that the end-to-end solution is a long way off.  Change is slow.  Until then, users will have to split the baby by layering industry-specific revenue management software over industry agnostic CRM. 

However, last week, there were a series of announcements that I feel are deserving of a mention. The industry is changing, albeit slowly. 

Model N with its Feet on the Ground and its Head in the Clouds.  Last week, as I sat in the packed audience at the Model N user conference, named Rainmaker, you could feel the energy.  As a company, ModelN is now nine years old with 350 employees and a global presence.  It primarily serves two industries:  life sciences and high tech.  The company has moved to an agile release schedule allowing them to move quickly against the changing requirements of life sciences and Hi Tech.  Last year, they successfully released five major and two minor releases.  The good news for me was the successful launch of their cloud service.   Buyer preference in revenue management is clearly moving to Software as a Service (SaaS), and Model N can now answer this challenge.  

Model N is clearly a company that is beyond Smoke and Mirrors.  They have a strong product heritage, and pride themselves in serving their customers.  I have wondered on many occasions how more successful Model N could be if they improved their sales and marketing.  They lack name recognition, and have not differentiated themselves in the market, although the solution is clearly differentiated and reliable.  When the smoke clears, I feel that Model N will stay be a player.

M-Factor Acquired by DemandTec.  On Thursday last week, DemandTec announced the acquisition of M-Factor.  The M-Factor solution was a unique, niche solution that was launched before its time.  The solution enabled the optimization of all marketing spend in consumer products –advertising with a multi-year lift and trade promotion spending with single period lift—to determine the right mix of demand shaping activities.  The visionary founder died tragically seven months ago, and although the company had raised venture funds in tough market conditions, like many small enterprise software companies, scaling growth is expensive and takes time compared to the consumer plays that Silicon Valley currently favors.  Despite the depth of the optimization solution—one of the strongest technologies in the market to determine baseline forecasting—and a good number of tier one customers— the purchase price was a good deal for DemandTec. 

While the DemandTec press releases on the acquisition are bullish, and the companies share a common heritage, the merging of these two SaaS offerings does not yield a complete solution for consumer products.  While a strong offering for trade promotion management in the sales account teams, the DemandTec solution still lacks the core functionality for headquarters trade promotion management.  However, it is a nice complement to an ERP solution like Oracle.  The press release was a bit too much of smoke and mirrors for this old analyst gal.

ProMax: A New Contender.  A new contender in consumer products trade promotions from down under –Australian heritage—entered the North American and European markets in 2010.  Last week, they announced selection by Kimberly Clark.  ProMax is attacking the CAS (reference blog article Accenture buys CAS, http://www.supplychainshaman.com/page/4/) user base.  With successful implementations at Biersdorf, Dial and Henkel, the team is inching forward touting a simpler, easier best of breed solution.  I will keep my eyes on their references to see if they deliver.  This is a case of where there is smoke there may be fire.  Too early to tell, but promising.

Three announcements in a confused market full of smoke a d mirrors.   While we are inching down the path, we are still a long way from a perfect end-to end process solution for revenue management.  Next week, I will be at SAP Insider and the Logility User Conference.  Look for updates from me from Orlando. Also look for my post on the Rise of Social Commerce and the many interactions that I am having with retailers on Monday.  Lots of progress in that space….