Big Data

Three Questions People Are Afraid to Ask….

by Lora Cecere on October 29, 2014 · 0 comments

Groupthink is a psychological phenomenon that occurs within a group of people in which there is a desire for harmony within the group, but the result is an irrational or dysfunctional outcome.  Wikipedia

You know the drill. The meeting is on everyone’s calendar. It has been set up by the CEO or a board member’s assistant months in advance. The room is big, the PowerPoint deck is large, and the coffee cups are arranged in neat rows on the counter of the side of the room. There is an abundance of pastries flowing from the basket, and the stage is set for an impactful meeting. Even though things seem to be going well (all of the meeting details are well-executed and the speaker is giving an energized presentation), the room is eerily quiet. The speaker is speaking, the beautiful slides move quickly at the front of the room, but the audience is not engaged.

In my travels, I attend these meetings frequently. They are precipitated by a strategic relationship between a consulting company and the executive team. The consulting team pitches a theme—vision of supply chain best practices, big data analytics, or demand-driven value networks—to the executive team, and a new project is initiated. The first step in the journey is a kick-off meeting. The second step is usually a large implementation of a technology project—Enterprise Resource Planning, Customer Relationship Planning or Analytics. I feel that the industry is engaged in ‘Group Think’. No one in this meeting is going to ask tough questions. The board has not set up the team for success. Here are the three questions that I would like people to ask:

Table 1. Comparison of Results for Best of Breed Solution Providers to ERP Expansionists in Supply Chain Planning

Question 1: What drives a successful implementation of supply chain planning?  Supply chain planning is now in its fourth decade. The first evolution of technologies were built by best-of-breed solution vendors. These solutions were usually implemented by the technology provider by consultants with specialized skill sets. The promise was the delivery of a decision support system that would allow the organization to optimize the relationships between cash, cost, and customer service against the strategy.

The second-generation of solutions were built and marketed by Enterprise Resource Planning technology companies like SAP and Oracle. The promise of these solutions was that an ‘integrated planning solution with ERP would deliver greater value’. (This solution is termed the ERP Expansionist in Table 1.) This new solution was favored by the Information Technology (IT) organization. By purchasing planning and transactional systems for a common vendor, they had one throat to choke and they were familiar with the architectural elements. It was also the preference of the consulting partners because the projects were longer, more costly and better aligned with the consulting model. But, did it add more value? The answer is no. As shown in Table 1, the movement to adopt “integrated ERP and Supply Chain Planning software from an ERP vendor” moved the industry backward. Ironically, the solutions implemented by the consultants, as contrasted to those implemented by the technology vendors, also produced less desirable results.

How do I know this?  The results in Table 1 come from a nine-month research project of 120 respondents representing 183 instances of demand and supply planning. (The average company has more than one instance of both.) In the study, the respondents were asked to rate time to Return on Investment, and satisfaction. We also correlated the results to balance sheet performance. What do we find? Best-of-breed solutions have a higher Return on Investment and are quicker to implement. They also have higher satisfaction rates. The highest satisfaction comes when the technology vendor implements the solution. It is significantly different at a 90% level of confidence. In the data, we can also see that the implementations from the ERP Expansionists have significant gaps—requiring more planners, longer times to plan, and greater difficulties getting to data.

Why does this happen? Leadership teams struggle with the trade-offs between cash, cost and customer service. As a result, supply chain planning is often a targeted project when the strategic consulting partners talk to their clients at a board level. The strategic consulting partners are respected in these relationships and seldom questioned, and the stage is set. In parallel, there is a low-level of trust for the best-of-breed technology vendors. Many are very sales-driven and difficult to work with. The market was overhyped at an early stage and trust eroded. Would the board deliberately select a system that takes longer to implement, with a lower Return on Investment, requiring more ongoing labor and producing lower results? Of course not. But, the industry is in a groupthink. No one is having a fact-based discussion. This is how we see our role.

Table 2. Characteristics of those Satisfied with Supply Chain Planning

Q2: Who does supply chain planning well? What can we learn? As shown in table 2, the companies that are the most satisfied with planning are smaller organizations with 15 or less planners and without high item complexity.

To drive maximizing the value of planning, organizations need to be aligned against an operating strategy. Companies adopt planning to optimize the organization’s response from the customer’s customer to the supplier’s supplier. The supply chain planning cannot be effective if implemented by a supply chain function that is focused only on customer service, logistics and distribution. It requires the support of the organization to optimize the response for the end-to-end value chain that crosses functions.

What can we learn from this table, and the research? A successful supply chain planning implementation is about more than technology. The implementation of decision support tools needs to be a way of life. Planners need time to plan, and the organization needs to be aligned against a shared vision or operating plan. It cannot be about the optimization of vertical silos within the organization. This leads to a sub-optimal response.

The second thing that I learned from the research is that we do not have good solutions for large organizations in the market today. If you have a large number of planners and high item complexity, you are at risk. This I think leads us to the Third act of Planning.  In the third act, I believe that the technologies are very different from those in the first three decades of evolution. In the Third Act, I believe that the processes and technologies are redesigned outside-in from the channel back to the enterprise. I think that it is a new world of cognitive learning, rules-based ontologies, concurrent optimization, and B2B Networks based on canonical infrastructures with many-to-many data models. These new technologies are evolving. (I will write more on this in my next blog post.)

Q3: How do I become demand-driven? Data surrounds the company. The data in the channel is changing faster than the company can adopt processes and technologies to use it. It is piling up on the doorsteps of most major companies. Some may be used by the digital marketing teams for marketing purposes, but the average company does not know how to use it. They struggle to listen to and interpret market signals. It is ironic that there has never been a time in history where customer data is more available, and the demand higher for companies to operate a customer-centric value network to sense and respond to true demand, but the solutions to use the data are evolving. Today, they do not exist.

Most consultants and technologists are guilty of bait and switch. The discussion is on becoming demand-driven, but the recommended solution is a traditional approach. When the pretty slides are over, the consultant submits a project plan to implement the traditional forecasting, order management and supply planning that does not sense market demand and translate it into usable outcomes. The audience listening to these presentations does not have the courage to raise their hands and ask the question, “How do you define demand-driven value networks?” and then follow with the question of, “Can the traditional technologies really help us to become demand driven?” The consultants are incented to recommend the solutions that they are familiar with in implementing. Most know very little about the true definition of demand driven.

Tomorrow, I get to deliver this message to a large manufacturing client. I am speaking at their global kick-off. I am going to encourage them to not be guilt of industry groupthink. In this blog, I hope that I push you too. I want you to raise your hand and question the status quo. And, if you do not have the courage to do it directly, share the research and ask your leadership team to give me a call. I answer all emails and phone calls. I want to change the dialogue. It is tough for me to see that nine out of ten companies are stuck, and not making progress, at the intersection of operating margin and inventory turns. I grow weary of all of the consultant presentations of how supply chains can reduce inventory without looking at the form and function of inventory and the real needs for inventory to be a buffer of demand and supply volatility.

Join us next week for our webinar on Supply Chain 2020. In this session, we will share research on the future of supply chain technologies, and I will be joined by a panel of two leaders that will share their insights on what the future means for them. In addition, I am now done with the page proofs for my new book, Metrics that Matter. The book is a story. It is a fable about a guy by the name of Joe that does not want to be an average Joe. Instead, he wants to drive supply chain excellence and build the metrics that matter. To do this, he has to build a guiding coalition and  define outside-in processes. Like you, he works with a group of characters within his organization, and is struggling with how to define the opportunity for the company. To do this, he has to use political capital, against great opposition, within the organization to redefine supply chain excellence. The book publishes in December 2014. In parallel, we are busy building a simulation game for organizations to play to understand the concepts of managing the metrics as a system and the importance of outside-in processes. Attendees at our 2015 Global Summit will get to participate in the launch of this new simulated exercise. We hope to see you there!

 

 

Dreaming of Clouds, Lakes and Streams

by Lora Cecere on June 13, 2014 · 2 comments

 It is morning in Chicago. I crave coffee. The sun shines brightly out the window, but the noisy city is quiet. My mind is moving to the click-clack of my keyboard on this quiet morning.

I cannot sleep. It is hard to quiet an unquiet mind. Somewhere deep inside me is the need to write this post.

Yesterday, I spoke at the Eye for Transport conference on the Big Data opportunity in supply chain. (The slides are available on slideshare.) Before I start, let me give a preamble. I hate the term Big Data. It is overhyped and overused. As a result, it has lost meaning. I writhe in my seat when I hear it. It is even more painful when I hear others speak about it to drive commercial aspirations without a firm grounding in reality.

When I go to an analytics conference today, I feel like the dumbest person in the room. The rate of change is incredible. I firmly believe that the Supply Chains in the next decade will look vastly different from those we see today. I think that we are poised for the third act of supply chain technologies. It is a new world of Best-of-Breed Technologies. The reason? Today, we are stuck. Nine out of ten companies are not making progress at the intersection of inventory turns and operating margins.  Our supply chains are not able to meet the requirements of the business. Frustration abounds.

For the supply chain leader, the noise of change is deafening—rising complexity, demand volatility, new business models, commodity volatility, ethical supply chains, collaborative economy—yet, our processes and technologies are staid and unwielding. We are paralyzed by history that has been transcribed into a canonical myth of “best practices.”

“Rubbish!” I say.

So, in summary, today, we don’t have a big data problem. Instead, and more exciting, I believe that we have a big data OPPORTUNITY! It is not driven by data volumes. Few companies that I work with have databases larger than a petabyte. Instead, it is driven by the opportunity to use new forms of data, and to improve the speed of decision making with increased data velocity.

 

The Real World

Yesterday, Abby Mayer and I presented an update in a webinar on the two-year research project, The Supply Chain Index. As I look at the progress of the companies that I have worked with over the last decade, the stories hop off the page. They are stories that I can never tell because of NDAs. My voice is caged by my promises to my customers.

In one peer group, there are two companies. One company is outperforming the other in very volatile times. The company that is exceeding was an early adopter of in-memory analytics for self-service by business users. This company can do “what-if” analysis and on-demand reporting. They implemented ERP early and stabilized the implementation. They are now working on the adoption of new technologies that many would call “Big Data.” The other company has implemented ERP three times, and badly. In this second company, it takes five days for a business leader to get a custom report. All of the custom reporting is a service through IT.

The first company sits on one of the largest databases of channel data in the consumer products industry. They can see daily channel data daily by item. The second company reads market data through syndicated sources. They can see more aggregated data with a two-week latency. I believe that data matters. Every company that I work with that has invested in channel data sensing tells me that it is a project that pays for itself in days not weeks. Yet, the number one question that I get is, “What is the business proposition of building a demand signal repository?”

In the real world, we don’t wake every morning knowing what questions to ask. The markets are ever-changing. We see data, observe patterns and want to learn more. The business leaders at the second company are at a clear disadvantage. I can see it in their numbers. However, it is hard to package what I see into a nice, neat ROI package to delight a CFO.

Some Context

When I think about the big data opportunity, it is not the world that we have today. Instead, it is about the advantages that we can garner in this next generation of technologies. For me, it is about data lakes, clouds and streams. The financial, insurance and e-commerce industries are leading. They are paving a way for manufacturers to rethink their processes. I want to break down the barriers for adoption. What do I mean?

  • Clouds. In supply chain circles, data clouds get the most buzz. We have seen the impact and the effects are far-reaching. It enables new business models for B2B network providers, in-memory optimization and new forms of virtualization. Concurrent optimization allows us to paint outside of traditional Advanced Planning System (APS) frameworks. It is powerful; however, this is the concept that I find the least exciting when I think about the big data opportunity.
  • Data Lakes. The ability to mine data in data lakes or pools to unearth new opportunities through new forms of analytics using nonrelational techniques is exciting. The mining of structured and unstructured data together ignites my thinking.  Today, we do not have an average customer; yet, we broad-brush markets. Our abilities to sense outside-in and to listen to the market are too limiting. For example, why are we not using Google search trends as a causal factor for forecasting? Or mining sentiment data for quality insights? The market data is there. We are not using it. Why? It does not fit into our paradigm. We are frozen in our thinking on inside-out processes that are powered by relational database thinking of traditional enterprise applications. Our functional silos are struggling to get the technologies of yesteryear to work.
  • Streams. This is the concept that I find the most exciting. Streaming data is pervasive in e-commerce and also core to the future of  supply chain technologies. Why do I feel this way? Most companies are so busy recording transactions into nice, neat rows and columns that the streams are hidden. But, encased in the data, we have order streams, shipment streams, payment streams…. The list can go on and on. The evolution of big data techniques can allow us to sense changes in these streams quickly and change course. The same principle of streaming data from a temperature sensing RFID device can be applied to streaming data within the enterprise. The ability to write APIs to data streams and the possibilities for supply chain excite me.

I believe that we are attacking many business problems too narrowly. Let me give you an example to use these three concepts. Does master data management give you a headache? We are hard-coding master data into relational systems; and as we do, we lose the context. What if we could place enterprise reference data into a data lake that can be assembled through rules-based ontologies and cognitive learning?

What Are the Barriers?

The problem is us. There are three primary obstacles for the line-of-business leader:

  1. The first barrier is that we , as manufacturers and distributors, are cheap. The great minds working on big data opportunities are working in industries where investments in technologies are viewed as mandatory to drive innovation. They are not hampered by having to have a fixed ROI with a three-year payback proven by a two-year pilot.
  2. The second barrier is that we have to retool our minds to understand the opportunity. It requires a new language and embracing new concepts. It requires education. Spend time teaching your team about the new concepts. Here are some to start with: Hadoop, Yarn, MapReduce, R, Nonrelational Data, Unstructured Data, Rules-Based Ontologies, Canonical Integration and Cognitive Learning. Business leaders need to spend time learning the concepts and brainstorming the use cases. This shift will not come from the IT department. They are too constrained with fixed budgets, mountains of requests, and traditional vendor interaction. It will also not come from discussions with the traditional vendors that you find pasted all over the airports. Instead, it is found in conferences that are primarily attended by e-commerce pure plays, home entertainment companies, financial, and insurance institutions. Form a small team to go learn about the new opportunity.
  3. The third barrier is how we think about technology. We are hard-wired to think about technology as a fixed project with a set of defined deliverables. As a result, we cannot be open to the outcome of what we can learn by testing and learning with new forms of analytics and techniques to seize the big data opportunity. The companies that are doing this well have cross-functional teams that are funded with innovation seed dollars to test new technologies against a business problem. However, there is a major difference. The projects are small and iterative. They are not the massive, large consulting projects of yesteryear. I love my podcast with Fran O’Sullivan from IBM that explained this very succinctly.

I will stop now. Room service is knocking. Coffee is in my future, and soon….

This morning, I will be presenting on the opportunity for social data in the supply chain. A subject that many of you know has been top of mind for many years. Hopefully, I will sleep tonight….

How Can We Help?

If you would like to better understand the Big Data Opportunity in Supply Chains, please join us in our quest to help others think differently. Join us in our research study on Big Data and join us at our Global Summit in Scottsdale, AZ on September 10-11, 2014. It is only 89 days away…

At this conference, a number of business leaders have asked to have a private whiteboard brainstorming session. It is private. It will be a session amongst peers to rethink the opportunity in supply chain analytics. I am looking forward to it. I hope that it is standing room only. I hope to see your face in the room….