Throwing Down the Gauntlet

by Lora Cecere on June 14, 2014 · 0 comments

“We live in a world where supply chains, not companies, compete for market dominance. But companies often have diverging incentives and interests from their supply chain partners, so when they independently strive to optimize their individual objectives, the expected result can be compromised. ”

Hau L Lee, Triple-A Supply Chains, Harvard Business Review, October 2004

“The idea of the value chain is based on the process view of organizations, the idea of seeing a manufacturing (or service) organisation as a system, made up of subsystems each with inputs, transformation processes and outputs. Inputs, transformation processes, and outputs involve the acquisition and consumption of resources – money, labour, materials, equipment, buildings, land, administration and management. How value chain activities are carried out determines costs and affects profits.”

Institute for Manufacturing, 2013

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Tipping points are fascinating to watch. They are even more fun to create.

I want to be part of the evolution that helps leaders to redefine strategies based on the changing physics, electronics and capabilities in value networks.

Tonight, I am stranded in a hotel in Chicago. Facing a string of canceled flights yesterday at O’Hare airport, instead of fighting the mayhem, I opted to pull my bags to the hotel across the street.  It is 3:00 AM. The Chicago airport is closed for the night. It is quiet and a good time to think. So, on this Friday night, I find myself typing away on my keyboard on what is another sleepless night.  So, bear with me as I throw down the gauntlet for the high-tech value chains to seize new levels of effectiveness.

What Is Value?

When I wrote the book Bricks Matter, I cavalierly penned a chapter on the evolution of supply chain thinking from cost to value. It sounds simple, but it is not. I found this out the hard way when Keith Harrison, contributor to the Forward of the book, asked me to define what I meant by ”value.” Keith is the former Global Product Supply Officer for P&G, and a person that I greatly respect. So, I swallowed hard and began the discussion.

It is one thing to write it, but it is a horse of another color to defend it. I think about this discussion with Keith often as I work on the Supply Chain Index and edit the chapters of Metrics That Matter.

I believe that value is what you create. You know when you have created value when it drives improved brand perception, increased sales or improved market capitalization. Easier said than done. You might say…. Yes, I agree.

How do you know when you have created it? And more importantly for supply chain leaders today, how can we create new levels of value from new business models that are happening based on the tipping points within the extended supply chain?

Let’s Take a Hard Look at Value Chains

To understand value creation, we have to understand how companies have made trade-offs. In Figure 1, I share a composite orbit chart of progress of Cisco Systems, Intel, Samsung and Flextronics on the Effective Frontier at the intersection of inventory turns and operating margin for 2006-2012. What can we learn? I think three things:

1) A Hard-Fought and Tough Journey. This is a group of leaders. I have great respect for each of them. However, no company in this chart is on a linear path towards improving both margin and inventory turns. Instead, it is a gnarly road with each company struggling to make trade-offs.

2) Efficient Supply Chains, Not Effective Networks.  Each company operates within its own plane, improving its own potential. We are still very early in the true adoption of value chain concepts. Our current processes and dependencies on Excel spreadsheets cannot get us to our goal.

3) Risky Business? Contract manufacturers operate at low margins and lack resiliency. The value chain depends on the contract manufacturers to drive value, but the lack of stability of the business model is a risk for the system.

Figure 1. High-Tech and Electronic Supply Networks

The investment in technologies has made companies more efficient, but not more effective. This is an important distinction. Why? All of the companies in the chart have improved revenue/employee.

Across the industries, this is the case. Our historic supply chain practices have made companies more efficient, but we have not made them more effective. Sadly, we also need to admit that we have not progressed very far on the creation of value networks. We have talked the concepts, but not enabled the processes. So, my question this morning as the sun comes up is, “Could we?

But, Could We?

My thought is yes. I think that this industry is poised for a tipping point. As we think about this value network, and the potential redefinition through new business models, I think that we are ready to change the equation. Three things are happening, that I think are significant:

  • Flextronics has invested in the development of Elementum. This is a new start-up in the B2B network technology space.
  • E2open last week announced the purchase of Serus. This purchase increases E2open’s capabilities for visibility into the processes of the outsourced semiconductor network of foundries.
  • Kinaxis successfully orchestrated an IPO. New money into Kinaxis, a wider portfolio for E2open and the evolution of a new player with Elementum could change the equation.

As we move forward, I think that it is important to take a hard look at Figure 1 and ask ourselves the question, “How can we drive greater value into this value network through more effective data sharing, market sensing and translation and the enablement of the digital supply chain?” I am stepping forward to throw down the gauntlet.

My Advice:

The pace of change in this value network is increasing rapidly. As a result, we need to enable the moments of truth in this value chain. This includes the decision to build, the digital definition of the product, the decision of when and where to ship, and the decisions of which materials to put into which products. It is about more than visibility. It is not about the automation of yesterday’s processes into the cloud. Let’s work backwards on the announcements:

  1.  Kinaxis needs to briefly congratulate themselves—it was a tough fight—and then move on. It is time to move to a many-to-many data model. The Kinaxis model is an enterprise solution. It lacks the community infrastructure and the canonical integration layer of E2open. The current work on data sharing and control tower for Kinaxis is inside-out, not outside-in. It is an enterprise solution, not a network solution. As a result, it works well for a singular company connecting with its trading network; but, not with the interaction needs of the greater community.
  2. E2open needs to better define the value proposition, and the go-to-market messaging, and continue to add value at the application level. They have fought a hard fight to evolve through a turbulent decade. It is now time to build-out the model.
  3. Elementum is the new kid on the block. It is important for Elementum to build a community. It needs to be more than Flextronics.

So, as we think about the drivers for the tipping points—and the coalescence of new forms of analytics with big data systems, 3D printing, and the Internet of Things—there is a need in this value network to quickly to automate the supply chain moments of truth. This is the clear articulation of when and how to make, source, and deliver for the community. It needs to be multi-tier and many-to-many.

Without this, the players are stuck. Their current performance is stalled, and their interdependencies are too great to not improve through automation. It is about the network, not the enterprise. It is about the new definition of the digital supply chain. It is about new models.  This is the challenge.

So, here, I throw down the gauntlet. The race is on. Let’s see who delivers. I look forward to your thoughts.

How to Learn More:

 At Supply Chain Insights we are having fun exploring and understanding these trends.

We are currently doing research on the evolution of new forms of analytics in the hype cycle that many people refer to as Big Data, and we are completing our survey on digital manufacturing. We would love to hear from you. As with all our research, when you share with us, we share with you. If you respond to one of our surveys, we keep all of the responses confidential and only report the findings in aggregate.

In the Big Data research study, we are analyzing the adoption rate of Big Data Analytics of concurrent optimization, streaming data, cloud adoption, and cognitive learning. For more on this tipping point, please see my last blog post Dreaming of Clouds, Lakes and Streams. When it comes to digital manufacturing, it is a new world combining the Internet of Things with 3D Printing. We will be showcasing case studies of both new forms of analytics and the use of 3D printing at our upcoming Supply Chain Insights Global Summit on September  10-11, in Scottsdale, AZ. 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….