Ava Ex Machina and the Retail Supply Chain

There is a lot of buzz about the “autonomous” supply chain these days.  The subject came up at a conference I attended where the theme was the supply chain of 2030.  But, before we turn out the lights and lock the door to a fully automated, self-aware, supply chain “Ava Ex Machina”, let’s take a moment and put this idea into some perspective.

The Driverless Car Analogy

From CBC News https://www.cbc.ca/news/business/driverless-cars-stratford-1.3390279

I’ve heard the driverless vehicle used as an analogy for the autonomous supply chain.  However, orchestrating the value network where goods, information and currency pulse freely, fast, and securely between facilities, organizations, and even consumers, following the path of least resistance (aka the digital supply chain), may prove to be even more complex than driving a vehicle.  Digital technologies, such as additive manufacturing, blockchain, and more secure IoT infrastructure, advance the freedom, speed and security of these flows.  As these technologies makes more automation possible, as well as a kind of “autonomy”, the difficulty and importance of guiding these flows becomes ever more crucial.  

Most sixteen-year-old adolescents can successfully drive a car, but you may not want to entrust your global value network to them.

Even Elon Musk says that Tesla autopilot will never be perfect.

Before you can have an autonomous supply chain, you need to accelerate the Detect, Diagnose, Direct Cycle – let’s call it the 3-D Cycle, for short, not just because it’s alliterated, but because each “D” is one of three key dimensions of orchestrating your value network.  In fact, as you accelerate the 3-D Cycle, you will learn just how much automation and autonomy makes sense.

Figure 1

Detect, Diagnose, Direct

The work of managing the value network has always been to make the best plan, monitor issues, and respond effectively and efficiently.  However, since reality begins to diverge almost immediately from even the best plans, perhaps the most vital challenges in orchestrating a value network are monitoring and responding.

In fact, every plan is really just a response to the latest challenges and their causes.

So, if we focus on monitoring and responding, we are covering all the bases of what planners and executives do all day . . . every day.

Monitoring involves detecting and diagnosing those issues which require a response.  Responding is really directing the next best action.  That’s why we can think in terms of the “Detect, Diagnose, Direct Cycle”:

1. Detect (and/or anticipate) market requirements and the challenges in meeting them
2. Diagnose the causes of the challenges, both incidental and systemic
3. Direct the next best action within the constraints of time and cost

The 3-D Cycle used to take a month, in cases where it was even possible.  Digitization – increased computing power, more analytical software, the availability of data – have made it possible in a week.  Routine, narrowly defined, short-term changes are now addressed even more quickly under a steady state – and a lot of controlled automation is not only possible in this case, but obligatory.  However, no business remains in a steady state, and changes from that state require critical decisions which add or destroy significant value.   

You will need to excel at managing and accelerating the 3-D Cycle, if you want to win in digital economy.

There is no industry where mastering this Cycle is more challenging than in retail, but the principles apply across most industries.

 

Data Is the Double-edged Sword

The universe of data is exploding exponentially from growing connections among organizations, people and things, creating the need for an ever-accelerating 3-D Cycle.  This is especially relevant for retailers, and it presents both a challenge and an opportunity for competing in the digital economy with a digital value network.  Redesigned, retail supply chains, enabled with analytics and augmented reality, are not only meeting, but raising consumer expectations.

Figure 2

Amazon’s re-imagination of retail means that competitors must now think in terms of many-to-many flows of information, product, and cash along the path of least resistance for the consumer (and not just to and from their own locations).  This kind of value network strategy goes beyond determining where to put a warehouse and to which stores it should ship.  Competing in today’s multi-channel world can mean inventing new ways to do business, even in the challenging fashion space – and if it is happening in fashion, it would be naive to think rising consumer expectations can be ignored in other retail segments, or even other industries.  Consider a few retail examples:

Zara leverages advanced analytics, not only to sense trends, but also to optimize pricing and operations in their vertically integrated supply chain.

Stitch Fix is changing the shopping model completely, providing more service with less infrastructure.

Zolando has been so successful in creating a rapid response supply chain that they are now providing services to other retailers.

Nordstrom, of all organizations, is opening “inventoryless” stores.

Walmart has been on an incredible acquisition and partnership spree, recently buying Flipkart and, as early as two years ago, partnering with JD.com.  And, then, there is the success of Walmart.com.

Target is redesigning the way their DC’s work, creating a flow-through operation with smaller replenishment quantities.

Yet, many companies are choking on their own ERP data, as they struggle to make decisions on incomplete, incorrect and disparate data.  So, while the need for the 3-D Cycle to keep pace grows more ever more intense, some organizations struggle to do anything but watch.  The winners will be those who can capitalize on the opportunities that the data explosion affords by making better decisions faster through advanced analytics (see Figure 2).

The time required just to collect, clean, transform and synchronize data for analysis remains the fundamental barrier to better detection, diagnosis and decisions in the value network.  A consolidated data store that can connect to source systems and on which data can be consolidated, programmatically “wrangled”, and updated into a supra data set forms a solid foundation on which to build better detection, diagnosis, and decision logic that can execute in “relevant time”.  This can seem like an almost insurmountable challenge, but it is not only doable with today’s technology, it’s becoming imperative.  And, it’s now possible to work off of a virtual supra data set, but that’s a discussion for another day.

 

Detect, Diagnose and Direct with Speed, Precision & Advanced Analytics

Detection of incidental challenges (e.g. demand is surging or falling based on local demographics, a shipment is about to arrive late, a production shortfall at a vendor, etc.) in your value network can be significantly automated to take place in almost real-time, or at least, in relevant time.   Detection of systemic challenges will be a bit more gradual and is based on the metrics that matter to your business, such as customer service, days of supply, etc., but it is the speed and, therefore, the scope, that is now possible that drives better visibility from detection.

Diagnosing the causes of incidental problems is only limited by the organization and detail of your transactional data.  Diagnosing systemic challenges requires a hierarchy of metrics with respect to cause and effect (such as the SCOR® model).  Certainly, diagnosis can now happen with new speed, but it is the combination of speed and precision that makes a new level of understanding possible through diagnosis.

With a clean, complete, synchronized data set that is always available and always current, as well as a proactive view of what is happening and why, you need to direct the next best action while it still matters.  You need to optimize your trade-offs and perform scenario and sensitivity analysis.

Figure 3, below, shows both incidental/operational and systemic/strategic examples for all three dimensions of the 3-D Cycle.

Figure 3

Speed in detection, speed and precision in diagnosis, and the culmination of speed, precision and advanced analytics in decision-making give you the power to transpose the performance of your value network to levels not previously possible.  Much of the entire 3-D Cycle and the prerequisite data synchronization can be, and will be, automated by industry leaders.  Just how “autonomous” those decisions become remains to be seen.

Fortunately, you don’t need Ava Ex Machina, but your ability to develop a faster and better (and, I will even say more autonomous) 3-D Cycle is fundamental to your journey toward the digital transformation of your value network.

The basic ideas of detecting, diagnosing and directing are not novel to supply chain professionals and other business executives.   However, the level of transparency, speed, precision and advanced analytics that are now available mandate a new approach and promise dramatic results.  Some will gradually evolve toward a better, faster 3-D cycle.  The greatest rewards will accrue to enterprises that climb each hill with a vision of the pinnacle, adjusting as they learn.  These organizations will attract more revenue and investment.  Companies that don’t capitalize on the possibilities will be relegated to hoping for acquisition by those that do.

Admittedly, I’m pretty bad at communicating graphically, but I’ve attempted to draft a rudimentary visual of what the architecture to support a state-of-the-art 3-D Cycle could look like (in Figure 4 below), as a conceptual illustration for facilitating discussion.

The convergence of cloud business intelligence (BI) technology and traditional advanced planning solutions supports my point, and that is definitely happening.

As the unstoppable train of time pulls us into the weekend, I leave you with this thought to ponder: 

“Life is short, so live it well, in gratitude, honesty and hope, and never take it for granted.”

Figure 4

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Guest Post: Big Data Is Getting Bigger! Are Retail Companies Ready?

This month’s McKinsey Quarterly carries an article, “Are You Ready for the Era of Big Data?”.  In contrasting two competitors, it states the following:

“The [One] competitor had made massive investments in its ability to collect, integrate, and analyze data from each store and every sales unit and had used this ability to run myriad real-world experiments.  At the same time, it had linked this information to suppliers’ databases, making it possible to adjust prices in real time, to reorder hot-selling items automatically, and to shift items from store to store easily.  By constantly testing, bundling, synthesizing, and making information instantly available across the organization—from the store floor to the CFO’s office—the rival company had become a different, far nimbler type of business.”

This week’s guest post explores what retailers need to do in order to take advantage of “big data”.  The author is Özgür Yazlalı, one of my former colleagues and a gentleman who has years of experience helping retailers significantly improve decisions that directly impact financial results through data-driven analysis.  I greatly appreciate Özgür’s contribution and look forward to more in the future.

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As an internal consultant in a Fortune 500 retail company, one of the interesting transitions I have witnessed was the significant change in all dimensions of available data.  Day by day, not only more granular data but also more categories of data have become increasingly accessible to planning functions.

The retail store remains the closest link to the end customer of supply chains – the consumer.  Leveraging this connection requires collecting tremendous amounts of very detailed transaction data.  Consider the following examples:

– Customer:  “For this very random reason, I’ll rightfully return this product that I don’t remember from which store I bought”.
– Retailer:  “Ooops, we cannot see how much we charged you for this product, so we’ll credit the ticket price back to you.”

The emergence of social electronic media has multiplied the complexity and volume of interaction between consumers and retail stores.  Smart phones, social networks, image processing, and cloud computing have become reality in our everyday lives, further illustrating the relentless and ubiquitous nature of Moore’s Law.

The “big data” phenomenon is a great opportunity for the manager who knows how to utilize it.  The possibilities of localized assortment planning with reliable store-product category forecasts, localized pricing, and demand planning with social networks and web-based trends are now very real.  The limitation lies neither in the analytical capability nor the existence of reliable data, but rather in the legacy planning systems.

This phenomenon can also be a curse if all you know about data is limited to spreadsheet programs.  My humble observation is that there is now an ever-expanding gap between the rate of increase in the data and the spreadsheet capabilities of individual users.  Getting the big picture could now be a difficult task.  Without a better way to address this gap, you risk being limited to analyzing specific events and/or drawing conclusions based on a limited sample.

Given the increasing gap, the traditional way of building IT solutions based on business requirements documents and restricted interaction is no longer viable.  Big data requires cross-functional and data-capable analytical teams that operate as intermediary functions between business and IT organizations.  This is not a team simply put together from ex-business and ex-IT folks, but data scientists, optimization experts, experienced data and business analytics consultants that could unleash the capabilities of SQL and spreadsheets together.  Such teams not only facilitate the discussions between the two organizations but also enhance a tool design process that is more interactive with rapid innovation and prototyping.  This is critical because no one really knows a priori what assumptions and models will consistently work.

Most companies are organized as silos of functions, as are their data sets (though IT might store these datasets in the same server).  Thus, in addition to enhanced IT-business interaction, these analytical teams could work with different business functions that do not routinely communicate, leveraging data as common ground.  For example, the inventory management teams that use sales and inventory data may not know about, or may choose to ignore, trends in store traffic that consumer insights teams usually track.  Yet, an integrated analysis of these two data sets could reveal that a decreasing sales trend in a well-inventoried store is being driven by poor assortment, even if there is sufficient traffic.

Big data presents significant challenges and opportunities to businesses.  Cloud-based, functionally local solutions may help individual business functions maintain their own small data warehouses, but an holistic approach demands greater IT involvement.  Unhappily, IT organizations are not often seen as centers of innovation.  A team of skilled, experienced analysts with access to both the computational power owned by IT and the big data in which other business functions find themselves awash provides an effective means for overcoming the challenges and delivering on the opportunities of big data.  These analytical teams can be a part of the IT organization, but whatever organizational structure is employed, they must have powerful computing resources, access to all relevant data, and a voice with decision-makers. [Editorial Note:  A hyper-performance, secure, could-based platform would be the natural vehicle for data of all types from all sources where challenges and opportunities can be identified, diagnosed and the next best action determined and directed.]

In summary, big data is now more accessible, and companies must continuously explore new ways of using it to increase profitability and market share.  For retailers in particular, these opportunities are far greater than for any other industry because of both their proximity to consumers and the availability of structured data (social networks, CRM, POS, inventory, traffic, e-commerce).  Integrating all this valuable information into a predictive [Editor’s note:  and prescriptive] planning process is a difficult task requiring tighter linkage between the IT and business functions.  Analytical consulting teams with enhanced data-capabilities who could facilitate and guide this interaction are now more important than ever in achieving this goal of increased profitability and market share.

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Thanks again for dropping by.  If you liked this week’s guest post, please rate the piece and feel free to leave a comment.  Until next week, remember the words of the American poet, Harry Kemp who said, “The poor man is not he who is without a cent, but he who is without a dream.”

Have a great weekend!