Highlights: Enhanced Tanpin Kanri is a specific example of
Intelligence Augmentation. Store staff's local knowledge and engagement with their shoppers cannot be
replaced; but Big Data & Analytics can provide a significant leg-up in
their difficult job of hypothesis-generation by providing data-driven
predictions that they can safely rely on and improve incrementally. In a highly
competitive low-margin business such as fast moving consumer goods retail,
pioneering use of IA in their operations will determine the winners.
Dr. PG Madhavan is
the Founder and Chairman of Syzen Analytics, Inc. He developed his expertise in
analytics as an EECS
Professor, Computational Neuroscience researcher, Bell Labs MTS, Microsoft
Architect and startup CEO. He has been
involved in four startups with two as Founder. He has over 100 publications
& platform presentations to Sales, Marketing, Product, Industry Standards
and Research groups and 12 issued US patents. He conceived and leads the development of SYSTEMS Analytics
and is continually engaged hands-on in the development of
advanced Analytics algorithms.
Artificial Intelligence or “AI” is the technology of the
future; it has been so for the past 50 years . . . and it continues to be today!
Intelligence Augmentation or “IA” has been around for as long. IA as a paradigm
for value creation by computers was demonstrated by Douglas Engelbart during
his 1968 “Mother of all Demos” (and in his 1962 report, “Augmenting Human Intellect:
A Conceptual Framework”). While Engelbart’s working demo had to do with the
mouse, networking, hypertext, etc. (way before their day-to-day use), IA has
increased in scope massively in the last 5 plus years. Now, Big Data & Analytics can truly augment human intelligence beyond anything Engelbart could
have imagined.
IA is contrasted with Artificial Intelligence which
in its early days was the realm of theorem proving, chess playing, expert
systems and neural networks. AI has made large strides but
its full scope is yet to be realized. IA on the other hand can be put to
significant use and benefit today. Such is the story of Enhanced Tanpin Kanri
in retail commerce.
As a keen observer of the retail ecosystem’s demand chain portion for the past 3 years or so, I am struck by the inefficiencies in large portions of Retail. Shoppers finding what they want on the shelf is considered a BIG problem in the industry (the so called “OOS problem” or Out-Of-Stock problem) – to the tune of $170 Billion per year! The following diagram illustrates the portion of Retail ecosystem on which we will focus in this blog.
The left half represents the current dominant model.
“Push” model drives manufacturer’s FMCG (fast moving consumer good from
“brands” such as Procter & Gamble, Kraft, Unilever and others) into the
supply chain with store shelves as the final destination. As retailers move to
the right half of the picture, they tend to be more agile and mature in their
practices and are seeking a competitive edge through differentiation – they find
that by focusing their store operations to satisfy the LOCAL customer via making
available what she prefers on the store shelves, they can win. The poster-child
of this revolution is 7-Eleven, the ubiquitous store at virtually every street
corner around the globe!
7-Eleven’s use of the “Pull” model since early 2000’s has
been very successful as captured in a case study at Harvard Business School in
2011 (HBS Case Study: 9-506-002 REV: FEBRUARY 23, 2011). Quoting from this
study, “Toshifumi Suzuki, Chairman and
CEO of Seven and I Holdings Co., was widely credited as the mastermind behind
Seven-Eleven Japan’s rise” and goes on to say that ‘Suzuki’s emphasis on
fresh merchandise, innovative inventory management techniques, and numerous
technological improvements guided Seven-Eleven Japan’s rapid growth. At the
core of these lay Tanpin Kanri, Suzuki’s signature management framework’.
Proof is in the pudding – “Tanpin Kanri has yielded
merchandising decisions that has decreased inventory levels, while increasing
margins and daily store sales” since the 2000’s, the HBS case study points out.
So what exactly is Tanpin Kanri?
Tanpin Kanri or "management by
single product," is an approach to merchandising pioneered by 7-Eleven in
Japan that considers demand on a store-by-store and product-by-product basis.
Essentially, it empowers store-level retail clerks to tweak suggested
assortments and order quantities based on their own educated hypotheses . .
.
You can tell that Tanpin Kanri lies well to the right
in the Retail Ecosystem diagram above. To call out some features:
·
PULL model
·
For a buyers’ market
·
Focus on How to satisfy customer
·
Item planning and supply driven by retailer and
customer
·
Symbolic of Consumer Initiative
I believe that it is simply a matter of time before Tanpin
Kanri variants dominate the Retail demand chain model. As shoppers
clamor even more for their preferences to be made available, Retailers will evolve
incrementally.
So, where does the PULL Model provide most bang for the buck
today?
Where ever Product Density (number of products to be
stocked per unit area) is high, “customer pull” will help prioritize what
products to stock. Today’s Tanpin Kanri at 7-Eleven Japan accomplishes
“customer pull” incorporation via super-diligent store staff manually making
the choices.
Here are the “hypothesis testing” steps that 7-Eleven store staff goes through
in operationalizing Tanpin Kanri. Based on frequent interactions and personal
relationships with the shoppers at a store, the staff generates hypotheses of
the shoppers’ needs, wants and dislikes. Based on such information, store staff
formulates hypotheses of what to carry (or not) on their store shelves (“merchandising”).
Sales during the following days and weeks allow them to ascertain if their
hypotheses should be rejected or not; this continuous iteration goes on over
time to “track” shopper preferences. Clearly, Tanpin Kanri methodology has been
highly successful for 7-Eleven according to HBS case study.
IA based on Big Data & Analytics can play a major
role in Tanpin Kanri. In any scientific methodology, coming up with meaningful
hypotheses is the HARD part! In Tanpin Kanri case, it is the personal
relationships, diligence and intelligence of the store staff that help generate
the hypotheses. This is the super-important human value-add that no AI can fully
replace! However, we can AUGMENT the hypothesis-driven Tanpin
Kanri with Data-Driven precursor that enhances staff intelligence by
providing them with predictions that they can build on to formulate their
hypotheses.
IA happens in the data-driven precursor step. 7-Eleven has vast amounts of transaction and customer data in their data warehouse. They can be “data-mined” to find shopper preferences at a particular store which can form the basis of what to carry and how much on the store shelf. The data-driven predictions then become the “foundation” on which the store staff adds their own “deltas” based on the shopper quirks that they have surmised through their all-important personal relationships.
Syzen Analytics, Inc. has accomplished IA integration using
Machine Learning and a new development in Analytics called “SystemsAnalytics”. A dumb “prediction” for SKU shares is same sales as last year (see
the multi-colored bar chart in the middle of the picture below) – in other
words, historical sales is the “information-neutral” prediction. But surely, we
can do better than that with Systems Analytics.
Syzen is able to provide SKU-by-SKU, store-by-store and
week-by-week predictions using typical T-Log data that every Retailer has in
its data archives. A typical prediction of Syzen’s ROG-0 SaaS product for a
typical SKU at a particular store looks like this.
·
The purple uneven “picket fence” is the weekly
predictions – lowest bar chart. This is obtained by combining different
“masks”.
·
The papaya-colored mask is the new and most
significant one. The values are predicted based on appropriate past intervals
of T-Log data digested via Systems Analytics and updated adaptively.
·
The numbered masks in the middle accounts for
things that the store manager knows that will happen next year such as a local
festival or a rock concert in the nearby park.
·
The MANUAL part of Tanpin Kanri now only involves
the store staff simply making daily small adjustments to the SKU “facings”
based on local shopper “gossip” to the purple bar chart!
Convenience stores are drawn to the Enhanced Tanpin Kanri
method because of the maturity of operations they already possess. With more
agile supply chains and the desire to differentiate their stores in response to
their local clientele, Syzen finds a lot of enthusiasm among “high-density”
Retailers for our predictive solution that makes Tanpin Kanri more scalable due
to lesser dependency on super-diligent store staff. Advances in Systems
Analytics and other quantitative methods will refine products such as Syzen’s
ROG-0 SaaS in the future to sharpen shopper-preference based product assortment
predictions.
Enhanced Tanpin Kanri is a specific example of Intelligence
Augmentation. Store staff's knowledge of local happenings and engagement with their store shoppers cannot be replaced; but Big
Data & Analytics can provide a significant leg-up in their difficult job of
hypothesis-generation by providing data-driven predictions that they can safely
rely on and improve incrementally. In a highly competitive low-margin business
such as fast moving consumer goods retail, pioneering use of IA in their
operations will determine the winners.
More on ROG-0 SaaS: http://advancingretail.org/product/syzen-rog-0-saas/
Syzen website: www.syzenanalytics.com
