This Recession – Supply Chain Perspective

I was reading a WSJ article discussing the key trends of this recession which is already in its 19th month. One of the key trend identified by the author was the time it is taking for the market to hit rock bottom and go for the upward swing. The author had compared the numbers with previous years.

In my opinion, one of the key reasons why it is taking longer for the market to recover is the nature of the beast itself. Today, supply chain is much more sophisticated than ever before. Even between 2001 and now, myriads of changes has taken place, pushing global supply chain network upwards on a complexity and sophistication scale.

To understand the scale of complexity, let’s consider the most popular product of current genre. The iPhone. The iPhone is a perfect example of globally networked supply chain. Tens of thousands of people at more than 30 companies on 3 continents work together to make  iPhone possible. Some of them are well known names, like Intel, which supplies the flash chips and Samsung, which makes the video processor IC. Sharp and Sanyo Epson for 3.5-inch display. Then there are the multitudes of unknowns, each of which plays a small but vital role in making iPhone assume shape.

Now, that is just one side of the picture.Another equally important aspect is the geographical distribution of the individual activities. Design of software and hardware occurs in USA. Chips are designed in Europe and USA, Production of these chips takes place in Asia from companies like Taiwan Semiconductor Manufacturing Co (TSMC)or United Microelectronic Corp (UMC).

Supply chain has also evolved to cater to this well distributed environment. Companies need to plan their product in advance, so that parts suppliers and assemblers get enough lead time to prepare. It is like a machine. When you initiate the process, it takes time but after a while, once all the wheels are in motion, it works like a charm, efficiently delivering the results. In action, delivery can be very fast and the entire chain can react very fast to cater to needs.

Now, what happens when the engine suddenly stops? Different parts of the engine comes to a halts at slightly different points in time. Considering the case of recession, primary company would have placed the orders from each supplier in 2007 January for September release. now, when the recession hit market in September (at least in the public eye), primary company would have informed its supplier to reduce the production. These suppliers need to inform their supplier to do the same. By the time all the entities slow down (and retrenched its workforce), some time has already passed.

Now, equally interesting action follows when the economy starts showing signs of recovery. Primary company informs its suppliers to start manufacturing more. But that would mean that the suppliers need to inform its own supplier for the raw material. By the time all the different parts start firing on cylinders again, together they would have added between 3 and 6 extra months before main street starts seeing the green shoots since the tipping point of market activity (hiring, increased sales etc.) gets reached. So, a recession which would have lasted 12 months in 1993 starts appearing more like a 20-24 months recession in 2009. It is interesting to note how a system laid out to increase efficiency can also lead to longer market recovery during extreme situations.

CIT and its Retail Connection

Consider this. Retail economy is 2/3rd of the overall US economy. So from pure common sense perspective, it would be wise to assume that what is good for retail is good for US. Or is it? That is the question that Congress is debating while making the decision to whether or not bail out CIT.

CIT sprang to limelight recently for all the wrong reasons. For the uninitiated, CIT is a a major lender to small and mid size businesses in USA. A significant chunk of their clients are part of Retail Supply Chain links and a failure of the bank would mean trouble for Retail Supply chain network.  CIT bankruptcy would mean a sudden lull in the lending which would potentially have a cascading effect on the retail vendors leading to a vicious domino effect.

The counter argument to not bail out the firm says let ‘Capitalism do its work’. The point argued here is that credit market in general has thawed in last couple of months and even if CIT is allowed to go under, other credit providers will assume its place and market will play itself out.

Next couple of weeks will show which way the Congress bends and would also highlight how strong the market confidence has become in last couple of months since ‘green shoots’ has started to appear.

Food Inc.

Just wached the trailer and boy did I get hooked on to it. I must watch the movie. It raises some fundamental questions against which American society has turned a blind eye.

I always used to question myself how can McD sell a burger for less than a dollar and still make money. The movie promises to answer this and many more questions. Food industry has turned into one of the largest lobby groups in America and they have significantly influenced the way we eat.

Following are some tit-bits from the trailer:

1. Our food habit has changed more in last 50 years than in last 10,000 years.

2. Supermarket has on an average 47,000 products.

3. The tomato we find in the supermarket is not tomato but an idea of a tomato.

4. If you can get a chicken grill ready in 49 days why would you grow one which takes 90 days?

And btw the answer to McD question is not by cross-selling Coke & Chips! Check out the movie website for more – http://www.foodincmovie.com/

Shelf Real Estate & Data Analytics

In the whole gamut of retail supply chain, there is only one moment of truth! The time when the customer looks at the product on the shelf and decides to buy it. As they say, its all prep work till register rings. Business wise, shelf is the only real estate property for any retailer. A place where the retailer has that unique opportunity to persuade the buyer to buy the product.
So how does a retailer do it? Traditionally couple of thumb rules has been practiced in the industry like putting the most profitable items at the eye level shelves and pushing others to the bottom. However with astronomical rise in the number of items and increase in competition among the brands becoming fierce, it is the need of the hour to assign right shelf spec to the right set of products so as to meet the underlying business objectives. These objectives can be as varied as minimizing the cost, Maximizing the sales, Increasing the assortment or a combination thereof. The skill is in arriving at that right number of items and variety to be placed on the shelves to meet these rules.

Now it may all sound very simple but the complexity of the problem lies in numbers. Considering the number of items that a category manager has to carry, the possible combinations of facings of the items that can be placed on the shelves is mind bogglingly high to be analyzed by a human brain. This is where data analytics can help to process the information at incredible speeds to arrive at the desired solution. This is a perfect opportunity area for human – computer participation where the computer’s data analytical skills supplemented with category manager’s business know-how can do wonders!

Does Etail Has a Future in India?

This is a difficult one to answer. On the face value, it may appear that online retailing is for the neo-rich upwardly mobile variety who are always hooked on to the internet and love to explore the new trends.

Well- that is true but that is only the tip of the iceberg. There is also a majority of population who can benefit from online retailing if it is done right. These are the average low and middle class segment which form the majority of indian population. The reason being twofold.

One – the bad state of physical infrastructure and increasing real estate prices leading to a scenario where customers have to pay more and have a less than satisfying experience at the retail stores. Think of the number of times you curse yourself while standing in a queue in Big Bazaar.

Two – The death of distance! While shopping online, the distance is of insignificant importance. It works favorably well for India where the travel is a big pain. Roads are in bad shape, pollution and traffic on the road is mind numbing etc. In these situation, having no worry to travel is a big boon. This is where etailing has a huge prospect.

So why its not happening? Well, first of all the etailing model have to be tuned towards the local needs. For e.g. – availability of information in regional languages to reach the masses. efficient logicstics operations to make sure delivery happens quickly. Measures like this will put the etail shops on the mainstream and industry will swing along the positive feedback loop.

Know Your Store

Perhaps every retailer understands that not all their stores are created equal. Due to demographical and many other socio-economical differences, sales pattern varies dramatically. So – why is it that most of the retailers still have one- size-fit-it-all assortment mix and merchandise plan?

The unpleasant truth is, its really hard to intelligently segment the stores into manageable chunks which makes business sense.

Guess what? With advent of technology in the field of Business analytics, it is now well within the boundaries of reality to achieve the holy grail of merchandising. However, to achieve it, there are two fundamental tenets that one must adhere to.

1. Data don’t lie

2. make sure your data is speaking the truth

However much these two may sound contradictory, they are both correct. Let’s see how.

When I say data don’t lie, what I mean is that data is not biased with pre-conceived notions. If intelligent analysis is applied on top of it, it can show key insights. For e.g. – a good data mining algorithm can identify the sales patterns and help group the stores which have similar propensity to sales in the same bucket. The benefit is, such type of analysis is completely unbiased and can at times help in correcting the conventional wisdom.

Now, that being said, it is also important that the data is captured in clinical condition and free of any type of corruptions like negative sales, missing stales information etc. If the data is not cleansed it is easy to understand that all the subsequent analysis will have inherent errors which may grow as we delve deeper. Always remember the GIGO law of computing – ‘Garbage in – garbage out’.

Now assuming that both the fundamentals have been adhered to, there are several exciting things that can be done. for the scope of this article, let’s just talk about store grouping. You can use the data mining packages to identify the group of stores which have similar sales profile. Once you have the list, you can use it for any number of things. Following are some examples that immediately come to my mind:

1. Specific assortment mix for each logical group of stores

2. Localized advertising plans for each cluster. for e.g. – Hispanic heavy ADs in Hispanic heavy stores.

3. Reorganization of distribution and item authorization plans to minimize the inventory and logistics cost

4. Focused merchandise plans for each chunk

It is obvious that the application of such a bottom – up store grouping process is only limited by the creativity of the users. So go ahead and figure out what you want to do with your set of store groups.

A Problem of Plenty

I’m sure many of you would have felt that with too many television channels around, it has become really difficult to focus and follow your favorites? Similarly with the explosion of content on World Wide Web, it has become far more cumbersome to zero – in on the information you desire. Guess what, there is a similar trend taking place in the product assortment at the store.

Per latest consumer research performed in UK by Consumer Marketing Capitalism, more and more people are complaining about having too many variants of the same brand leading to increased confusion and at times frustration in consumers’ mind.

If these findings are to be believed, it is probably detrimental for the brand managers to have myriad number of variants of the same brand on the shelf. Probably each brand has a tipping point beyond which the extra number of sub-Brands cannibalizes on their cousins anyway. What is significant is for the Brand managers to identify the tipping point and single out those sub brands who together supplement the sales rather than eating into each others share.

Similarly the category managers at the Retail end have an equally important role to play. Considering the proximity to POS data, they should pick out the significant few and get rid of problematic plenty. This will lead to increased profit as well as improved customer satisfaction.

Retail Analytics 2.0

Retail analytics is nothing new. However with recession  tightening its grip on economy, retailers are leveraging more and more analytics to gain new insights and identify trends and patterns which define their business.  The idea to gain an ability to make smarter decisions and manage the business more effectively based on data was always an exciting one but recession has taken retail analytics from the realm of being ‘exotic science’ to a ‘must have business engine’ which is part of the survival kit in the current economic downturn.

I call it retail analytics 2.0. In my opinion, 1.0 version was the industry’s first brush with maths and science based data analytics championed by companies like A.C. Nielsen which helped them analyze their data to identify market trends and such using rule base engines. 1.0 was also pretty much adopted by the top tier retail firms  and largely ignored by tier 2 & 3 companies.

However 2.0 has a much wider footprint with small and medium size embracing it with both hands. There are two key factors in my opinion which has led to this change. One, retail analytics technology has matured significantly in last couple of years providing smarter and more intelligent insights thus proving their effectiveness to the business community. Two, the growth and success of early adopters has inspired enough confidence in smaller firms to pursue analytics with much more enthusiasm.

2.0 also means that the nature of firms who are in the business of working on data analytics domain has undergone a metamorphosis of sorts. Big firms are still the leaders but many new nimble players have entered the market with the idea to provide a service oriented approach to data analytics recognizing that each problem is unique and there is no one size fit all solution.

It would be interesting to see how this space further evolves but it is fair to say that retail analytics has successfully made the transition from being a supporting fulcrum to become the cornerstone of the business decision making in retail arena.