Tag: forecasting
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Value of SKU Intelligence (What Are Customers Buying?)
One of the most frequent questions we are asked about Emcien’s methodology is “why hasn’t anyone done this before?” The answer seems obvious to us, but we should probably write it down once and for all so we can just point people to a definitive document.
Exploding SKU’s to attributes generates a LOT of data. It is only useful if this translates to actionable intelligence. As we all know, we don’t need more data. We need actionable items and recommendations to improve business. The simplest answer is that collecting all the attribute data for SKUs has not been done before, because the algorithms have not existed to farm intelligence from them. In this blog I am going to address –
- Why having the data with the SKU attributes is invaluable!
- What analytics capabilities are needed, if you have that data
- Value of having SKU Intelligence (To drive SKU velocity)
Consider a product that has attributes, and offers lots of variety, also called configurations. Almost all products come under that class today! Think car, computer, or cell phone. The product has features, and each feature has alternative options. For example, a car has an engine (V-6 or V-8), a body type (sedan or convertible), and a color (red, green, blue, black, or white). Any specific car has many choices for each of the features. Customers make choices on the features. Do you want the cloth seats or the leather seats? Do you want the DVD player? How about the iPod connector? Make a choice for about 30 features and you are done. This applies for shampoo, toothpaste, computers, light fixtures, consumer electronics, … all products that have attributes choices.
We consider a configurable product that has a large number of features, each of which many alternative choices/options. Notice that we have finessed the hierarchy problem by allowing only two levels: feature and option. A customer will typically call out only a few attributes during a purchase. They expect you to know how you can complete that spec to fill the order. That is the single biggest opportunity at every point of sale!
If we have data with attributes for every SKU, we can begin to talk about buying patterns! A buying pattern is groups of options that are bought together, like the red color with the convertible body style. Or the DVD player with the leather seats. Or the pattern might involve 3 options, 4 options, 5 options…. many options.
Auto detect most popular attributes in fastest sellers
This brings me to a very important point. If you have sales data with attributes, you need an analytics engine that will automatically detect and tell you what attributes are bought together and are highly popular. A reporting tool that makes you query every choice combination will NOT work! You will be very old by the time you get an answer to most popular choices, as there are millions of attribute combinations. Emcien offers an analytics engine with cluster analysis that will tell you what attribute groups are popular. This answers the questions – what are customers buying and what attributes are popular. This knowledge can also be used for planning the SKU definition and knowing what products need to be on the top landing pages of your web site/ store aisles. (SKU Definition is the list of attributes in the SKU.)
Once the SKU definition is in place, 75% of the cost structure and efficiency of your supply chain has been fixed ! Your supply chain operates under the assumption that the SKU definition is correct. What does this mean? This means that if you offer 2 SKUs with slightly different attributes, that could have been consolidated into one, the supply chain will suffer that cost and inefficiency. The SKU definition has to be optimized before you send it into the supply chain. This is a key driver to SKU inventory.
Customers buy products based on choices at the attribute level. If you cannot gather demand intelligence at the attribute level, you are out of touch with your customer. Customers DO NOT buy SKUs. They do not know the SKU numbers, and they do not care.
SKU Challenges Based on Supply Model
Your business falls into one of these categories based on your supply model (see table). Knowing what attributes customers are buying can dramatically improve your demand response. You will be able to improve -
- SKU definition -This means knowing how many SKUs you need and what needs to be in the SKU
- Demand Forecasting – You will be able to forecast demand at the attribute level, which is the level that customers are buying
- SKU Inventory - plan what to stock to have highest turns.
- Sales Productivity and Efficiency – If you know what attributes are selling together, you can implement an automated recommendation engine (EmcienMatch) and your sales rep can recommend a good choice to the customer during the ordering process. This is probably the single biggest value of knowing what attributes customers are buying. Your sales reps are a trusted advisor to your customers. If the sales person know that all customers of a certain type bough a particular configuration, he could recommend that choice to the customer. Win! Customer is happy because he done! Sales person is happy because he looks smart! You are happy because you increased your sales repeatability!
For our Nerdy Math readers, here is technical nugget that you will love:
SO – here is another reason why what Emcien does has not been done before. Statistics deals with numerically valued variables. A numerically valued random variable X has a domain that can be classified as a ratio scale, an interval scale, an ordinal scale, or a nominal scale. In a nominal scale, the numbers {0,1,2,3,…} are just labels and have no numerical significance. In an ordinal scale the numbers provide an ordering, so that 2>1 in some appropriate sense. An interval scale allows real numbers (like 3.27) and differences are significant, but ratios are not. The classic example is temperature. A difference of 10 degrees is bigger than a difference of 5 degrees, but 100 degrees is not twice as hot as 50 degrees. Finally, a ratio scale allows all of the usual numerical operations on real numbers. Most of statistics assumes a ratio scale, and almost all of it assumes at least an ordinal scale. This leads directly to the mean, variance, covariance, and correlation. It also leads to metric spaces with distance functions.
But we want to consider a random variable X that represents a choice between cloth seats and leather seats. You might argue that leather > cloth, because leather is more expensive. But what about color? Is there any sense in which green > red? We are really interested in nominal scales. We may assign 0, 1, and 2 to “none”, V-6, and V-8 respectively, but these are just labels. That has a profound effect on the statistics we can use. And beyond statistics, we will have to develop notions of proximity or closeness that do not depend on distance functions. So it hasn’t been done before because the mathematics you need is not the popular stuff that’s in the textbooks.
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Part II: Analytics to Action….. The Holy Grail
Can-you-have-too-much-information?
… in my last blog we talked about reporting, BI and data mining, and ? the information overload. So how can help business users with solutions for better decision making, as opposed to drowning them in more data and pretty charts? That is the Holy Grail and the purpose of all this data!
Lets start by defining analytics. So, what is analytics? Neil Raden of Hired Brains, a market research and management-consulting firm, has said that, “the proper term for interacting with information at the speed of business, analyzing and discovering and following through with the appropriate action, is ‘analytics’. I agree. In the information age, this must be done by specialized applications built on analytics based on the requirements of the actions/recommendations required by a business function. Dumping data on a users lap with the message – “Figure it out!” is NOT analytics, and it not very useful either. (I am reproducing a picture I really like as it conveys the message so very well! The Picture is from mathewingram.com/work)
So – how can we transform the user experience for analytics? As mentioned earlier, this can only be accomplished by focusing the analytics on a business problem with the mission to deliver actionable tasks. The challenge is selecting a business problem that the analytics truly delivers unique capabilities and intelligence that is relevant to that problem. This level of focus can be perceived as very limiting, and hence many choose not to go this route. Why limit the scope of the analytics to one specialization, when we can claim that we can do everything! To that I say – you are better off doing one thing very well, as opposed to many with mediocrity at best.
I am going to bring this back to Emcien, as this is a company that has focused analytics on a very specific business problem. The problem is one of product variety, product variants, and lots of attribution. In this age of product variety, that is a problem that is causing tremendous challenges to various business functions.
The analytics automatically detects what features customers are buying, where you are making money. This SKU or configuration intelligence is leveraged for:
SKU Intelligence Analytics Used to Drive Application Specific Recommendation
- Better forecasting at the mix level - The application uses the analytics intelligence to determine the exact product mix with very high accuracy based on true demand sensing.
- Improving the customer experience at the point of sale - The application uses the analytics intelligence and guides the buyer to a good configuration based on the few features they have called out. And by the way – customers love it when you can recommend a configuration based on the few features they ask for. They want you to stop asking more questions and recommend a good choice.
While the analytics may throw out volumes of data, the user can relax, as he does not have to crawl through volumes of date wondering what it is telling him. Converting analytics to actions and recommendations minimizes human interpretation and error on a day-to-day basis. For analytics to be functional in business applications, this is a mandatory requirement in today’s business environment.
So – when you are evaluating BI tools, Analytics, Data mining….. what ever they are calling it! Ask yourself, how am I adding value to the company? What am I giving my business users? Am I adding more work to their busy schedule by piling on data on their computers???? If the answer is YES, please don’t do it. They will thank you for it.
If the data has not been converted to recommendations the business can act on, you will not get value from your investment!
Q&A with John Sloan, former director, Jeep Brand Global Product Marketing
In today’s post, John Sloan talks about challenges dealers face in ordering inventory that best matches customer demand.
Emcien: Describe the Chrysler-Emcien initiative that examined dealers’ struggles with complexity in the ordering process.
JS: In a soft “push” market where volume is driven by heavy incentives versus the merits of the brand / model, managing cost is paramount. A key piece to focus on is product inventory. Dealers get roughly 60 days of no-interest floor plan. In a soft market, vehicles can easily sit for longer than two months before being sold, so it’s critical that vehicles be easy to order, stock and sell. Simple is better.
Emcien worked on a model to simplify the Chrysler PT Cruiser product mix. There were thousands of possible build configurations for the PT Cruiser, creating significant complexity for engineering and the assembly plant, as well as the supplier extended enterprise. Emcien’s ability to accurately forecast demand is invaluable for a complicated product line because it can assist with reducing the build configurations to those that best match demand. The PT Cruiser initiative validated the power of the Emcien inventory model.
Q&A with Mark Gottfredson, Bain & Company
In today’s post, we talk to Mark Gottfredson about product complexity and customer choice.
Emcien: It’s natural for companies to add products and features to keep customers happy. What are the downfalls?
MG: The challenge of adding complexity is it’s the most natural thing in the world. Marketing comes up with new ideas for products or configurations to get the next bit of market share or a little bit more share of wallet. But most companies aren’t so good at retiring products; they don’t have a similarly robust process for taking things out of the catalog that no longer sell, or sell only small amounts. They don’t do a good job of balancing.
Most decisions we make are based on incremental economics. Each decision makes sense in its own right, but the costs of complexity tend to grow systemically. You can’t tie them to a single product decision. Take tinted windshields, for example, that you can sell as an option for $120 and 40% of customers will buy. Assuming the costs of tinting the windshield including inventory impacts, etc., are $9, it will always make sense to add the option. By itself, it is a rational decision, but when coupled with hundreds of other decisions, we end up with dozens of options like power windows, 13 exterior colors, 10 interior colors, 7 different radio and speaker combinations, etc. Eventually, the vehicle can be made in 10 billion different ways, and you don’t know what the next order will be. Since you can’t effectively forecast anymore, you get frustrated and buy a $50 million forecasting module to try to manage all the complexity. You have difficulty balancing your lines, build inventory and increase supply chain costs. Unfortunately, when most companies finally decide to reduce complexity, they “cut off the tail” of low-running options or SKUs. But they don’t remove the systemic costs, and they don’t see any benefits.
Emcien: Companies often overestimate the value buyers place on having many choices. What are the downsides?
MG: Go to a banking website like Citibank or Bank of America. The site describes itself as a full-service bank that has all the items you could want. There are long lists of products like credit cards with different reward programs, as if to say, “We have a lot of products. Surely there’s one here for you. Good luck finding it.” High complexity is a priori evidence that you don’t know what your customers want.
Emcien: When do fewer choices mean higher sales?
MG: When you understand customers. Dell understands customers well. Dell’s website is Spartan; there are just a few choices. If you choose a desktop, up pops three computers: high, medium and low cost. These three configurations are what your segment – home, professional, government – wants. You can customize each one, but you’ll make it as expensive as the next higher model, so then you switch to that and you’re still buying a standard configuration. Every time I have seen complexity reduction done right, sales have increased.
Emcien: How do overoptimistic sales expectations help to spread complexity?
MG: What happens is sales looks for a gimmick that gets them the next sale. Many manufacturers think whatever’s thrown over the wall from product management and sales must be good to go. And sales thinks more is better! Engineers love to engineer; they’ll give you complexity. Most firms build complexity systematically into operations, and then they build systems to handle the complexity, and that’s high cost.
Companies should think about what business would be like with a zero-complexity baseline – how they would operate if they offered just one product or service. The purpose of zero-based thinking isn’t to eradicate complexity; it’s an exercise to reimagine the business with the optimum amount of complexity.
Mark Gottfredson is a director of Bain & Company’s office in Dallas, Texas, which he founded in 1990. Over the past 26 years, he has advised chief executives and top-level managers in a wide range of industries. Currently, he serves as the Global Head of Bain’s Performance Improvement Practice and is also a leader in the firm’s business strategy, airline, financial services, manufacturing and energy practices.
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Stop product complexity at the door
In any manufacturing company that builds configurable products, there is a lot of discussion around what product complexity is. What’s interesting is that when times are good and there are lots of sales, the discussion is usually around how to simplify or streamline with the goal to sell more product even faster, that complexity is keeping sales from going even higher. In bad times, the discussion typically moves to how complexity is causing undue stress on the supply chain, creating problems with parts forecasting, quality and finished goods inventory.
Rarely do these discussions end with participants really agreeing about exactly what complexity is or how to reduce it. Solutions are attempted with internal projects like SKU reduction and part number reduction initiatives driven by Six Sigma teams that mean well and do good work, but usually are chasing the tail of the complexity dog, rather than leashing it for good and guiding it to higher profits, lower forecasting errors, even shorter sales cycles.
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The Root Cause of Product Complexity!
Emcien defines product complexity as simply the ability to predict what the next order coming into the company will be.
Think about it: If you only made product configuration A, you have 100% confidence in knowing that the next order in the door will be configuration A (assuming you get an order in the door at all, not a total given in this economy). But if you have configurations A and B, it’s harder to know and with A, B and C, it’s even harder, and so on. When you have thousands of configurations, predicting the next one is very difficult.
It’s not just the number of configurations that’s important but also how they’re distributed. If I have 10 configurations but 90% of my orders are for config A, then it’s still safe to predict that the next order is config A. But having 10 configs that have each been ordered 10% of the time is extremely complex!
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Why product complexity matters
I was telling some friends at a brunch about what I do, and how variety drives cost in manufacturing. “But all the manufacturing has moved to China,” commented one person. I’ve heard this comment over and over.
A picture is worth a thousand words — and here’s one that fits the bill.
- Commoditization of labor in manufacturing
- Higher output per worker
- The percentage of cost in goods is much higher
