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“This Sells with that” for Aftermarket Parts
The SKUs (stock keeping units) in an aftermarket business is nothing but the various components of an automobile, tractor or backhoe. Every new model that comes into the market sees an addition of 2,500-3,000 parts to the existing master list, while the addition of every variant adds another 500-1,500 parts. The last forty years has seen a phenomenal growth in the number of aftermarket parts. This phenomenal growth has been fuelled by the liberalization of global players resulting in thousands of models of cars, tractors and other machines.
Frequent launch of new models has reduced product life cycle and has negative implication on the aftermarket supply chain. The implication of shorter life cycle of automobiles on the aftermarket business is faster transition of parts from runners to repeaters and soon fading into obsolescence. The revenue from parts of a new model are typically low in the first two years after launch, increasing in the third and fourth years. The sales only stabilize if the model continues in the market. Unlike other businesses where a product is discontinued if it does not yield revenue, in an aftermarket parts business the part needs to be supplied even if the model has failed. This tremendous volatility impacts the number of parts the aftermarket business has to support.
For example, if an OEM introduces a new model and two variants each year – this results in about 3,000 parts. In six years time the parts proliferation in the aftermarket business of this OEM will be least 30,000 parts. This explains the relevance of having a SKU management for the aftermarket business in order to track the movement of parts till it reaches obsolescence.
Levering “this sells with that” Intelligence from Invoices
The challenges due to inventory and assortment planning for the aftermarket parts are significant. Traditional forecasting methods that treat each part as an independent entity are outdated. For aftermarket parts, the performance of traditional forecasting methods is dismal, and hence not all the parts can be forecasted. In fact, only five percent of the master list can be forecasted with an accuracy of 75 percent on a monthly basis. This results in bloated inventory, incorrect parts assortment and lost sales.
OEMs, distributors and retailer have realized the importance and complexities of the spares business and have initiated measures for assortment planning and SKU management. This requires sustainable methods as the market grows and competition gets fiercer. Every OEM has mentioned how the downturn has reflected in increased sales for the aftermarket/service parts. A part of the business that was typically ‘a nice to have’ has become a focus for improved customer service and profit.
"This Sells With That" For Aftermarket Parts
The aftermarket business deals with SKUs in tens of thousands. Unlike soft goods, in this business customers buy parts for projects and jobs. The invoices reflect that, and are comprised of parts bought by maintenance and repair shops to get a job done. Hence, the affinities in the sales transactions reveal what parts that are bought together for repairs/jobs.
The sales transaction data for the aftermarket businesses is very rich with parts affinities and trends. That’s because the data reflects the buying patterns of repair shops and mechanics. This represents a significant opportunity to leverage the transaction patterns for assortment planning, inventory management and of course, ‘suggestive selling’.
Emcien offers analytics that reveals patterns in sales transactions, producing a complete data map of the item affinities for ALL parts. This intelligence can be input into traditional warehouse planning and retail assortment planning systems. The affinities data can be used to make traditional systems smarter as the sales patterns change, parts change and market shifts.
The current sales patterns and item affinities in planning systems are input manually using manufacturer recommendations and gut feel. These recommendations get stale very quickly and are rarely based on actual sales invoices. The value of analytics driven affinities is that the relationships are always up-to-date, backed by actual sales transactions. Emcien’s analytics produces affinities data for all parts in the supply chain eliminating the need for ‘hard coded’ rules that quickly become obsolete and are a nightmare to maintain. This enables aligning parts inventory with with sales/model changes/parts utilization based on actual invoices. The affinities data can also be use for suggestive selling to increase customer service and order size.
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Demand Sensing And Demand Shaping
Forecasting and planning is a challenge in the best of times. The times we are in make this a herculean task. Market demand shifts continually as economic conditions change, products change, prices fluctuate, competitors act, new products are introduced, marketing creates promotions,……. The list is quite endless. Current planning and forecasting methods are reactive and struggle to keep up with these shifts.
The solution is “Demand sensing and Demand Shaping” – active methods to predict what demand will arise and keep ahead of the market. Demand sensing is the ability to detect what choices customers are buying patterns and the trends associated with these choices. Demand sensing can help you to quickly see market shifts to plan your product mix and offering.
Customer Buying Patterns "Customers who bought this SKU also bought this other SKU"
Demand shaping is the ability to guide customers to the best choices at point-of-sale. This is the key to increase revenue and supply chain efficiency. However, demand shaping needs product intelligence at point-of-sale to guide customers to the best choices. Some of the ways to demand shape are –
- If you offer many products or SKUs, there are typically strong buying patterns in the demand. For example – This printer is often bought with this unbleached paper, this ink cartridge and cable. Then, when a customer selects the printer at point of sale, you want to automatically show him the other items that have strong buying patterns. The customer will thank you for this recommendation because usually they need this additional stuff, and you just saved him a ton of effort thinking about it, and a ton of time searching for it. And you made more money in this sale!
- If you offer a product with many attributes, every sale will begin with the customer calling out a few attributes. The opportunity to demand shape is to recommend a good choice based on the partial list of attributes the customer has called out. Demand Shaping requires the ability to complete the order with the right attributes. The best way to complete the order is to have sales intelligence these attributes are bought with these other attributes. It is the Amazon-esque way to look at products with many attributes.
- The biggest opportunity of Demand Shaping is guiding customers to close-enough SKUs. Most customers describe the products they want to buy with a ‘kinda-sorta’ attribute description. As the number of product features grow, there are a large number of SKUs that are similar or close-enough that they can satisfy the customer. So there is a significant opportunity to guide a customer to a similar or close-enough SKU at the point of sale. The recommended SKU may differ in attributes that the customer did not “call out” or specify. If you can offer up this SKU it is a win-win. You have served the customer. You have won the sale. You have moved your inventory. And your competitor did not get this customer.
As product choices and the number of SKUs grow, these techniques are mandatory for an efficient supply chain and for a good customer experience in this customer-centric world.
I just read an article by Mark Pearson, Six secrets of Supply Chain Planning Masters.
Quoting Mark Pearson’s article – Think of demand sensing as predicting what demand will arise, as opposed to simply reacting to incoming orders. Shaping demand, on the other hand, is all about steering customers toward available products and services. Compared to laggards, more than four times as many masters said they can predict demand with greater than 80 percent accuracy levels. And nearly twice as many masters said their ability to shape demand was “good” or “excellent.
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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!