Next Generation DSRs - An Analytic name is not enough

You need not always build your analytic tools, sometimes you should buy in. If the chosen application does what you need that often makes good economic sense... as long as you know what you are buying.

Let's be clear, an Analytic name does NOT mean there are any real Analytics under the hood.

For many managers, Analytics is akin to magic. They do not know how an analytics application works in a meaningful way and have no real interest in knowing. At the same time, there is no business standard for what makes up "forecasting", "inventory optimization", "cluster analysis", "pricing analysis", "shopper analytics", "like products" or even (my favorite) "optimization".  Don't buy a lemon!

Recommended Reading: The Definitive Guide To Inventory Management

A little over 15 years go now, I was set the task to model how much inventory was needed for all of our, 3000 or so, products at every distribution center.  Prior to this point, inventory targets had been set at aggregate level based off experience and my management felt it was likely we had too much inventory in total and what we did have was probably not where it was most needed. (BTW - they were absolutely right and we were ultimately able to make substantial cuts in inventory while raising service levels).

I came to the project with a math degree, some programming expertise, practical experience simulating production lines, optimizing distribution networks, analyzing investments and with no real idea of how to get the job done.  The books I managed to get my hands on gave you some idea how to use such a system but no real idea how to build it.  They left out all the hard/useful bits I think.  So, I set about to work it out for myself with a lot of simulation models to validate that the outputs made sense.

I still work occasionally in inventory modeling and I'll be teaching some components this fall, so I have been eagerly awaiting this new book : The Definitive Guide to Inventory Management: Principles and Strategies for the Efficient Flow of Inventory across... by CSCMP, Waller, Matthew A. and Esper, Terry L. (Mar 19, 2014)

Back to blogging on "Better Business Analytics"

It's been quite a while, just over 12 months in fact since my last blog post.  In that time, I've been hard at work developing analytic applications for the Orchestro DSR.  (Orchestro's off-shelf alerting tool is especially cool and something I am very proud of contributing to).    I enjoyed my time at Orchestro, they're a good team and have big plans, but one key thing I found out about myself is that I prefer working real-life problems to developing software for someone else to have all the fun :-)

So, I'm now back full-time on consulting and I will occasionally blog on topics of interest to me.   Expect to see more soon on:

• Next-generations DSRs (Demand Signal Repositories)
• Retail supply-chain analytics
• Handling (BIG-ish) data for analytics
• The right tools for the job (Predictive Analytics, Business Models, Optimization)
• Some more thoughts on store-clustering
• Inventory modeling at retail (and why it's different, again)
• Order forecasting using POS data
• Further thoughts on SNAP and other ignored demand drivers
• and if there is something you would like to hear more on ... just drop me a line.

How to save real money in truckload freight (Part II)

In the first post in this series (Part I) I looked at the opportunities to reduce freight cost from traditional transportation management, but the really big opportunities may lie outside of your transportation team's control.  In this post, we'll look at some additional (and very possibly larger) opportunities.

 By the time a request hits the Transportation Team the damage has been done.  It’s already been decided that something needs to move, how it needs to move and when it must depart/arrive.  This is where you can really save.

How to save real money in truckload freight (Part I)

How can you save real money in truckload transportation?   In this post, let’s look at the areas that your transportation team manages directly.

Inventory modeling is not "Normal"

We can build models to know how much inventory we need to hold of each product in each location. Do this well and you improve service levels AND reduce inventory.   I've posted on this topic before including an online calculator from a relatively simple Excel model to help you visualize the relationship between uncertainty, lead-time and case-fill rate. (Check out How much Inventory do you really need ?).

I wrapped up that post with a warning/disclaimer that the spreadsheet model was really too simple for real life use, but I didn't tell you why.  Now here's the kicker:  many packages appear to have the same problem and can cause you to severely underestimate your inventory needs and lose sales.

Inventory modeling in action

Inventory modeling and inventory optimization attempt to drive out unnecessary inventory from your systems, to improve service levels to your customers.  This does work and can drive very significant reductions in inventory, but, if you lack discipline around execution you will not get as much value as you should.

Balancing safety-stocks across DCs

Earlier today I saw and responded to a question posted on the IBF (Institute of Business Forecasting & Planning) LinkedIn Group.  It's a question I come across often so I thought I would repost it here (with a few edits).

Question:  
How do I go about preparing an aged inventory analysis? I need to show fast moving,slow moving item, then I want to transfer product with in DC's that are slow so that the amount of SS is balance

WARNING: Bad business analytics may be hazardous to your wealth !

You paid handsomely for the software, perhaps for consulting too and have had some bright sparks working on it for months: the results of your analytics project are in and the answer is ... useless without some understanding of how good the models are it's built on.  If the analyst cannot give you detail on how 'good" the model is for its purpose, all results should come with a wealth warning. 

BAD BUSINESS ANALYTICS MAY BE HAZARDOUS TO YOUR WEALTH.

Point of Sale Data – Supply Chain Analytics

I’ve spent a large part of my career working in Analytics for Supply Chain.  It’s an area blessed with a lot of data and I’ve been able to use predictive analytics and optimization very successfully to drive cost out of the system.  Much of what I learned in managing CPG supply chains translates directly to Retailer supply chains it’s just that there is much more data to deal with.  

How much inventory do you really need?

If you are following lean methodologies you will have encountered the concept of inventory as waste.   It’s something you have because you cannot instantly manufacture and deliver your product to a shopper when they want it, but not something that the shopper sees any value in.

I find that a very interesting idea as it challenges the reasons that you need inventory, and that’s definitely worthwhile.   However, many of these causes of inventory need more substantial changes in your supply chain (additional production capacity, shorter set-up times, multiple production locations) so as a first step, I suggest that you figure out what inventory your supply chain really needs and why.  Take out the truly wasted, unnecessary stock and then see what structural changes make sense.

Typically you can remove at least 10% of inventory while improving product availability. What’s that worth to you?  If that sounds a little aggressive, I can only say “been there, done that, got the coffee-mug”. (We didn’t do t-shirts).

Bringing your analytical guns to bear on Big Data – in-database analytics

I've blogged before about the need to use the right tools to hold and manipulate data as data quantity increases (Data Handling the Right Tool for the Job).  But, I really want to get to some value-enhancing analytics and as data grows it becomes increasingly hard to apply analytical tools.

Let’s assume that we have a few Terabytes of data and that it's sat in an industrial-strength database (Oracle, SQL*Server, MySQL, DB2, …)  - one that can handle the data volume without choking.  Each of these databases has its own dialect of the querying language (SQL) and while you can do a lot of sophisticated data manipulation, even a simple analytical routine like calculating correlations is a chore.