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Lokad : Look ahead?

Better late that never – I came across a SaaS forecasting technology provider named Lokad in one of my sojourns on the interwebs. I must confess at the very outset though that I’m a biased reviewer at best. Biased, how and why? Two salient points:

1. I am of the firm belief (note the word) that forecasting is best done in hindsight where it may do the least harm. Forecast all you wish about the past – I have no issue with you.I have no problems with economists creating all sorts of economic models to forecast the state of the economy because I rarely if ever pay any attention to them. So also the Raputre – same thing. This thread of skepticism pervades everything from the macro to the micro forecasting world. That’s the first point – Do no harm.

2. I am firm believer (note the word again) in the primacy of execution – the best forecast that I can think of is the one that creates the world ahead because of the design, of the plan, of the intent to dominate etc. As you may imagine, this is restricted to the world of growth and not the world of maturity and decline of a product’s lifecycle. While each of these three phases of a product’s lifecycle have their own specific variables and levers of interest, my mantra here is not to predict the world but to respond to it in the least amount of time. Thus, I’m not a big fan of long lead times, centralized planning et al.

Please do keep in mind these points as I delve into this review.

"We benchmarked Lokad on client data (a beverage distributor) against a model we specifically developed for the case. Following a deep analysis of the data we combined different forecasting techniques like ARIMA, VAR, LOESS, HOLT-WINTER and others using R, the statistical computing software. Lokad performed very good, the values of MAPE were similar to our results, after 3 months of analysis of the case. I am really impressed of this accuracy. Lokad is also very fast and provides a high level of automation." Mauro Coletto, Business Intelligence Consultant

Empiricism – always a good idea.

The good practices that I see at Lokad’s forecasting engine

1. Getting as close to the point of demand data as possible – vital for execution and even more so for forecasting. But from the looks of it, if you as the client of forecasting as a service don’t have really granular data, then Lokad’s service can only be as good as your own execution efforts are likely to be. The implication here is that you really can’t expect Lokad to improve your sorry ass case of datatitis.

Our technology is designed to deal with your data in their current form.

One point to note here though is that most companies firmly believe that they’ve got a good handle on data. That’s until they see how their industry/segment leaders benchmark at.

Verdict: Neutral.

2. Getting the statistician(s) out of your firm. Spouting statistics on this and that is a finely honed skill that has considerable usefulness to your career progression – the higher you go, the more access you have to utterly useless statistical gordian knots. I don’t think that I would be so far off the mark as to say that higher honchos who browbeat you with statistics quantitatively know how poor their own competence and consequent results are without having any insights on what qualitative actions can be taken to surmount, nay transcend the current set of pitfalls marked on the pareto charts. While you can outsource the real statisticians and their professional output to Lokad – this may be an opportunity that Lokad is leaving on the table. Statisticians both inside and outside a firm are quite likely to be treated as black boxes anyway – so why incur the cost of having that black box on your payroll? I can predict quite easily that as Lokad grows, they will go after the opportunity on the table and I believe that can be a big big opportunity. The real competitors for Lokad in that space are the business/operations consultants. Upsell the interpretation once you have the lock on the statistical computation service.

Verdict: A big plus for Lokad.

3. The use of computational power – Call it the cloud, compute storm, global warming – whatever? What cloud computing does for every client is a very old idea in new clothes. That is to say, when you need the burst of computational power to solve a multi-variable (in the millions of them) for a short period of time, doesn’t it make better sense to pay for your slice of time rather than buying the whole set of computers and storing them in the basement? Companies like Lokad that are doing this today are all set to ride a power wave of such adoption – the time and assets to solve these large problems are now shared. Talk about cooperation without actually talking about it. Lokad is riding the wave and that’s a good way to piggyback on the successes of others riding the same wave.

Verditc: A plus for Lokad

4. Finally the Technology – The nitty gritty of the technology. First up, Quantile forecasting. I did struggle more than a bit to understand this and I’m not entirely sure that I understand it (Has that ever troubled me in blustering on but I digress…). The idea here seems to me to introduce a weight (that is correlated to the difference in payoffs for the positive vs negative realization of a particular event). To me, this is a part of modeling i.e. appreciating the sensitivity of a forecast and erring on the side of the lower cost. Makes sense.

Now, how can it be applied?

This Lokad blog post examines that (a little scroll down in the post) : Quantile Forecasting Technology

What I understand about the use of quantiles in forecasting is the improvement (marginal or more) that one gets from a better extrapolation of the expected demand predicted by a normal distribution over a cost weighted distribution? Is this the heart of it? Over 1 SKU the delta between a normal distribution and weighted distribution is probably quite small but extend that over 1000s of SKUs and the numbers begin to add up.

Verdict: A plus for Lokad (Remains a plus if it is what I understand it to be as above)

In all, I find such a move by Lokad quite an interesting thing. Validated empiricism would make it compelling as well. However, as I outlined above, my biases and experience is on execution. Good execution with good forecasting in your frontal view rather than in the rear view mirror is something of a mythical beast but strange things are beginning to happen in the world of the cloud.

Predictions from Supply Chain Gurus 2012 – Part 1

As promised, here is my take on the Predictions from Supply Chain Gurus for 2012. You can read the article at SC Digest :  Predictions from Supply Chain Gurus for 2012

First up the Gartner Boyz:

Like others, Gartner is projecting a movement of manufactured goods overseas back nearer to US soil, if not within the country itself. It projects that "By 2014, 20% of Asia-sourced finished goods and assemblies consumed in the US will shift to the Americas," which of course can mean Mexico, Honduras, Costa Rico and other nearby sourcing locations.

The drivers? First,they says that many companies initially underestimated the true total costs of long supply chains offshored to Asia, miscalculating inventory costs, greater issues with product quality, lost sales or discounted prices due to long lead times, IP theft, and more.

They also note that some of those issues may soon be exacerbated in some countries (meaning China) as more and more production will be consumed in Asian markets, not Western ones.

Sorry, guys but I predicted this way back in 2006-07. Don’t believe me, you can read it for yourself here: Surviving the China Rup Tide – How to profit from the Supply Chain Bottleneck and The Intimate Supply Chain – Part 1 amongst other posts. The simple point is that none of these supply chain moves (no matter who the guru predicting it is – not even me) are holy writ but they’re very much context writ.

And what explains this shift?

"Customer demand for service excellence and increased product choice at competitive prices is
driving brand owners to reassess the value delivered by their supply networks," the analysts say. "Sacrificing lead time for reduced unit cost will be insufficient to satisfy this customer requirement."

For certain segments of the supply chain, a "nearshore" strategy will make a lot more sense, they believe.

Come on, does this hypothesis pass muster? Isn’t there a recession on still – unofficially but have you taken a look at the purchasing power of the middle class lately?

Production to scale is a great idea when demand is always pointing upwards on a growth chart but it’s a sorry idea if your demand falls off a cliff or in this case becomes as volatile as fickle fig leaf.

Further,

Gartner says that "After billions of dollars spent on ERP, many companies still lack the timely, accurate and network-based data that can guide fact-based, timely supply chain decisions." It also notes that companies are collecting in one way or another vast amounts of information, much if not most of which is not being used effectively for improved decision making. "Big data" is the term associated with the opportunity to better mine this information and extract more value out of it, to the great delight of data warehouse. analytics, and storage vendors.

Now, remember what I said just a few posts back about the inevitable entrance of Big Data into the enterprise world – this is no big secret and my advice to you is to get on the bandwagon now. That’s also the reason why I started my Big Data blog because there’s no escaping this elephant in the room.

This provides a perfect segue into a curious coincidence wherein this firm came into my crosshairs – Lokad. Now, I’m not very familiar with the technology but that will be the focus of my  next blog post as I investigate what they do. The gist of it is that they’re trying to get better forecasts by digging through Big Data.

Stay tuned for that update…

 

About me

I am Chris Jacob Abraham and I live, work and blog from Newburgh, New York. I work for IBM as a Senior consultant in the Fab PowerOps group that works around the issue of detailed Fab (semiconductor fab) level scheduling on a continual basis. My erstwhile company ILOG was recently acquired by IBM and I've joined the Industry Solutions Group there.

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