Category Archives: Forecasts

Cross-Functional Tactical Planning Matters

Because if you don’t get tactical planning right, you cannot align cross-functionally, as pointed out in a post over on Supply Chain Shaman by Lora Cecere who said Enough!

So how do you get tactical planning right? According to Laura, you need to address four attributes:

Assessment
Tactical planning aligns functions beyond the enterprise with market drivers and insights using outside-in thinking. It goes beyond a corporate silo.
Action
The team must be knowledgeable and have the authority of line management to act.
Accuracy
Companies need to have access to the right data model and recognize the different value networks because one model does not fit all supply chains. The appropriate risks and opportunities are only identified when tactical options for a strategy are evaluated against market drivers.
Timeliness
Analysis needs to happen within a finite window of actionability.

Then you have to understand the success requirements. According to Laura, to be successful you need to first answer three questions:

What is the goal?
Good tactical outcomes cannot be reached if the company is not aligned on the strategy. The goal has to be well understood.
What does good look like?
How are incentives aligned cross-functionally to achieve the goal? The answer is more than rewarding organizations for the same-old same-old.
What are our risks and opportunities?
Has a proper sensitivity analysis been performed? Has the right data been used?

Once you have the answers, you can start proper planning. Until then, the questions need to be addressed and re-addressed until they are understood.

Why Your Supply Chain Needs To Be Flexible

Thanks to economics, your forecasts will be right only 30% to 40% of the time, as per this recent article over on BBC News that asks why do economists get it so wrong. Whether you care to admit it or not, all forecasts implicitly assume that the general economic condition will stay the same, since that determines not only how much money your potential customers will have, but how much they will be willing to spend. But since the foundation of the economy — humans, resources, wars, natural disasters, technology, etc — are in a constant state of change and flux, all of the models used to describe the economy are flawed.

Thus, your forecasts are only likely to be right at the macro level. Since nearly every economic forecast will be right at some point, every product line forecast will be right at some point, but like a broken clock, may only display the correct volume 0.13% of the time. If you have years of past behaviour, you’ll be able to create a good forecast at the macro (year) level, but it will get less and less reliable as the time period shrinks, no matter how much you throw into your model. That’s why you need an adaptive and flexible supply chain that allows for relatively quick replenishment — so you can ramp up production and distribution when you need to, but not have too much inventory on hand when you don’t.

Why It’s Time to Take that Leap of Faith

Lora Cecere, of the Altimeter Group, just published a great piece on the Supply Chain Shaman blog on why it’s time to take a leap of faith as far as using POS data to drive actionable replenishment is concerned. Yes, it’s time to move from PUSH to PULL using real demand and insight and not just seat-of-the-pants forecasts.

The post, which is over 2100 words in length, is too in-depth to do justice in a short summarization, so I won’t try, but I will point out a few keen observations that often go overlooked in most discussions and add one or two of my own.

First of all, as Lora keenly points out, you can’t prove ROI for an initiative before the initiative is done, which means if you want a proven ROI, you can’t be an innovator … and the big returns in this space will go to the innovators, not the renovators. You have to take that leap of faith.

There aren’t a lot of predictive analytic solutions on the market, but there really don’t need to be. If you get better data faster, you can re-run and correct your forecasts on a regular basis and minimize the the divergence between macro-level estimates and reality. That alone could save you millions.

You have to get close to the customer and get good at using the data in the sales relationship. If you don’t get close to the customer, learn their pain points, and figure out how you can use their data to help them, they’re not going to be that interested in helping you get access to it on a regular basis.

You have to build a cross-functional team led by business unit leaders focussed on innovation, or the initiative isn’t going to pick up enough momentum to make it. You have to break through mental barriers built up over years, or decades, of doing forecasting and inventory planning a certain way, and that’s not easy to overcome.

However, if you follow Lora’s advice, the rewards could be significant as stockpiles of obsolete inventory will quickly become a thing of the past. More importantly, and this is the one point the article should have really emphasized, so will costly long-term stock-outs. If you have access to daily POS data, you will not only see what is selling fast, and what’s not, but you’ll be able to run cluster analysis to see what products are selling well in what locales, and how the demand is spreading (outward or inward). This will not only allow you to quickly refill inventory on a popular, high-margin, item like a cellular phone or tablet PC, at a location about to run out, but predict which neighbouring locations should also be stocked up, and sense demand surges earlier in the cycle, giving you more time to ramp up production to prevent lost sales that could make or break the quarter.

You Cannot Overlook SSDO And Optimize Your Supply Chain

I was taken aback at this recent article in SupplyChainBrain on Supply Chain Optimization in the New Analytics Economy which outlined five analytics-enabled objectives which did not include strategic sourcing decision optimization, which is the next logical step in the sequence. Consider the objectives:

Supply Chain Visibility
Step one is to understand how much the supply chain is costing you.
Demand Forecasting and Inventory Optimization
Step two is to segment the supply chain, forecast demand, and then optimize inventory for each segment.
Network Optimization
Step three is to periodically perform TCO assessments on the different segments of the existing supply chain network to identify the optimal performance configuration.
Predictive Asset Maintenance
Step four is to perform preventative maintenance to minimize downtime and maximize uptime.
Spend Analytics
Step five is to understand how much is being spent on each procurement category and identify those with the most savings opportunities.

The next natural step is:

Strategic Sourcing Decision Optimization
Once the categories with the biggest savings opportunities are identified, it’s time to optimally source them so the overall TCO is minimized and the utilization of the current networks, optimized in step three, is maximized.

How could you possibly stop at step five?

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How Do We Harness The Wisdom of Crowds In Supply Chain Forecasting?

As per a recent article in Strategy + Business on cleaning the crystal ball, which discussed the challenges associated with forecasting, we are reminded how the old game of estimating the number of jelly beans in a jar illustrates the innate wisdom of the crowd. In a class of 50 to 60 students, the average of the individual guesses will typically be better than all but one or two of the individual guesses. Furthermore, not only can you not identify the best guesser in advance, but that “expert” may not be the best individual for the next jar because the first result likely reflected a bit of random luck. Then there’s the fact that research by James Shanteau, professor of psychology at Kansas State, has shown that expert judgements often demonstrate logically inconsistent results. For example, medical pathologists presented with the same evidence twice would reach a different conclusion 50% of the time.

However, teams of forecasters often generate better results (and decisions) than individuals as long as the teams include a sufficient degree of diversity of information and perspectives. This is partially because a naive forecaster often frames the question a different way and thinks more deeply about the fundamental driver of the forecast than an expert who has developed an intuitive, but often overconfident, sense of what the future holds. But you can’t just throw a group of people together in a room and ask them to come up with a consensus because the most vocal or senior person might dominate the discussion and overly influence the consensus because most people put too much confidence in the most senior or highest-paid person.

So how do we harness the wisdom of the crowds and insure that no one voice dominates the forecast when the forecast is inherently risky and unpredictable? We look in the place that we are probably most familiar with — e-Sourcing. A blind RFX survey sent out to an interdisciplinary team of carefully chosen and randomly chosen individuals who, given a scenario description, past sales, and expected market trends (from third party analyst firms) are asked to provide their input to the short-, medium-, and long-term forecasts at the SKU and group level. Then, we simply average all of the responses, giving slightly higher, but individually equal, weighting to the carefully chosen respondents (who collectively complete an interdisciplinary team and do it as part of their jobs) and slightly lower, but individually equal, weighting to a section of random organizational individuals asked to weigh in with outside opinions. It won’t be perfect, but it will be substantially better than all but a few guesses — and since you won’t know what guesses will be good or bad in advance, it will substantially reduce your risk.

Thoughts? Comments? Criticisms?