Demand Planning Function: Where Does It Belong?

Posted on November 15, 2013 by Jeff Shaw

In a prior blog, “Using Forecasting Software to Drive Specific Business Improvements, Example One”, we emphasized the importance of ownership of the demand planning and S&OP process by a member of the C-suite. Who in the C-suite was not the end-game of that blog, as long as it was someone in the C-suite. But a recent energetic discussion in the Institute of Business Forecasting and Planning (IBF) LinkedIn group brought to light several issues that could drive that answer. I love discussion groups and forums: they are like roundtables that allow the kind of real time crossfire and discussion between thought leaders that was not possible before the advent of this and other online forums. Although not everyone agreed or even had a final opinion to the question of who should own demand planning, some of the issues that came up are considerations before organizational boundaries around planning should be set.

Roundtable Discussion

The following is a collection of some of the opinions gathered from the group as to demand planning organization ownership, together with a summary of the Pros and Cons of each approach.

Ownership Choice 1: Sales & Marketing
In his book on S&OP, “Sales & Operations Planning, Beyond the Basics” Thomas Wallace puts demand planning function in the hands of Sales, with longer-term demand planning with Marketing.

Pros: Sales & Marketing are the closest of anyone to the customer, thus having the best knowledge of demand. Advancements in processes and technologies allow them to have adequate statistical predictors of demand. They can work with customer service more directly to manage the market risks.

Cons: Sales people are good at sales, but not necessarily at predicting sales, so this placement can backfire. Marketing can be more focused on marketing and product analyses than on demand plan generation. Making the demand plan part of the Sales KPI could lead to low forecasts to ensure goals are met, potentially followed by failed customer service when Operations follows the plan and produces insufficient inventory.

Ownership Choice 2: Supply Chain Organization
Recent IBF event polls show that 61% of organizations have the demand plan generated here, with the trend rising. It can often end up here not by deliberate means, but out of Operations frustration of leading an S&OP initiative, or when Sales & Marketing rejects ownership. Business financial analysts could offer support.

Pros: This can be beneficial as an independent, more unbiased group. Higher-qualified statistical specialists are typically on staff.

Cons: There is no influence on pricing, marketing programs, etc., that is available if it were on Sales & Marketing side. Ownership too far from Sales can create disconnects. Smaller companies can often not have the budget to allow for a Supply Chain organization, meaning that another home could be Operations.

Ownership Choice 3: Hybrid
A handful of contributors offered up a hybrid approach, backed up by the IBF forum itself. The idea of the hybrid goes something like this: For the majority of companies, Operations or Supply Chain management would own and manage the planning function, while Sales can own the forecast in terms of the month-to-month unit volume commitment. In effect, the plans produced by Operations/Supply Chain are given to Sales & Marketing, who then identifies impactful factors like marketing campaigns, competitors, new markets, or economic indicators, and over-writes the baseline forecast where appropriate. Analysis would follow on both the original and overwritten plans, with metrics feeding back into the process to identify improvement efforts for Sales & Marketing.

Pros: This seems like a more collaborative approach, taking advantage of the skills of Operations, Supply Chain, Sales & Marketing, or any other organizations adding value.

Cons: Without a strong leader with responsibility and accountability, this could be a chaotic process that accomplishes little, and creates divisiveness between different camps.

My Takeaway on Best Demand Planning Placement

There are opposable forces at work here. There is no right answer, but in any case, I see 4 basic rules of thumb:

1. Do your homework. To pick a method, you will have to do some research and ask some questions. What has worked for others in the past, or companies like yours? Where are your skill sets? Where are the internal challenges that need to be addressed? Are there built-in biases, such as compensation tied to KPIs, or reviews tied to performance, that could affect demand plan judgment? Bias is inevitable, but it can and should be minimized.

2. Pick a champion. I see this as the cornerstone that makes it all possible, no matter what the plan. It all goes back to our blog about responsibility for the results. You need to find one individual that is willing and able to see the demand plan through, and with a passion for uniting different company departments to meet the company’s goals.

3. Enable collaboration. Buy-in from the groups from users through C-level executives is crucial to meeting the real goal of accomplishing the company goals. The stakeholders should feel that their voice and results are considered. Led by a facilitator, the collaborative platform should be in place to maximize communication between different business units, with the same goal of maximizing business effectiveness.

4. Make it repeatable. Make sure that the processes, people, and technologies in place are such that the demand plan generation is repeatable, and results are measureable and provide feedback to the process. The review meetings should be structured with a consensus of the stakeholders all present. The process should be automated and streamlined as much as possible.

While generous focus and energy invested on creating the demand planning structure will yield good results within an organization, continuous improvement efforts should be made to revamp the structure if necessary with lessons learned both internally and externally. A good demand planning organization can defend against market volatility and overcome competitive challenges, and follow a path to a demand-driven, and ultimately a customer-driven, success story.

Happy 4th of July: What demand forecasting and the land of the free have in common

Posted on July 3, 2013 by Gene Tanski

The 4^th of July ranks as one of my favorite US Holidays – on equal footing with Thanksgiving. Both commemorate uniquely American experiences and events and fundamentally celebrate what makes this a great country.

Coincidentally, I was reading an interesting article from Niall Ferguson, the Scottish Historian who is a professor at Harvard and fulfills fellowships at Stanford and Oxford (yes I know, you are hugely interested – here is the link): http://www.thedailybeast.com/newsweek/2013/06/26/niall-ferguson-on-the-end-of-the-american-dream.html

While there are political undertones to the article, what struck me was the importance of remembering the fundamentals; not forgetting the critical focus points and capabilities that formed the basis of success for each of you. Mr. Ferguson was focused on the United States as a country, but I am extending the key message to apply to individuals, associations and companies.

Huh? How is relevant to demand forecasting? To S&OP? To anything involved with this blog or this industry or the people who may be so wise as to associate with Demand Foresight?

Well, it is a bit round about but here is my thinking. Back in 2010, I wrote a piece about cloud computing. The takeaway was that cloud computing would have an impact but real competitive advantage would go to those companies that utilized the cloud within the context of how it enabled their business strategy. Even more relevant today, the benefit of the cloud emerges through its application to the specific capabilities that make your company different, special and competitive: for example, collaborating in real time with key customers on new product introductions. These new product introductions have traditionally presented a challenge to demand forecasting, but are also critical to being a differentiator between companies and their competition.

However, the cloud as a technology disruptor is now joined by other disruptive concepts such as big data, in memory computing and causal analytics. On the surface, each of these technologies can seem overwhelming and something that deserves huge attention and resources. And they do, but only within the context of your vision and strategy for your company. You want the power of these technologies for when it’s appropriate, but be sure not to limit your enterprise and its ability to compete and thrive in a wholesale chase of technology for technology’s sake. The companies that outperform the competition over time are the ones that embrace their vision and ensure that everything in the enterprise, including technology, focuses on achieving that vision. At this particular point in time, with the advent of so many new technologies, what your company needs more than ever is a clear vision of what your competitive advantage is: decide what makes your company better, and find the right mix of technology (and ongoing adaptation/evolution, culture and people) that’s going to preserve and enhance that advantage.

And, in what should be no surprise to you at all, we maintain that those companies that designate demand forecasting and S&OP as a strategic capability will outperform their competition.

All of which brings me full circle to the start of this blog. It is remarkable that we have the environment and the setting in which our focus can be topics such as the impact of big data on our ability to gain market share or fulfill a 5 year strategy. We have the freedom and the opportunity to concentrate on marshalling resources to compete and innovate and do good. That is fantastic and is the result of the fundamentals on which this great country was founded – the land of opportunity and the land the free. Happy 4^th of July!

Ferguson, N. (2013, June 26). General format. Retrieved from http://www.thedailybeast.com/newsweek/2013/06/26/niall-ferguson-on-the-end-of-the-american-dream.html

Using Forecasting Software to Drive Specific Business Improvements – Example One

Posted on June 26, 2013 by Gene Tanski

I am going to veer off from best practices for just a blog or two – we were recently going through a couple of project review sessions with clients and it struck me that there were some good examples of how to specifically apply improvements with forecasting software. So thought I would share them; and for a special treat, I happened to have been running through the trails by my home and for some reason the Beverly Hillbillies sprang into my mind so the opening paragraph – please read it to the opening stanza of the BH theme song:

Want to tell you a little story about some companies, looking to establish best practices in S&OP.

Then one day, as they were boarding on despair, they discovered the miracle of forecasting software.

Okay – that was lame but it made me giggle, and I have always enjoyed the incredible gift of cracking myself up.

The point here is that the specific improvement example is based on using the more accurate forecast from the forecasting software to drive performance improvements in both inventory and customer service.

Specifically, this example happened with two different companies in different types of business: one in paper and one in lawn care. So this type of work should have broad application.

Upon applying the best practice of measurement (please see earlier blog on S&OP software investment), these companies were able to identify opportunities with their top customers. One – they had indeed improved their forecasting in excess of 25%. In addition, they were able to show that the forecast supplied by their customers had error in excess of 50% (don’t think I need to bring up the dart board analogy here). Please remember that we are talking about forecast error measured at the execution level, which in one case is by SKU by customer and in the other is SKU by ship to location. Lastly, they were able to show what the implications were in terms of product inventory that was held throughout the value chain – specifically customer stores and distribution centers as well as our clients’ warehouses.

Incidentally, I should also point out that the companies were now running their computer forecasting based on the combined input of historical internal information (shipment, inventory, etc.) along with Point of Sale data from their customers (both sales at stores and inventory at stores and DCs).

Armed with this revealing information, these companies approached their customers. Based on the data and information that was provided (of course having gone through a verification process), the partners in the value chain were able to take specific action.

With regards to the paper company, they were able to work with their client to streamline the new product introduction process. Since they can sense the sales activity throughout the value chain, they can more specifically match the timing of introducing the new product upgrade to when the old product will sell out in the normal course of business. For the retail customer, they were able to cut out almost $2 million in working capital carrying unneeded inventory at their DCs and stores. Our client was able to save $1 million plus per quarter in promotion incentives used to clear out old product in anticipation of the new product intro.

Focusing on lawn care, our client’s retail partner was ordering far too much product in anticipation of the spring season, which took up space in DCs, required extra space in the stores, and far exceeded actual demand. While I do not have the exact figures, we do have verification that this unnecessarily consumed working capital and sub-optimized the performance of many of their stores as that retail space could be used for other products with equal demand. Our client in turn was able to avoid the inevitable return process and accompanying issue of product credits. In addition, they were able to utilize constrained capacity for a broader array of products, some with higher profit margin. They are estimating improved profitability of approximately 6% and counting. The counting part stems from the application of the new practices to their remaining base of retail partners.

The most accurate forecast possible derived from your forecasting software is worthless unless you apply it to specific components of your value chain and back it up through measurement-in other words, discover your “bubbling crude” and “Texas Tea”. Hopefully these two examples will provide the basis for some creative thinking within your value chain, and some measurable benefits to your bottom line.

Best practices to ensure investments in S&OP software and Demand Forecasting deliver measurable benefits – Number Two: Measurement

Posted on June 19, 2013 by Gene Tanski

This is a part 2 of a series, following recent part 1, Best Practices to ensure investments in S&OP software and Forecasting deliver measurable benefits – Number One.

Okay, after the hugely controversial and panic-inducing selection at Number One, no one should be at all surprised that Measurement is Number Two as a best practice of S&OP and demand forecasting investment. Business strategy is rapidly evolving to accommodate more complex and sophisticated views of how firms determine and execute their strategies. New momentum is gathering around strategies such as Multi-Echelon Inventory Optimization, (MEIO), and Segmented Supply Chains, (SSC), all of which should be managed within an S&OP framework appropriate for your value chain.

Measurement, as it pertains to those strategies, is bringing multiple pieces of the organizational puzzle together into an integrated framework that aligns your strategic initiative with the assets necessary to execute and the information critical for improved decision making. In this blog, rather than recreate what has so professionally and exhaustively been researched and published, we will provide practical feedback from our clients as to why they focused on measurement and what they measured.

Why measure?

Because everyone has accepted that it is a good idea and your company has just spent big dollars on a BI tool?

Or, because there are a large number of pithy quotes out there that seem to make it obvious? — Such all time classics as:

1. “In God we trust, all others bring data” – Deming I think.

2. “You can’t manage what you don’t measure” Is that Drucker or DeMarco? I always forget.

3. “You get what you measure. Measure the wrong thing and you get the wrong behaviors” This is definitely Lingle.

4. “What gets measured gets done, what gets measured and fed back gets done well, what gets rewarded gets repeated” This is Jones and if you need proof of the truth of this quote – ever train a rat in psych class or one of your own kids; in a related note please see best practice number One.

5. And my personal favorite “ The only man I know who behaves sensibly is my tailor; he takes my measurements anew each time he sees me. The rest go on with their old measurements and expect me to fit them.” Shaw

George Bernard Shaw really gets to the heart of it – in order to achieve upfront and ongoing benefits from your investment in S&OP and demand forecasting, you need to measure because everything is changing and you need to honestly and correctly look at how things really are today, (and how they are trending moving forward), not what they were last year when you were doing a plan. And in order to look at thing as they are today, you need a measurement framework that allows you to understand what is changing, the impacts on your business of those changes, and the decision criteria for addressing those changes.

But many of you may be reading this and saying hey – yeah, very clever, using Shaw to get to the truth of measurement, but I can’t use that to convince my teammates that this is a good idea, so how about something useful?

Okay then, a couple of other reasons to measure:

1. It will increase the value of your business – whether public or private. Useful measurement frameworks are indicative of well run companies. “Well run” companies that earn that designation are rewarded with higher valuations, which means better rates from lenders and higher valuations from investors. In addition, for public companies, there exists a legal reason for measuring performance, and this is called Sarbannes Oxley (SOX). The value for measuring within this context can be summarized as:It’s critical for driving performance and decision making in downstream components of the value chain. Measuring forecast error performance is important because the actual outcome – Forecast error – is specifically used in areas such as safety stock, raw material/component purchasing, production scheduling, order management, supplier performance and others.

a. Higher level SOX compliance reinforces reputation and trust for a specific company within the investing community, thereby potentially making said company eligible for inclusion in certain pension and investment portfolios, which increases demand for the stock.

b. The associated improvement in forecasting revenue and related financial measures helps support and potentially enhance the valuation of a company as reflected in the stock price.

2. It supports collaboration and learning. If professionals within your organization believe that the measurement is fair, consistent, and tied to strategic initiatives, then the focus becomes less on CYA (cover your ass…) and more on looking at what the measures are revealing, and looking for areas to learn and improve. If forecast error reports are used to understand which inputs are most useful, everyone better understands their role in driving accuracy, the impact of accuracy, and how to get better.

What to Measure:

Our clients have focused on what to measure around S&OP and demand forecasting, so that is what we will focus on here.

1. You have to measure at the lowest level of detail. Ideally in forecasting, what we have seen drive the best results is a focus on measuring the execution level of forecasting – that level that drives operational performance. The trick is how to measure the details but still make the measurement and reporting of that measurement (formats, GUI etc.) easy to use and effective at highlighting issues that need attention and action (and yes, we at DF think we have a pretty nifty solution around this).

2. You have to measure the input of the professionals involved in the S&OP and forecasting process. This includes all participants, internal and external. Practically speaking, this means managing and measuring multiple forecasts. Typically you see one from sales (and perhaps marketing), and operations certainly have their point of view. Finance adds value from a top down perspective, and there might be other internal forecasts. It then becomes part of the S&OP process to work through the different perspectives and arrive at consensus (whether authentic or dictated). In addition, we see input from customers more and more often. This is where the whole learning and collaboration point above comes in. Your customers usually are horrible at forecasting. I mean they suck, and that should be no surprise, as they focus on what is best for them. That usually means “more product is better,” especially in situations where they can return excess product or don’t pay for it until sold – (VMI type situations). Being able to measure their error, then go back to them with the impacts of their error-ridden forecasts and offer intelligent and verifiable alternatives can often help you be a value added partner.

3. You have to measure the input and impact of data. This is becoming increasingly important as more data becomes available more often and in bigger quantities. This boils down to constantly reviewing what information is driving your S&OP process and whether it adds value. Historically, shipment history has been a big drive and it will continue to play a role moving forward. But now there is a growing abundance of what we call Type 1 external data. This is data that exists within your value chain and comes from your customers and suppliers. A good example is Point of Sale data from retailers. Does this impact forecast accuracy? On what time frame? Then there is what we call Type 2 external data and this captures causal data which has been addressed many times in this forum and others. The key is what data helps and how so and what data does not help so that it can be ignored and/or not collected anymore. This is a very underutilized area of measurement.

4. You have to measure the impact on the strategic imperatives. What is the impact of a certain level of forecast error on EBITDA? Or Margin? Or working capital levels? Measurement must occur within a context, and for the businesses we are fortunate enough to partner with, that context is strategic imperative. This is the basis for justifying the investment in S&OP and forecasting.

Measurement systems are critical to both the company as well as the individuals who drive the company. They capture and disseminate strategic information and outcome measurement for the firm, and performance measurement, incentives and motivation for the individual. They can be feedback not only on the forecasting technology, but also on the process and people involved, especially on the effectiveness of the demand planning function within the company as a whole.

When done right, measurement systems, (and S&OP specifically), can become the primary means for a company to understand (coordinating what a company knows and learns) and to do (how a company alters and improves what it does).

Given that importance, we feel strongly, (as do our clients), that it is time for a new framework of measurement specific for S&OP and forecasting. In a follow up blog, we will cover our thinking about what that new framework is and how it should be done.

Best Practices to ensure investments in S&OP software and Forecasting deliver measurable benefits – Number One

Posted on June 12, 2013 by Gene Tanski

Gartner Research just completed its annual supply chain conference during which they were sharing a lot of their research and one of their findings is really just mindboggling: after initial investment in S&OP software (and forecasting and Demand Driven Value Networks (DDVN)), fully two thirds of companies have failed to mature their processes or continue to drive investment in order to obtain measurable benefits to their bottom line. In fact, most of the companies did not focus on ROI at all.

That’s somewhat reprehensible, not to mention disheartening in a business world that is growing more competitive and difficult. We have spoken at length about the fiduciary responsibility of executives and officers to drive the performance of their companies and to be able to measure and confirm those impacts for the good of their stakeholders, their shareholders, and their own careers. There has also been ample information posted here and elsewhere about what those benefits would add up to in terms of shareholder value and cash flow.

But for some reason, obtaining and documenting a minimum 5% improvement in company earnings (measured by EBITDA and as used as a proxy average for ROI measured in a bunch of different ways) has not proven to be a big enough incentive to focus companies on wholeheartedly pursuing improved forecasting.

Maybe companies really don’t know how. Perhaps enterprises are struggling to figure out how to move forward once the original work (process design? System implementation?) is done. Or, perhaps the fundamentals of the business are changing (acquisitions, competition, etc.) such that the original work is no longer relevant and no one has the time or the focus to make sure that the S&OP and forecasting components stay up to date and relevant to the business.

Given all of that, we would like to share what we have learned from our clients regarding best practices to ensure relevant S&OP software and forecasting and, more importantly, measurable bottom line results and continuous improvements to those results.

The number one most important best practice for any company wanting to be best in class for S&OP and to ensure continuous improvement within this strategic capability is:

A member of the C-suite executive group owns the forecast, its accuracy and the S&OP process.

And by own it, I mean

It is part of their job description
It is publically acknowledged as a critical strategic capability for the company that will improve
The remaining C-suite executives publically commit to support the executive in achieving the stated goals
Most importantly, it is a large part on the compensation profile for the executive who owns it as well as a portion of the compensation for the remainder of the c-suite. By large I mean somewhere between 20 and 40% of their total compensation (for the specific c-suite executive and 5 to 15% each for the remaining C-suite team) is determined by the companies’ S&OP performance anchored most visibly by the forecast error measurements.

We are not going to go so far as to say which C-Suite member should own it, as different industries and different organizational structures and cultures will lead to different answers that work best for each company, but someone in the C-suite has to own it.

It is not possible to overstate the importance of this best practice. If Gartner were to go back and apply this additional criteria piece to their research, I would bet body parts, (yes even my own), that they would find that the vast majority of the companies in their research data base do not follow this best practice.

Why is it so important? What does it add? Because once this fundamental best practice is fused into the DNA of the company (i.e. the C-suite putting compensation at risk), it ensures that the next set of best practices can and will be set in place and that addresses the problem highlighted in the Gartner research cited at the start of this note. We will cover these in upcoming blogs, but an example of additional best practices include (not in any particular order)

Measurement
Corporate alignment – not just who does what but also what does each group gain from their involvement
Appropriate resources and measurement
Appropriate and flexible process
Appropriate technology.

No one is suggesting this is easy; we are here to say that the payoff is more than worth it and that is supported today by a large and growing quantifiable pool of research from many different communities.

If you are wondering, there is a first, pretty good step you can take to start this journey – get the group (C-suite, perhaps direct reports) together and answer the question – If we could improve forecast error by 5% what would that be worth to our bottom line (EBITDA, Cash flow?)? What would 10% be worth? What would 25% be worth?

Once that question is answered, the basis for this best practice is patently obvious. And the exercise will also open up all the supporting areas that will contribute to and benefit from the elevation of S&OP to a strategic capability – competitive advantage, improved customer service, reduced working capital requirements etc.

This is just the start. But always best to start a journey that is worth it. And getting S&OP and forecasting right is absolutely worth it.

This is a part 1 of a series, followed by part 2, Best practices to ensure investments in S&OP software and Demand Forecasting deliver measurable benefits – Number Two: Measurement