Sanjiv Sharma - Rolls Royce

Having worked in the industry, we all know that Sales & Operations Planning (S&OP) is all about mega decisions being made by executives about issues such as building factories, authorizing inventory or hiring people for major projects. We also know that executive decisions are made with limited information and, especially in the world of S&OP, with uncertain information as well. In a well functioning S&OP process, the consequences of decision making are made clear. However, with all of the weight that hinges on these things, information, decisions, and consequences provide an excellent opportunity to identify your organization’s top level business risks..

At Rolls-Royce, risk management and S&OP are very closely linked together. In fact it is firmly said and widely believed that if we did not have a S&OP process in place, Rolls-Royce will be at risk to be blindsided by major risks. For instance, we discovered first hand that a drop in demand for a particular product, could put a few of our key suppliers in financial distress even though overall demand was growing. We looked to S&OP to provide us with information that allowed us to developed and implement a risk mitigation process as well. We also found that building up our inventory in anticipation of an increase in orders from a major customer was ineffective thus causing Rolls-Royce to rethink how we design our products so as to minimize inventory exposure in future.

I firmly believe that S&OP is the pivotal point in capturing and managing risks provided your organization has implemented a proper risk management process. In fact, an active Risk Manager is an integral part of the S&OP team at Rolls-Royce. Obviously, not all risks are created equal. Some selective risks are reported to the company Board of Directors. This helps to get the executive help needed to mitigate these risks. Managing risks allows us to learn and develop new ways to escape the same risks in the future.

I very much look forward to being able to share some insights into Rolls-Royce’s “S&OP Journey.” More importantly I look forward to being able to learn from a large group of Professionals at the IBF & APICS Best of the Best S&OP Conference

Sanjiv Sharma
Head of Sales and Operations Planning
Rolls-Royce

Hear Sanjiv Speak at:

Andrew Lewis - Electrocomponents

The Background…

Throughout a financially turbulent 2009, the Supply Chain at Electrocomponents plc needed to better integrate into the business and become more intelligent, agile and effective. Just two years into our long-term transformation journey, our key innovations have been to:

• Introduce formal forecasting.
• Embed leading Supply Chain thinking including demand planning and SIOP where many thought it couldn’t be done in an industry like ours.
• Integrate leading edge forecasting technology.
• Identify unsatisfied demand resulting in back orders, lost sales or substitutes.
• Transfer our Supply Chain from the UK to an international focus.

The Burning platform…

Our Electronics Division’s strategy to expand our global range by 120K products over the next 2 years required a step change in forecast accuracy and safety stock policies. This in turn would  support the high level of New Product Introductions (NPI) that would be involved. Our outdated processes had been designed to cope with circa. 5,000NPI/annum and had to be changed so  that they would be capable of coping with 5,000NPI/month.

A key challenge for the Supply Chain in most organizations is aligning the business behind a single set of numbers. We chose a SIOP process rather than S&OP process to manage this because we do not consider inventory to be waste, unwanted or an accident in our business. Inventory  is fundamental to our business model as a distributor.

Above all else, it has been real innovations like positioning our Supply Chain within a Demand Driven Value Network , SIOP, and the development of true lost demand that have started to shape a truly agile and intelligent global Supply Chain at RS Components. In the future we hope to integrate trend software drawing from our eCommerce site to further strengthen our demand signals.

The payback…

Our Supply Chain performance has improved with:

• Increased service to our customers whilst adding nearly 60,000 products per year.
• Process 40,000 parcels per day (1 every 2 seconds), and deliver 99.8% on time in full.
• Maintain stock turn and increase cash flow during the difficult transition period.
• We have gone from 5,000 New Product Introductions (NPI)/year to 5,000 NPI/month!
• Stock Availability greater than 97.5%.
• Reduced Supply Chain cost by 9.5%.

We have learned a lot of lessons, some of them the hard way, as we have taken this journey and I look forward to sharing our experiences with you at IBF’s Demand Planning & Forecasting: Best Practices Conference in Dallas, Texas.

Andrew Lewis
Head of Global Supply Chain Planning
Electrocomponents plc

Hear Andrew Speak At:

Lora Cecere - Altimeter Group

Trading Places

The storyline is an old one. It was the theme of the 1983 American comedy titled Trading Places starring Dan Aykroyd and Eddie Murphy.  You may remember it; It is one of my favorite funny movies in which an upper class commodities broker and a homeless street hustler switch roles when they are unknowingly made part of an elaborate bet.

It is an ageless theme where someone less fortunate trades places with a more fortunate person.  As a child, I was enthralled as I saw it play out in Mark Twain’s Prince and the Pauper and Disney’s Parent Trap. While these are fictional stories, this week, I found a story where it happened in real life. Some of my favorite supply chain management leaders, organizations that I have worked with over the past seven years, had traded places in their organizational capabilities to forecast demand, and it was not a conscious choice.

Prelude

Before I tell the story, let me share a quick perspective on benchmarking demand metrics.  I have been working in this area for seven years.  It is one of the hardest area of the supply chain to benchmark.  I would like to take this opportunity to share my personal experience.

While companies eagerly want the data that benchmarking reports provide, benchmarking forecast accuracy is tricky.   Why is it so hard?  Let’s start with two major reasons:

• It’s Hard to get Apples to Apples.  It is a Fruit Basket. The first reason that makes it tough to benchmark forecast accuracy is that every company does it differently.  When doing this type of work, it is essential to have an “Apples to Apples” comparison.  To do this, you need to look closely at five variables:  frequency of  planning, granularity of the planning or does the organization use  monthly, weekly or daily planning, the construct of the data model, the input into the data model (E.g. shipments, orders, channel data), and the drivers of demand forecasting variance such as promotions, seasonal builds, etc. To get it right, the data must be scrubbed and normalized to ensure an “Apple to Apple” comparison.  As a result, companies should never accept data from self-reported sources.
• The Apple doesn’t fall far from the Tree. The second reason that benchmarking forecast accuracy can be difficult is the fact that the data can be hard to get.  To be useful, and since market conditions change, the data set needs to represent a like peer group from the same point in time.  Since many companies have multiple supply chains, and competitors tend to not want to share data directly with their competitor, getting the data is quite a feat.

Prologue

I ran into the, CEO of a major forecasting software developer earlier this week, and I was excited to find that he had just finished a project to benchmark demand data for consumer products companies to be deployed with his software solution.  Five of the companies were organizations that I had benchmarked in 2003 and worked with over the past five years.  While, neither he nor I can share the names of the companies, I would like to share my insights on their journey. It is truly a story of Trading Places. (In table 1, I have made up fictional names to hide the identity of the companies involved in the case study.)

The Story

While this story may not be as much fun as the original Trading Places movie, it is a real story where a focus on supply chain basics made a difference. In Table 1, I show the relative positions of the companies in the two analyses:

Table 1: Comparison of Five Consumer Products Companies Forecast Accuracy

Progress? For the group of companies that were benchmarked, the average monthly Mean Absolute Percentage Error (MAPE) for a one month lag was 31% + 12%.  Data eight years ago for the same companies was an average of 36% + 10% MAPE.  What was the result?  This group of consumer products leaders has gotten slightly; but not significantly better in demand forecasting.  They have weathered the storm of market changes that could have made the forecast much worse.  The industry has experienced major shocks including shorter product lifecycles, product proliferation, higher levels of promotions, changes in competitive behavior, and global expansion.

Better Math? Consistent with other industry studies over the last ten years (E.g. IBF, AMR Research, etc); the data from the present study shows that we have not made progress to improve forecasting & planning processes in leaps and bounds through the use of statistical models.  In the new benchmarking study, the use of statistical modeling software improved the forecast 3% on average (on a MAPE level with a 1 month lag) when compared to a naive forecast where this month’s volume planning is based on what was shipped last month.  In the top quartile of customers, the impact was 2X or a 6% improvement in MAPE.  A fact that was consistent in both studies showed that when the forecasting group reports to sales, the forecast bias is higher.

What is a 6% improvement in forecast accuracy worth?  Based on AMR Research correlations, a 6% forecast improvement could improve the perfect order by 10% and deliver a 10-15% reduction in inventory. Slow moving items on the tail of the supply chain are most greatly impacted by this.  Unfortunately, most companies let their supply chain tail whip them around.

It doesn’t just happen.  Basics matter. For me, the interesting story can be found beneath  the data. I am referring to the switch in position of the players over the course of the past eight years.  In this time period, the best in Class Company from 2003 became the worst performer and two lowest performers propelled themselves upward.  As I thought about why, and recounted my many experiences with these companies, several ideas came to mind:

• Moving down. The company that showed the worst performance in current benchmarking and the best performance in 2003 had a very high bias.  Why do you think this is the case?  The company made a decision shortly after the benchmarking in 2003 to have the forecasting group report through sales where there was a pervasive belief in the organization that if the company over-forecasted that sales would be higher.  This decision increased bias and cast a cloud over the forecasting & planning process.  The lack of a “true North” in the organization became a stumbling block to improving forecast accuracy.
• Moving up. The companies that moved up in the analysis, focused hard on the basics. This included efforts to clean data, frequently tune supply chain planning software, a strong corporate demand planning team that reports through supply chain and the use of the statistics.

Thoughts on tactical forecasting: While technology vendors like to brag that the use of their technology will make a difference in supply chain leadership, the data here is inconclusive to that point.  Instead, what made a difference in relative position was the process, data, and organizational reporting.  I know this may  not be the sexy stuff, but the basics matter.

Wrapping it Up

I commend this software developer for spending the energy and the manpower to benchmark their client base.  This type of commitment to ones client base differentiates and creates long-term relationships.  It is my hope that this type of analysis will be able to be part of continuous efforts for supply chain leaders.

I look forward to sharing these journeys and many other lessons that I have learned from my experience at IBF’s Demand Planning & Forecasting: Best Practices Conference w/ Demand Management Forum in Dallas, Texas USA this coming May 2011.

Please let me know your thoughts!

Lora Cecere,
Partner
Altimeter Group

Hear Lora Speak At:

Mike Gilliland: The BFD

Our ability to forecast was met with increasing skepticism in 2010 – and this is a good thing.  A decade ago, the thrill of technological innovation provided hope that more data, bigger computers, and fancier models would one day solve all our forecasting problems.  Yet we now have more data, bigger computers, and fancier models than ever before – and our forecasting challenges remain.  Is there no hope?

While we’ve acquired a healthy skepticism this past year, we’ve also come to recognize that some things are forecastable to a reasonable degree of accuracy – and some things are not (at least not to the degree of accuracy desired).  For behaviors that are amenable to statistical forecasting methods, we can focus on automatic model building, and the overall efficiency of our forecasting process. 

Methods like Forecast Value Added analysis allow us to streamline our forecasting efforts by identifying the waste and non-value adding activities in our process.  (The goal being to generate forecasts as accurate as they can reasonably be expected to be, and doing this as efficiently as possible.)  Sales and Operations Planning, with growing adoption and better execution, helps mesh a demand forecast with supply capabilities.  Visibility to demand/supply imbalance, provided through S&OP, lets management act in ways that are most beneficial to the health of the organization – by better (and more profitably) serving its customers.

For behaviors that are not amenable to statistical forecasting methods, recognition of this “unforecastability” is a key first step.  We wisely do not apply super-human efforts to forecast Heads or Tails in the tossing of a fair coin, because we recognize the randomness and our inability to improve upon a simple guess.  In the business world, when we cannot expect an accurate forecast of customer demand behavior, there are still many things we can do so solve the business problem.  These “alternative approaches” just may not involve forecasting.

Supply chain re-engineering is a well-recognized method for reducing an organization’s reliance on highly accurate forecasts.  When a supply chain is more flexible and responsive, it can react to demand as it occurs.  Postponement strategies, where final configuration or packaging of finished product is delayed until the demand signal is received, is one way to accomplish this.

Demand smoothing is an approach for reducing volatility in demand patterns.  Organizational policies and practices, like the quarter end “push,” or ill-designed pricing or promotional activities, can create demand behavior that is more erratic – and therefore more difficult to forecast.  The demand smoothing approach looks for ways to encourage more stable, more forecastable, and (likely) more profitable demand from your customers.

Finally, the pruning of extremely low volume items can lead to surprising growth for the products that remain.  All organizations eventually have their dead weight – those aging products near the end of their lifecycle, or newer products that never caught on.  Rather that encumbering the sales force, ordering systems, warehouse pallet spaces, planning systems, and (most costly) management time dealing with such products, it is often best to just get rid of them.  A CPG manufacturer was able to cut 25% of its product portfolio that accounted for only 0.5% of total sales the previous 12 months.  An apparel manufacturer found that 50% of its items account for only 1% of sales.  A simple Pareto chart (ranking items by volume or revenue) can reveal the opportunity at your organization.

The growing recognition of the limits of forecasting can lead to benefits in two ways.  For the “forecastable” products, attention can be focused on automation and efficiency of the forecasting process – and not wasting effort pursuing unachievable levels of accuracy.  For “unforecastable” products, alternative approaches can help solve the business problem without reliance on better forecasts.  Just because we are forecasters does not mean we can solve every business problem “if we just had a better model.”  As forecasters we can help organizational performance by bringing these issues to management, and guiding them to the right decisions.

Mike is author of The Business Forecasting Deal, serves on the IBF Advisory Board, and moderates IBF’s annual Demand Management Forum at the spring Best Practices Conference (this year in Dallas, May 4-6, 2011).  His blog The Business Forecasting Deal helps expose the myths, the frauds, and the worst practices in business forecasting, while providing practical solutions to its most vexing problems.

Maria Simos CEO e-forecasting.com

Wednesday afternoon kicked off this year’s Institute of Business Forecasting & Planning’s Best Practices Conference with a near-standing room only pre-conference Demand Management Forum.  The Forum was led by Mike Gilliand from SAS and a panel of experts who brought the group through three topics: (1) Applying demand management sensing, shaping and creating, (2) What management must know and (3) Worst practices in business forecasting.  The forum touched on many key issues faced by demand planning and forecasting professionals, giving the group a taste of what’s to come over the next two days.

What makes this group so unique is that they are all demand planners. While they hail from different organizations they share in this specific role and are all part of a community dedicated to sharing and helping each other learn new techniques and working through challenges together.  The last session, which was cheekily dubbed ‘a confessional’, had the panel and audience sharing stories of what didn’t work.  Try to imagine a room of over 75 people openly sharing mistakes and what was a disaster in their organization. For me this was such a refreshing experience seeing heads nod throughout the room as each participant spoke of their woes and a fury of note taking as a panelist or fellow forum attendee would provide some solutions or ideas for how to alleviate the issue.

Panelist Jonathon Karelse from Yokohama Tire shared a worst practices experience and led the group through his folly of using a collaborative forecasting system for tire demand that did not work.  While this was a method he learned at a previous IBF event, it happened to be ill-executed at Yokohama and led to excess inventory because of the bull-whip effect and beer games.  One might ask “What do beer and whips have to do with forecasting tire demand?” Jonathon explained the bull-whip effect that as the velocity of the end of the whip gets so high that once it hits its target and ‘snaps’ what you are hearing is the end actually breaking the sound barrier. He used this analogy to explain what happened to the errors in supply chain. The Beer Games reference eluded to an MIT experiment which showed that a lack of trust within an organization can cause too much inventory.  The Lessons learned were that removing the effect can be done by getting closer to each piece of the supply chain (and Jonathon still has a job so it must be true).

A few other ideas that came up during discussions:

• Arbitrary forecast error targets – Why does your company shoot for 5%? Is that realistic? Perhaps a more sound approach would be to work towards continuous improvement and aiming to beat the standard forecast model.
• Location-product combination – Working to prioritize this combination is important.  A 10% error in one location may not have the same impact and financial loss as the same error  would cause in another location.
• Excess of meetings – Having too many meetings and reports takes too much time away from actually analyzing your forecast.  Is your organization over-doing it by having the demand planner’s time split into too many directions?
• Backing into a number – Just don’t do it, whatever you do. This will come back to haunt you.

Today many of the panelists along with other demand planners attending the conference will give presentations that will go  more in depth into challenges and solutions faced by those in the role of  demand planner.  Is it all for not? Anish Jain shared with the group some statistics found in collected data the IBF has accumulated over the years which shows that forecast accuracy has increased while inventory levels have remained the same.  What does this mean? Are the forecasts being taken seriously?  I imagine this is a topic that will be talked about more throughout the conference.

Maria E. Simos is CEO of e-forecasting.com, an economic research and consulting company based in Durham, NH with clients ranging from media, academics, federal banks, major manufacturers to other consulting firms.  In her role, Ms. Simos works to further develop the reach of e-forecasting’s economic data and reporting capabilities. She also works closely with clients to ensure that they are receiving the important forecasts, economic data and support needed to be successful. She promotes the work of e-forecasting.com and provides economic analysis through her twitter account (@mesimos) and via other social media outlets.  Ms. Simos holds a Master’s Degree in Management from Carnegie Mellon University where she focused her research on management and network analysis. Her research explored social and business networks and their tie in to culture in organizations.  Her undergraduate study was completed at the Tepper School of Business at Carnegie Mellon.