The central theme of the presentation quickly established that S&OP and New Product Planning activities are inextricably linked. So much so, that you cannot have one without the other.

To ensure that all in attendance were on the same page, defining S&OP was at the top of the Agenda. This was extremely important because while S&OP does have clearly defined steps, it is very often different across many organizations.

The basic definition started with a simple sentence, “S&OP is a process by which a business makes its decisions”. Though simply stated, the definition runs true in that in any well-oiled S&OP process, simplicity can be refreshing. Without any complications, the definition was extended to describe it as a Process that results in a single achievable plan, a collaborative process to align decisions, activities and resources against a defined business strategy. And finally, S&OP must be a data and fact-driven process.

In S&OP, Reality must Rule. Here is an example of how a New Product Introduction (NPI) process can self-derail despite seemingly going thru the correct process and procedures. Can one imagine the bewildered faces on the NPI team members when what was supposed to be a home run product launch, actually proceeded surprisingly to nose-dive into complete failure? Post-launch root cause analysis exposed two key failure points – that the product had not been test-marketed to consumers, and the retailer surprisingly, and despite objections, positioned the product (HDTV Wall mounting bracket) in the Hardware section-not with its offering of HDTV’s. Though there had been a lot of collaborating prior to product intro between Sales, Marketing and Operations, two key pieces of data were inadequate at the very least. The real lesson is that following the steps of S&OP with mechanical regularity, and resorting to using data without a business team analysis (Demand Manager, Marketing Manager and Sales Manager) is an eventual roadmap to failure.

This was one of the supportive themes in the presentation. The NPI process can not merely be rammed into the S&OP with a data dump. The entire data set of any proposed launch must be set against business trends, data supported interpretations of consumer behavior trends, lead-times, verified in-store dates, production capacity review, the predicted effects of possible promotions, and the effects of previous product phase-out. There are other factors to study, but the key point to consider is that collaboration and an informed discussion about the numbers is critically important.

Another key point emphasized was that Market Volatility in today’s consumer products environment can be a moving target with one rarely earning the moniker ‘sharpshooter’. And it is likely to remain this way into the future due to the effects of the quickening pace of innovation, clearly defining the sometimes elusive aspects of consumer behavior, and other factors such as the real and sometimes significant effects that On-line retailers are having on the big boxes. What this really works out to be is that New Product planning and S&OP absolutely need to be integrated into a seamless interface. As compared to the normal gathering of demand data and the following inclusion into the overall Demand Plan/Business Plan, New Product planning, as emphasized by this session’s presenter, raises the bar with respect to collaborative Sales, Marketing and Operations team discussions, consensus-decision making, and robust data analysis. Some additional takeaways, highlighted by the presenter, were the necessity of a Product Review/Life Cycle process and that our best intentions can still miss the target. But that is no surprise, S&OP is both an evolutionary, and a learning process.

A final point… Conference events such as this one, which was sponsored by the IBF, are great learning events as well as a great opportunity to rub elbows with the “Best of the Best”. Check it out in the future.

Richard P. Kerivan
Rpkerivan @ gmail

Kien Leong - JCIT Asia Pacific

On the supply-side, Sales & Operations Planning (S&OP) most commonly fails when an unconstrained plan meets real-world constraints.  In this post, we would like to explore the nature of these constraints and suggest ways to moderate the operations plan in order to come up with a more predictable supply capability.

Process and Product Constraints

Supply constraints can be divided into two broad groups: material and process.  The operations planning objective is to position product and capacity in the right place, in the right quantity and at the right time.

However, depending on the way the organization measures constraints in the operations plan, there are several problems that can occur.  Materials are planned in units over fixed intervals, which are either weeks or months.  This being said, is it also convenient to  measure capacity in units such as the quantity of pieces, cartons or pallets that we can we supply over a given period of time?  In a high-volume, low-mix business this is less of a problem since production lines and factories are rated for a fixed rate of output.  On the other hand, a high mix business needs to share resources between products with different process and work content.  Here in lies the problem with measuring capacity in units of production.

In the real world, process capacity is never effectively managed in terms of volume.  The correct unit of measure for a process is time.  Minutes of run- combined with setup-time add up to a process load.  This is met with hours of shift-time capacity and days of lead-time.  A constraints-based model will calculate these elements over dynamic periods of time.

The bias towards discrete units of measure stems from MRP. A material planning system performs production planning with an infinite capacity model.  If the purpose is to constrain the operations plan, ignoring half of the constraints is clearly not going to work.

Enter Advanced Planning and Scheduling

If you want S&OP to blend well with process constraints, the answer is a finite capacity model.  This begins with production capacity and extends over many more processes in the modern manufacturing business. Advanced Planning and Scheduling or APS systems offer this finite capability.  However, I am not promoting the need to implement yet another software system.  APS systems are often expensive, complex and bundled with extra supply chain add-ons that are not commonly used.  It also seems to be driven towards process optimization.  In the real-world, optimizing multiple factors can get mathematically complex very fast.  Before we optimize the process, we should examine simple prediction.

Prediction of supply capability doesn’t require complicated algorithms.  We can begin with a simple definition of process and drive it to a demand model.

Processes and Products in the Value Stream

Just as constraints are organized around material and process, so too is the definition of value.  Starting with production, the product definition can be found in bills of material and the item master.  Process definitions are most commonly found in the form of routings.  There are many other critical processes to define, but there is much less representation in the enterprise master data of those processes as opposed to routings.

Here we get another glimpse of the bias towards materials. Companies tend to have more detail and accuracy in regards to BOMs than their process routings.  Perhaps this is the reason why it is understandable that the more human side of the business is the one that has a less quantified definition.  However, process innovation has become more important than just product design and features.  As the world realigns to a service- and solution-led global economy, defining and standardizing the process will become increasingly important to having the competitive advantage.

We do not need to start with detailed procedures of customer service excellence,  or face the challenge of building a data-driven sales force.  Most of the added value in the supply chain still relates to the product in terms of  Design, engineering and manufacturing.  These are the first processes where we should add in our constraints-based model.  Linking a schedule of process requirements to demand, we can build a prediction engine that can hold the supply requirements of the operations plan to real-world constraints based on actual resources.

Integrated Planning with Constraints

The process definition is usually tied to demand with a product item or family.  In this way, the sales and production plan can generate a load and capacity requirement.  This method defines the process requirement at the lowest level of granularity which  in the ranges of S&OP is usually a day.  As the production plan is constrained to finite scheduling, this planning interval goes all the way down to hours and minutes.

Time lost during changeovers is a critical component of calculating batch sizes, which in turn constrains the order policy and demand release cycle.  A calculation of fixed to variable costs is central to all calculations for economic lot or order quantities.  All too often, the run quantities and MOQs across raw, semi-finished and finished goods are set with approximations or standards that possibly could have been optimized long ago.  These parameters are the primary factors when determining service level, cost and lead-times. I would suggest that you review them in a dynamic model and align with the demand profile.

Real world constraints can be modeled against the operations plan to improve the predictability and performance to-promise.  My presentation at the upcoming IBF Supply Chain Forecasting and Planning Conference: Asia in Shanghai being held from March 24-25, 2011 will show some examples of this and provide techniques on building a constraints-based model.  You can read more about production planning and scheduling and get resources for finite planning at Production-Scheduling.com

Kien Leong
Managing Partner
JCIT Asia Pacific

Hear Kien Speak at