In most companies, Sales and Operations Planning
(S&OP) is a tactical plan(1) designed to align the business by
balancing supply and demand. It is a key business process in
manufacturing companies.
The process varies greatly across
companies; from simple, semi-regular meetings to an on-going,
structured process with automated data integration that aligns the
enterprise in one plan of record. Appendix 1 provides a summary of
the evolutionary path from “Basic” to current “Best Practices” in
S&OP .
This article focuses on what is at the core of
S&OP -- how to balance supply and demand. In so doing, we will
explore what S&OP can learn from other industries(3). With this
in mind, the below picture illustrates a typical evolution of
complex decision making processes:
In the above picture, the path of
evolution proceeds from left to right. If we compare current best
practices in S&OP with the above, we find that it matches the
third box; “Integrated analysis, standard and ad-hoc reporting”.
The fourth box, “Decision Support tools”, is what other
industries have implemented to support complex decision making
processes. Companies that have invested in well-designed Decision
Support tools have reported revenue increases from 3-12% and profit
increases from 4-18%.
Current best practices in S&OP
fall short of best practices in other industries in two areas:
- Objective: Current S&OP practices are largely an
exercise in order fulfillment, less in maximizing corporate
profitability.
- Analytics: In spite of large investments in
“best-of-breed” systems, current S&OP decision making is still
largely a manual task without much analytical support.
Objective
Although a key driver of corporate
profitability, S&OP remains close to its supply chain roots as
an exercise in making sure customer demand is met. Other industries
have taken this a step further by throwing corporate profitability
into the mix -- to balance supply and demand in a way that yields
the greatest profits for the corporation, short and long term.
Balancing supply and demand then becomes an exercise to find the
most profitable match between available capacity and demand.
Analytics
S&OP it is still largely a manual
decision making process. With complex supply-demand structures and
information overload this can be a daunting task, resulting in
missed opportunities and unforeseen “surprises” when the plan is
implemented. The problem with manual decision making is that humans
generally compensate for complexity by simplifying information and
then making “sweeping” decisions that ignore important interactions
that take place between lower level supply and demand entities. This
is particularly difficult in companies that sell configured
products, and/or where different products or components compete
about supply resources.
This is where Decision Support tools
can help – they are not replacing decision makers, but they offer
recommendations, insights and what-if capabilities that manual
processing simply is unable to do.
Future
Best Practices in S&OP
Other industries have learned
that their problem solving tools -- in addition to providing the
right information -- also must be able to make recommendations about
how they can improve decision making. This paradigm shift – from
(merely) producing information to making recommendations – is a
proven solution that lends itself well to companies who have reached
(current) best practices in S&OP.
Before diving into
what the solution would do, let us take a look at what is required
in order to be successful:
- Global Perspective: Solutions must be based on an
enterprise-wide perspective. This is not to say that it needs to
“be everything to everyone”. It simply means that the solution
must aim to cut across functional boundaries to provide
recommendations that benefit the company as a whole – not specific
functions or departments.
- Clear Objectives: Developing decision support forces
the company to define their “real” objectives: Maximize profits?
Maximize revenues? Maximize market share? Minimize
costs?
This is not to say that other things become
unimportant – it simply means that one objective is overarching,
and the others become subordinate to that objective. The
overarching objective of most companies is to maximize shareholder
value, short and long-term – a goal commonly translated into
maximizing profitability subject to meeting given revenue
targets/growth rates.
- Leadership: To succeed, visible and active sponsorship
at the highest level is imperative.
- Roadmap: To start out, building decision support
systems require a well-designed, pro-forma model of how the
business process should work. With this as a starting point, the
company can start “chipping away” with a clear end-point in view.
Below is a simple diagram illustrating such a “meta
model.”
The following diagram illustrates how
business processes in an S&OP process can interact in order to
support supply and demand decision making to maximize corporate
profitability:
The blue-print model shows that
corporate revenue and profit are a direct result of how well supply
and sales plans are aligned – represented by the product mix plan.
How well this is done, depends on (1) how accurate the demand
forecast is, and (2) whether and how supply and sales plans are
aligned.
A well-designed decision support tool can help
calculate the best product mix given demand forecasts and capacity
constraints. This, again, should provide input to supply planners
and sales about what products to produce and sell, respectively. The
interesting aspect of this model is the dynamic interaction that can
exist between supply and demand plans. For example, if demand (or
prices) change, how will that affect the supply plan? Conversely, if
the supply plan (or cost structure) changes, how will that affect
the sales plan?
Moreover, a well-designed decision support
tool can point out bottlenecks in the supply chain and put a value
on what it would be worth to increase production capacity. On the
sales side – a decision support tool could provide insights about
what it would be worth – in terms of corporate profits – to modify
sales prices. As other industries have discovered, global,
closed-loop what-if analysis tools are very useful towards improving
corporate profitability.
Conclusions
Decision support tools do not replace decision makers –
the real value of such tools are the insights gained from the
exercise of using them, resulting in better business decisions and
fewer unforeseen surprises.
This article describes what many
would label a “revenue management solution” to manufacturing. It
includes a decision support tool with a powerful analytic engine
that takes as input supply and demand data and computes a product
mix that maximizes revenue, profit -- or something else.
Demonstrated in many other industries, manufacturing
companies could benefit tremendously from using decision support
tools to improve decision making in their S&OP process.
APPENDIX
1 – Current State of S&OP Evolution
The following
diagram illustrates four evolutionary stages in manufacturing
enterprise planning (including S&OP), from “Basic” to “Best
Practices.” Each stage is described in terms of the approach and
systems used.
Footnotes
(1) Tactical
planning means that it is at a higher level than day-to-day,
operational decision making, yet constrained by existing capacity
and other long-term resource or customer commitments.
(2) How
Does Your Company Compare to the Best Practices in Enterprise
Planning (Glen W. Margolis; Perspectives On Enterprise Planning,
April 2005).
(3) Industries like transportation, hospitality,
freight, and financial services have a long history of exploring and
developing tools to assist management in solving complex
problems.
About the author:
Rick Blair, CPIM
Rick Blair is Senior Director of Services for Steelwedge Software.
Rick has held consulting and operations management positions with several enterprises. His scope of management responsibility has included Project Management, Demand Planning, Forecasting, Procurement, Production Planning, Scheduling, Inventory Management, Warehouse Management and Operations Management. Industries served include steel, aluminum and aerospace structural adhesives. Rick was graduated from the University of California, Berkeley School of Business with dual emphases in Marketing and Management Science. Rick is APICS CPIM certified and is the VP of Education for the Golden Gate Chapter of APICS.
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