| Best Practices |
|
The
Impact of Forecasting Improvement on Return on Shareholder Value
|
| Conferences & Events |
Forecasting and Consensus Planning in a Rapidly Changing Environment - Webinar with Christine Pfefferle, Director of Global Demand and Order Management at Tellabs, |
| Using S&OP to Improve Revenue and Margins | ||
|
By Anders Gjerde, Senior Manager at Steelwedge Software |
||
|
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:
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:
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
Anders Gjerde is a Senior Manager and Business Development Analyst at Steelwedge Software. Since joining Steelwedge in 2002, he has worked with customers to implement innovative solutions to help them solve a wide range of planning and performance management problems. Prior to joining Steelwedge, Anders was Director of Global Client Solutions at Decision Focus/Talus Solutions (acquired by Manugistics, Inc in 2001). Anders holds an MBA from the Norwegian School of Economics and Business Administration. |
||
3825 Hopyard, Suite 155, Pleasanton, CA 94588
Copyright 2005 STEELWEDGE, Inc. All rights reserved.