Data Requirements for S&OP Global Planning

In this blog post, we’ll discuss the additional data requirements of a Systems of Differentiation from a Systems of Record and how it can be leveraged to enable global planning. We covered the need for moving from sequential planning to global planning in a previous blog post.

Data Requirements for S&OP Systems of Record vs. Systems of Differentiation

The data requirements for an S&OP Systems of Differentiation is additive in terms of the S&OP Systems of Record. In fact, getting 20% additional data (in addition to what they already have from their S&OP systems of record) could give them 80% or more of value through improved profit margins and revenue. Table 1 illustrates this point.

Table 1. Data Requirements for Enabling S&OP Systems of Differentiation

Additional Business Value that can be Gained through this Enhanced Data

In order to help explain how S&OP SOD solutions can provide additional value through the additional data identified in Table 1, we can look at demand optimization, supply optimization and financial optimization as three distinct examples.

Demand Optimization versus Demand Planning

S&OP processes which are driven by a layer based approach look at demand planning as the first step of a demand planning implementation. The focus of this approach is towards enabling the ability to generate demand forecasts at a SKU/Location combination or maybe at a higher level of the product hierarchy for a period of say 8-12 months.

The S&OP SOR focus on automation of existing Excel based demand planning processes. They have basic statistical forecasting processes which assume price and average cost/unit as a given.

Additional Data:

• Price
• Impact of price on demand

Additional Value through Demand Optimization using a S&OP SOD:

By focusing on additional data elements such as price and the impact price will have on demand, companies can now enable demand optimization scenarios.

• Product profitability and product mix by channel or region.
• Customer profitability analysis and bid support.
• Price and promotions optimization at the monthly level.
• The demand optimization has to be done simultaneously with supply optimization.

Supply Optimization versus Supply Planning

The S&OP SOR focus on Rough Cut Capacity planning instead of true supply planning. These solutions make simplistic assumptions of aggregate capacity resources without consideration of multiple resources, batch processing,

Additional Data:

• Fixed Costs
• Resource utilization as a decision variable
• Costs as a decision variable

Additional Value through Supply Optimization using an S&OP SOD:

Opportunity to optimize supply based on constraints instead of just matching against demand. The solution should model fixed cost of operating resources as well as the variable cost of producing units of products. Instead of making decisions based on average cost/unit of manufacturing products, the tradeoffs should be made with demand and financial dimensions simultaneously. Questions such as “what is the average profit margin I will make with operating my resource for 1 more hour?” should be answered very easily by the solution.

Financial Optimization versus Financial rollup/disaggregation

S&OP SOR have very limited capabilities in this area. These solutions make simplistic assumptions on Average Selling Price and Average cost. These solutions essentially aggregate /disaggregate financial data.

Additional Data:

• Taxes
• Working Capital
• Currency as a decision variable

Additional Value from Financial Optimization using an S&OP SOD:

The solution should provide the opportunity to move from just meeting demand to optimizing commercial objectives such as success of new products and also allows to evolve from estimated minimum cost to optimizing profit while meeting growth targets. Cost modeling should evolve from predefining fixed/variable to tying them into the decision. For example, product sourcing assumes capacity is a fixed cost, while capacity planning treats some capacity (such as shifts and overtime) as variable. Decisions should be made based on corporate and business unit financial statements – the solution should reflect these including representing policies and targets as constraints or objective function. A more complex constraint at organizations would represent ratios as constraints or objective function. For example, the constraint may be expressed as inventory turns, profit margin or ROIC.

Closing Remarks

In conclusion, by building on top of the infrastructure that existing S&OP Systems of Records planning tools have created and implementing a global planning solution, it is possible to gain significant additional business value with minimal costs.