5.3. Ensuring Procedural Quality at LeitaxThe quality of the plans is determined not only by the quality of the information used in the planning process, but also by the procedural quality of the planning process—the appropriateness of the perspectives and the soundness of the rules of inference and judgment used for developing and validating the plans. Procedural quality can suffer from awareness of different stakeholder needs, since incentives and priorities can bias the assessment of a plan’s validity. Recognition of this dynamic leads the forecasting literature to argue, for example, for the separation of decision making from forecasting (Armstrong, 2001). We found that procedural quality was enhanced at Leitax by (a) mechanisms that promote overall soundness of individual inferences, and (b) explicit and extensive validation across the organization. Leitax’s S&OP process included specific mechanisms that promoted the soundness of the rules of inference and judgment that would be used to validate the information in the BAP and the resulting forecast. Mechanisms that promoted procedural quality in forecasting included the combination of multiple forecasts in the consensus forecasting process, a focus on sell-through instead of sell-in, forecasting at an aggregate level, and the use of statistical forecasts to spur discussion about the assumptions behind the forecasting. In the forecasting literature, it is well known that combining forecasts, even through simple averaging, can improve accuracy (Lawrence, Edmundson, and Oconnor, 1986). The emphasis on forecasting sell-through provided a reality check for sell-in forecasts and shifted the focus away from sales incentives that could compromise forecast accuracy. Mechanisms for promoting procedural quality in financial validation included using BAP data to convert the forecasts in units into their monetary equivalents. Mechanisms for promoting procedural quality in operations validation included publishing production requests to suppliers more frequently (see **citation omitted** for a full discussion on how the implemented process ensured higher forecast accuracy). The S&OP process also included explicit and extensive validation across the organization, which in turn increased each function’s awareness of—and therefore its responsiveness to—the important needs and perspectives of other functions’ stakeholders. The separate and explicit validation steps ensured that function-specific concerns were given individualized attention so that they could be collectively planned for rather than being overemphasized by one function or underemphasized by the others. For example, feedback from operations and finance validations directly prompted changes to the product offering and promotions plan in the BAP and then indirectly prompted changes in the forecasts, rather than inappropriately affecting the forecasts directly. Constructive engagement in the validation steps of the S&OP process contributed to improvements in procedural quality. In the consensus forecasting meetings, the attending functions were actively engaged in reconciling differences in the forecasts generated by the sales force and the product planning group. By surfacing the private information (or private interpretations of public information) that motivated objections to the proposed consensus forecast, these discussions strengthened the procedural quality of the forecasting step. Open discussion of a particular function’s forecasting logic served to filter out poor rules of inference. Constructive engagement, by its very nature, ensured that the concerns of different stakeholders were at least partially addressed.