13Data Integrity Requirements to Support Master Planning and Scheduling
If you don’t have integrity, nothing else matters.If you have integrity, nothing else matters.
The past 12 chapters contain many examples of simulated computer grids and otherwise calculated master planning and scheduling outputs (e.g., projected available balance, available‐to‐promise) based on actual and forecasted demand data, computer planned orders, firm planned orders, scheduled (supply) receipts, bills‐of‐material structures, safety stock settings, planning time fence parameters, and the like. Future chapters will more deeply explore aggregate planning, resource planning, and demand/supply planning parameters, including inputs and outputs as well as other data elements.
At the risk of stating the obvious, none of these system calculations, algorithms, recommendations, and reports work without clean data. The somewhat tired adage “garbage in, garbage out” remains as relevant today as when it was first printed in 1957 and attributed to Army Specialist William D. Mellin.1
In this chapter, we explain what data integrity is, why it is important, and define the four pillars of data integrity that drive greater understanding, accountability, responsibility, control, accuracy, and consistency of data needed for effective master planning and scheduling processes.
What Is Data Integrity and Why Is It Important?
What Is Data Integrity?
Planners, schedulers, woodworkers, airplane mechanics, artists, and ...
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