I have worked for world leading companies in both scenarios for many years, and can tell you that the dimensionality, caos and scale of the manufacturing indust

I have worked for world leading companies in both scenarios for many years, and can tell you that the dimensionality, caos and scale of the manufacturing industry is difficult to match. Actually, depending on the place, it is difficult to comprehend, even for those who have been working inside that industry for just a few years. Retail's challenge most of the time is the lack of resources on those situations when the business is a low margin one or they just don't want to invest. Dealing with unreasonable and incompetent external customers is frustrating too. Receiving incompatible POS data from each customer, realigning sales organizations or cleaning products hierarchy, and even, getting the numbers from sales and finance to match is a pain. But the amount of processes is about one or two orders of magnitude smaller. In addition, the amount of data collected is huge (Plant Historians alone collect 200k to a 1M values every second), and your KPIs can be as diverse as the many internal and external regulatory agencies that oversee your processes. Consider just a few of the systems in addition to PI Historian: ERP (you know this one), Manufacturing Execution System, Laboratory Information Management System, Laboratory Electronic Notebook, Change Control System, Non-Conformance/Corrective Action System, Process Control System, Building Management System, Preventive Maintenance System, Learning Management System, Raw Materials Information System, Regulatory Submission Information System....and those are only the big ones. When the plant is in a highly regulated business like pharma, the amount of bureaucracy needed to change a single parameter in a process control system is disheartening. FDA specifically, is mandating pharmaceutical industry to perform process monitoring to prove that the processes remain stable. In these industries we do multivariate Statistical Process Control, not because of the big data trend, but because we have been able to save multi-million dollars batches by observing weak multivariate signals that would pass unnoticed in a typical, retail oriented OLAP cube.