Those working in the manufacturing sector have to work in a variety of positions such as – billing and ordering of materials, inspecting inventory on the shop floor, hand delivery of shipments, attending quality meetings with the world’s top automotive clients, etc.
It is an anomaly that we still can’t accurately forecast inventory flows, requirements, and prevention of obsolete stock. Why can’t we manage quality, analyze commodity spending, and minimize the efforts and cost of supply chaining?
The immediate answer will be – “Yes, of course. We do it; we do it all the time.” But, how? Even medium level organizations are spending millions of dollars in their I.T. infrastructure. Executives are better informed with the output of executive dashboard systems and management thinks they are in control with pre-defined reports from their Management Information Systems.
Analytics experts are hired. However, the organizations are still provided with inconsistent quality of past data. Predicted models are created which can forecast sales, production, inventory levels etc. straight from statistical software with some level of accuracy, but does that help? Yes – to some extent it’s a good starting point, but the most important angle is missed.
As Peter Drucker once said, “Business decisions should be made at the lowest possible level in an organization.” In this era, it’s not only important to focus on the lowest possible relational/human level, but also the lowest transactional level. We can safely add to Drucker’s comment that decisions should be made with current information & data in real time.
Technology is changing the way we analyze data. Sophisticated programming language that could be run only by IT professionals is no longer the barrier to statistical programs it used to be. Now, with a click or drag & drop on your laptop/iPad, business users can analyze the information of millions of transactional data in seconds.
Thus, analytics in the manufacturing sector supply is a definite force to reckon with and those in the analytics industry should take utmost advantage of it.