As manufacturers continue to face intensified global competition and increased pressure on profit margins, they can use analytics to identify ways to improve efficiencies and run their operations more effectively.
“Virtually every facet of the manufacturing operation – product design, customer relations, finance, risk, supplier and partner management, and sales and marketing – is now on a quest for more accurate and accessible information,” according to a Deloitte post in The Wall St. Journal.
Analytics can help manufacturers with a number of opportunities including the ability to identify, quantify, and prioritize margin improvement opportunities by delivering thorough information that provides insights into SKU velocity, price band and segment performance, etc.
Analytics can also help manufacturers gain a deeper understanding of different ways in which production costs affect the bottom line by applying analytics to design and supply chain data.
For instance, discrete manufacturers can apply manufacturing analytics to pinpoint whether – and to what extent – production capacity is being fully utilized as well as to pinpoint opportunities for acting on improvements to better maximize productivity.
Manufacturing executives can also use predictive analytics for better production planning to prepare for fluctuating demand. Analytics can be taken a step further in this scenario.
Based on seasonal selling cycles and information about the ebb and flow of demand for particular products over a period of time, it’s not enough for manufacturing leaders to just predict whether their organizations have the proper resources to meet product demand for a certain timeframe.
Analytics can also help manufacturers of multiple products make adjustments to production.
Let’s say a toy manufacturer is able to determine through the use of predictive analytics that seasonal demand for a line of robotic action figures is expected to rise beginning in late October based on the anticipated holiday shopping demand.
Meanwhile, demand for a sliding ball game made by the company typically spikes in the summer months but tends to ebb during the fall months. The manufacturer can adjust production accordingly to ensure that it’s applying the right mix of resources to both production lines in order to maximize its productivity.
Production analysis can further be used to identify material waste that’s occurring and help identify manufacturing techniques and processes that can reduce or even eliminate material waste. For example, a manufacturer of leather pocketbooks or “crossbody bags” calculates the amount of leather that’s used to make 100 bags.
Analytics tools can also be used to examine the amount of waste that’s generated from the company’s manufacturing processes to determine whether: (a) there might be more efficient manufacturing processes that could be used that would result in less waste; or (b) whether the leftover leather could potentially be reused for other product lines (e.g., wallets, necklaces, etc.), or sold to another manufacturer as a means to better optimize the investments in the raw materials.
- Subscribe to our blog to stay up to date on the latest insights and trends in predictive analytics, and manufacturing analytics.