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Sensai recently unveiled a list of the top five plant floor risks that reduce efficiency, drain productivity and negatively impact business results if left unattended. The list is based on the company's expertise as well as insights from its pilot programs at organizations in the automotive, construction materials and consumer goods industries.
"Industry 4.0 is making the plant floor much smarter than ever before, but that is not to say that automation, data exchange, IIoT and cloud computing can manage itself," said Porfirio Lima, CEO of Sensai. "Companies need to focus their efforts on identifying and addressing pain points and engaging their workers in the change process to bridge an understanding of what must be done to realize the full potential of these innovative technology solutions."
According to Sensai, the top five issues impacting manufacturing operations today are as follows:
When an organization has to delay or shut down operations due to aging or failed machines, this can have a serious impact on the safety of employees and bottom line of the business. Furthermore, in order to continue producing at the pace the market demands, companies may have to outsource repairs and production volume, which can be extremely costly.
For factories to be effective, information regarding inventory, supply, deliveries, quality, production, customer support, processing and day-to-day management must all be analyzed, monitored and updated on a daily basis. Important business decisions often need to be made using a comprehensive range of data from the production floor to spreadsheets and clipboards. Without an efficient system, operations managers and their teams waste time searching for the necessary information vital to making these critical decisions.
As important as it is to centralize data, it is even more important that the data is accurate. If the data is not reliable, companies may end up choosing the path of most resistance, resulting in wasted or misused resources and a complex operational process. Manual data entry is prone to human error, which can lead to poor business decisions that stem from misleading information. With facilities that are both robotic and manual, operations must still pay close attention to the actionable data as it comes in, which means there is an additional layer of complexity. Calculating inaccurate key performance indicator (KPI) data is something that continues to haunt many production managers today. With the right technology and accurate data, decisions can be made more effectively and efficiently.
When new employees are hired, there is often a steep learning curve, requiring numerous hours of coaching, training and shadowing veteran employees. However, many companies do not have the internal resources to properly train and onboard individuals, increasing the likelihood of operational errors, unapproved work-arounds and more. Alternatively, when organizations lose top talent to a competitor or retirement, those years of experience walk out the door with them. Depending on the existing management protocol, both of these factors can impact the efficiency and productivity level of an entire company.
The complex relationship between a machine's health, the processes' parameters and the material's conditions all have a tremendous impact on a manufacturer's final product. When any of these elements is not working correctly, it can be detrimental to productivity. Having the correct process to analyze and create robust models gives guidance to operators for optimizing performance, quality and uptime. Machine learning also enables smart process controls so corrections can be made automatically and even autonomously considering all the critical and relevant variables.
For more information, visit www.sensai.net.