As new technology comes down the pipeline, it can be tricky for a busy manufacturing company to know what’s worth their time, how much to invest, and where to begin. Industry 4.0 innovations like Industrial Internet of Things (IIoT) are gaining traction with manufacturers, and, when properly deployed, can help grow a company’s margin or shift the way they do business. IIoT is helping businesses run their manufacturing operations efficiently, increase product quality and consistency, integrate across the supply chain, and stay competitive in the market. So, how do you evaluate if IIoT could have those benefits for you? In this end-to-end IoT workshop, you will learn from manufacturing technology experts who will help you assess your company’s IIoT readiness and provide you a deep-dive into the components of an IIoT infrastructure, how peer companies are making use of the technology, and the tasks and steps needed to go from concept to successful IIoT launch. You will walk away with a foundational plan to do the same at your company.With the information provided in this workshop, along with a demo of specific use cases, you will be able to develop a deeper comprehension of the subject and get the guidance and resources needed to lead an IIoT project at your company.

A major objective of most manufacturing companies’ digital initiatives is increasing operating margins. Cost reduction through production process efficiency improvements, effective asset management, and sourcing optimization for direct and indirect items are some of the primary ways that organizations are successfully improving their profitability and competitiveness. Unlike for direct items, demand and usage for indirect items (also referred to as Maintenance, Repair and Operations, or MRO) varies depending on equipment failures and unpredictable factors. Forecasting the equipment failure and the need for required spare items is difficult. Companies tend to overstock repair parts, but still face stockout situations. Overstocking increases expense budget, hurting the bottom-line results; running out of an MRO item can cause downtime. Hamiltonian’s MRO Optimizer analyzes the Min/Max ordering and other planning rules, and then adjusts the necessary parameters to suggest the appropriate quantity for safety stock and replenishment. Finding the magic number for every part is the key to optimize MRO purchases. The safety stock levels and the quantities of purchase derived by replenishment methods are based on certain assumptions and individuals’ subjective decisions. The forecasts which drive the ordering rules originally start with assumptions for initial inventory levels. Subsequent ongoing replenishment should be based on: Actual consumption Equipment Reliability and Usage Preventive and Predictive Maintenance work orders Asset Criticality Material lead time Future Cognitive Maintenance MRO Optimizer has built-in, rule-based algorithms and machine learning to optimize the replenishment of every item, helping users maintain desired plant service levels while reducing stockouts. This business process automation improves maintenance efficiency and reduces the subjectivity of MRO inventory replenishment decisions.

In this one-hour webinar, we covered the day-to-day and strategic challenges that sourcing teams face, and how Hamiltonian Systems deploys our powerful software Kasei to help them tackle both. Creating sourcing data, managing the workflow, automating manual tasks, and more topics are demoed in the Kasei interface.

Users are invited by Kãsei administrators by clicking on Users icon in the Kãsei Modules page.

The most essential data structure through which we access data in Kãsei is a Template. Templates allow users to work with data for viewing, editing, governing and enforce security. A Kãsei template consists of fields or columns that define how the data is manipulated and stored. Templates can have static data as well as dynamic data.

Kasei application allows functions to be created and attached to work flow buttons and template pages to enhance the functionality. Kasei functions are written in Java.