Issue link: https://industrialengineer.epubxp.com/i/826827
June 2017 | ISE Magazine 45 Industrial engineering is often at the forefront of the practical use of technology, simulation, analytics and op- erational intelligence to improve the systematic approach to process design and improvement. The contemporary industrial engineer now has the ability to use analytics within manufacturing's design and improvement pro- cesses to drive performance and business intelligence to new plateaus. Predictive analytics can help manufacturers correlate pro- cessing parameters to lost overall equipment effectiveness, pre- dict specific preventive maintenance requirements, develop processing expectation patterns to performance and offer a wide range of data-driven business decision support. The key to success is applying the right tools, a systematic approach to the operational intelligence design process and a sustainable model of analysis and adjustments, all while con- taining the efforts in terms of costs and benefits. It is also important to recognize that simulation modeling and predictive analytics are best-suited for organizations fac- ing either high capital investment decisions or that frequently make operational resourcing decisions when faced with vola- tile demand. Collecting the data and then modeling and ana- lyzing it are significant investments. Therefore, you should use modeling and analytics selectively as a risk-avoidance or cost improvement effort. A systematic approach to operational intelligence Our goal is to provide a specific approach to analytic intel- ligence that is realistic, results-oriented and cost-effective. A fundamental requirement of success is a proven systematic approach, a developed template that any organization can implement to meet its competitive challenges. The measures of success include the outcomes of the long-term intelligence improvements as well as the cost and timing of the long-term processes to get there. 1. Pick the members and champion of your perfor- mance/operational intelligence team. The selection of a champion and team members, not to mention leadership and technical support for this type of assignment, often is driven by individual business cultural processes. Three specific recom- mendations could help ensure the long-term success of this endeavor. The first is to select a team leader who believes in statistical modeling and has a practical understanding of com- plex statistical modeling's implications. Second, select a technical member or partner who has a proven record of analytics success in production processes. Often, this is not a current employee. Since this person's role might be project-specific, it's less important to have a full-time worker on board. Third, establish a pilot team and plan to roll the outcomes into the current management's standard operating procedures. The organization can establish new pilot teams as programs roll to the other assets targeted for improvement. 2. Identify the project objectives and expected out- comes of the modeling effort. The selected team must de- termine specific and challenging outcomes for the assignment. The intent is to ensure that the modeling outcomes are real- istic, relevant, cost-effective and understandable. This is often the perfect time for the team to decide whether the modeling efforts will cost too much, if this is the appropriate time or if the needed analytical data is generally available. Depending on the project's objectives, a simplified design assessment may be an appropriate route. 3. Map the intended analytical simulation process in detail. A process mapping exercise can help the team establish a clearly defined set of goals, along with creating a unified understanding of the system. This document must include routine and nonroutine process steps, queueing re- quirements and options, cycle times and variances, statistical models of interference times and occurrences, labor require- ments and limitations, and appropriate production inputs such as volumes, mix and variations. The process of mapping the intended situation will challenge the seemingly obvious inputs and parameters, as well as establish a priority for what analytics are needed to make sure the modeling solutions have the desired effects. 4. Prioritize the analytics selected for the simulation models. Using historical analytics can remove the opinions and assumptions of the team, helping move the discussion to a strictly data-driven model. The sources of data analytics can be similar processes with production monitoring, historical PLC data of capital performance, inventory status and quantities from ERP systems, historical staffing records, etc. These ana- lytics can be collected across minute time frames and clearly define the statistical performance implications of labor, capital, inventory, model mix, etc. 5. Select the appropriate simulation technology and partners. The unique aspects of this type of process design assessment often require teaming partners with investment in technology. The simulation modeling and analytics consulting options are often driven by the marketing programs of these technology organizations. Selecting the appropriate partners is critical to cost-effective designs, timely and relevant decisions and long-term success. Enterprises should base these selections on the partners' cultural fit with the organization, situational production and process experience, and their proven technical capability. 6. Develop the operational intelligence simulation and assess specific costs and benefits. The model develop- ment process will require individual team member expertise, and effectively communicating the programming, analytics, output reporting and statistical transparency can help develop trust. The modeling statistician should provide the team with I