Manufacturing is the most challenging aspect of any business. Several factors affect manufacturing such as accuracy, focus on details, and economical use of resources. But, there is more hidden and challenging information to identify significant components. Metrics like quality, dependability, and flexibility are included in this. To find the exact metrics and make informed decisions every business requires managed analytics services.
Finding this true cost of the product involves identifying hidden costs and analyzing the total cost of Ownership (TCO). TCO includes all direct and indirect expenses such as manufacturing, buying, shipping, design, inventory control, labor inefficiencies, and even refunds. However, it also provides the most exact statistics to compare different production technologies, such as 3D printing and injection molding.
Manufacturing analytics solutions are used by manufacturers to track KPIs, decrease unplanned downtime, increase customer satisfaction, and optimize production efficiency. Industry 4.0, or smart manufacturing, is the term used to describe the larger trend. To do this, data gathered from industrial machinery and traditional IT systems must be combined, and analytics software must be used to make better decisions. Additionally, by predicting bottlenecks managed analytics for the supply chain in manufacturing processes that could interfere with order fulfillment, producers can determine the underlying reasons for production mistakes.
With the help of managed analytics as a service, and using managed BI for smart factories, businesses can now automate the process of converting data into insights, which will help them further accomplish objectives like successful customer retention and the ability to forecast customer behavior through predictive modeling and 360 Degree Customer View.
We have covered several important subjects in our blog during the past year. This has involved deciding what has to be improved; that is, figuring out all the possible areas to enhance, picking the crucial ones, and seeing those through to the end. For your improvement activities to produce any meaningful results, this is vital. The key features that set apart businesses that truly improve are knowing what to do and having the discipline to follow through without spreading your available resources. No amount of skill, managerial ability, or procedural expertise can bring about meaningful change in the absence of these qualities.
Numerous hidden expenses exist. While some are particularly detrimental in high-product-mix processes like customized fabrication, others are inherent in practically any value-producing operation. Furthermore, if these expenses are not identified and addressed, they frequently complicate other improvement initiatives. Let us now discuss the ones that are most relevant to high-product-mix operations.
It might be very overwhelming to have all the information you need on what to construct when to build it, what to order when to order it, dates and quantity changes, revision levels, acceptable quality, and a lot more.
Many things can go wrong. The more information there is, the more varied it is, and the more likely it is that something will go wrong if it is not expressed effectively. The information problem causes numerous forms of waste. That might be a monster.
The quantity and diversity of information in a high-product-mix store are unlikely to alter. You have no control over that. However, you can make improvements by converting the input data into a standard output format that is highly accurate, understandable, and actionable.
The idea is to create a system that feeds accurate data to the real processing operations, particularly those that take place on the factory floor. Correctness, clarity, timeliness, understandability for all participants, and identical actionability — the ability to produce the same action independently of the person performing it — are all requirements for the information.
This instruction seems quite lofty. However, upon performing a root cause analysis to determine what went wrong and what causes needless expenses, you will nearly always discover that information errors are at the top of the list. It’s also possible to overcome the majority of these problems with the help of manufacturing data warehouse services or managed analytics.
Here’s a basic yet typical example: A new machine that you recently purchased can process parts 50% faster than the previous one. Isn’t it an improvement? Not if you build to the incorrect revision, overbuild, or build the incorrect thing at the incorrect moment, that is. The costs of correcting the situation will outweigh the throughput gain. And those expenses will be forgotten. These overhead costs, however, hidden, are actual, cumulative, and unavoidable if nothing is done about them.
Because they follow the routine as usual, search costs are hidden. We are used to watching ourselves and other people engage in the common conversation that begins with the phrase “Let’s see… where can that be?” It’s entirely avoidable, fully typical, and also quite expensive.
The one benefit of search waste, which is a type of downtime and information waste, is that it is comparatively simple to improve. Eighty percent of it can be resolved with any long-term 5S/visual workplace strategy. It is one of the few enhancements that is theoretically simple to start. The execution of the sustaining plan is the sole area in which knowledge is needed.
It’s difficult to discover good reasons to tolerate search waste and its associated expenses. Because search wastes time, they typically manifest as variations in efficiency and collectively cause schedule problems and delayed orders.
It is rather typical to move materials around in a plant. Moving people around is also common. However, moving objects and people excessively has an impact that is almost identical to searching excessively. It’s one more traditional waste that lean concepts have found. It can need changes to the structure of plants and equipment, strict adherence to quantity and timing guidelines, and an emphasis on part flow, which makes it more difficult to eliminate. However, much like search waste, it can be greatly improved. To find the best answer, experience is undoubtedly necessary. However, progress can be accomplished gradually. Two excellent examples are cells and virtual cells.
You will generally be taken aback when you “see” and calculate the true cost of looking and moving around a lot. However, realize that the excessive expenses associated with moving and searching are just a portion of the overall waste they cause.
The only way to make up for lost time is to generate extra capacity, which can be done by hiring more staff or machines or by working overtime. Time lost is cumulative. Furthermore, the cost of adding that capacity is something you wouldn’t have paid for if you had movement waste under control. This explains why the waste is so costly and concealed.
By connecting IoT-enabled business assets, doing advanced analytics or utilizing the real-time data they provide, and using manufacturing data governing services the resulting insights guide informed, economical, and efficient maintenance processes, predictive maintenance aims to prevent equipment failure and downtime.
While most people are aware of major equipment failures, not many consider the various possible instances of machine downtime. Machine downtime is, therefore, a significant inaccurate quoting or hidden expense. It destroys average throughput and labor efficiency and frequently leads to people chasing the wrong goals. A few of the factors that contribute are excessive material movement, changeover times, and search waste.
Analyze the causes of the machine’s downtime first when you see inefficiencies in operations. Numerous factors could be at play, such as material mobility, material availability, tool searches, unclear drawings, inadequate preventative maintenance, or unmeasured and unusual changeovers.
Pausing manufacturing during working hours lowers machine capacity, which results in missed deadlines and the expediting that follows. To make up for the lost capacity — capacity you never even knew you had — you needlessly add more machines and workers.
This cost is not so hidden; rather, it is a result of several other factors in addition to the hidden drivers covered above. Drivers can include expedites, rework, and scrap. Scheduling sequences are like gold to great companies because of the waste that occurs when there are disruptions. However, you have to eliminate the causes of the disturbances to achieve this. The optimal approach is to have quick cycle times. To do that, you must do away with hidden waste or overhead costs and work in a lean flow environment using single-point scheduling, small-lot processing, effective transfer methods, and work under work-in-progress control.
Another word for schedule alterations is chaos. The multiplicity of costs is enormous and widespread. The aim is to eliminate production meetings: everything is done according to plan, with a set timetable.
Hiring and training new employees nearly always comes with a hefty hidden cost. You incur larger costs than necessary when you have a high turnover rate. Because they know what it takes to replace an employee, great firms make it a top priority to track the true causes behind employee departures. People are frequently fired for being inefficient, but you need to identify the root of the problem. Is it the individual, or is it a result of some of the elements mentioned here?
Any store will have hidden expenses, but these are the most prevalent ones. They can all be resolved. When considered separately or collectively, these hidden expenses have a significant impact on a business’s ability to compete and make money.
Manufacturing analytics plays an important role in exposing the hidden costs for any business. Manufacturers can get insights into various operation segments, discover mistakes, and expose inaccurate quoting like quality improvement, supply chain management, operation efficiency, cost clarity, labor productivity, etc.
Using advanced managed analytics services in the manufacturing process is an ongoing process. To get the desired results, these analytics will need to be used again and over. Manufacturers must therefore see this as a continuous enterprise transformation that calls for mindsets and methods of operation to shift from the top down, involving managers, entrepreneurs, floor workers, process engineers, and senior leadership.
Manufacturers will be able to fully realize the potential of these new technologies and realize advances in productivity and profitability if they can organize their organizations to adopt this approach, which involves continuously applying and learning from advanced analytics, machine by machine, process by process, and plant by plant. For more details get in touch with us.
Originally Published on Medium
Data Engineering
AI & Machine Learning
By Use Cases
Business Intelligence & Tableau
