Creating manufacturing operations of the future
Information is rarely shared quickly enough for managers to respond in the tight time frames required and is most often paper-based with a large amount of manual work digitizing and analyzing the recordings.
Manufacturing Operations of the Future
Many companies do their best to optimize production processes using established rules of thumb or incomplete data. But at the end of the month or reporting period, they often discover sizable gaps in their data recording. In our experience, that is because they typically lack reliable-enough measures to understand the small, real-time variations in process flows and manufacturing steps.
Our work across a number of industries suggests that companies can speed up reaction times, and reduce administrative work at the worker‘s and supervisor‘s level. Leveraging available data is possibly by simply using the right tools / devices with an intuitive interface and analytics. That also supports an we described as a way to improved resource productivity. It also provides a much more exact view of fluctuations in the operating environment and a much better means of communicating the implications to top managers.
Extending the measurement frontier
By combining measures of value, cost, and volume over time, profit per hour is more potent than the sort of metrics commonly used in many industries. Using data captured from sensors, along with advanced-analytics tools, industrial companies can deploy self-learning models that simulate the expected value and cost of individual processes and even entire factories on a continuous basis. From this analysis, patterns emerge on where costs, heat levels, recovery levels, and other variables are deviating from predicted values. We call this the digital twin with whom the operators can then fine-tune process procedures or adjust inputs so as to eliminate losses as much as possible during those periods in the day when profitability falls below optimum levels. The insights create a new information backbone, linking real-time performance at ground level to company profitability and allowing managers time to make the necessary trade-offs.
Until recently, companies lacked the usable data, advanced sensors, and processing capabilities to gauge the performance of operations with real-time precision. But increases in lower-cost sensors, wireless connectivity, cloud data storage, and computing power have changed the equation, as has the development of smarter analytics tools that analyze continuous process flows and complement advanced-process-control systems. Moreover, as more efficient and effective analytics emerge, there is greater scope to widen profit-per-hour analysis beyond just a few of the most critical processes. Meanwhile, further reduction in the cost of storing vast quantities of data allows finer-tuned performance management to reach across entire plants and even across companies.
Reaping the benefits
Two examples demonstrate how profit per hour can result in significant performance gains.
Process-level improvements at a plastics processing plant for flexible film:
The installed systems already provide a significant level of automation and advanced process control. However, the level of complexity operating the system was affecting the efficiency of the process and the performance of the plant: the problem was they didn’t know to what extent. Technicians therefore identified a list of ambient and internal conditions that tended to play a major role. Armed with the necessary data, they built an analytics model that was able to simulate profit per hour for the line under ideal conditions - enabling management to note disturbances and take remedial action. The model further allowed the team to identify precisely the lost output and margin effect resulting from any variations. The team then focused on the top five that could be controlled by process adjustments or targeted investments. The company ultimately discovered that closing gaps in the general flow of information and administrative work connected with each work order could yield nearly €250,000 in value annually, in an investment that paid for itself within 12 months. The model also indicated how speedy reaction to operating deviations boosted profit per hour, informal messages to supervisors and front-line operators charged with monitoring dashboards and adjusting processes in real time. After a pilot installation was proofed successful the digital solution was installed across the compete plant with the goal of increasing profits per hour by up to 2 percent.
Facility-wide gains in manufacturing operations:
A metal processing factory seemed to be operating in the dark. Capital upgrades only intermittently resulted in higher returns. Operational decisions were often based on historical wisdom, manual work and personal experience, with little in the way of facts to demonstrate their potential financial impact. Meanwhile, data gathering was substandard, and a set of non standardized key performance indicators (KPIs) were used, preventing an integrated view of performance across the whole plant. Senior management decided to remedy the situation with a radically different, multi-step approach. At the core was a new KPI, which cascaded to the entire executive suite and linked operations performance to a single plant-wide daily profit standard, grounded in profit-per-hour analytics. The goal was to give plant-level managers and front-line operators greater visibility into production variability, as well as to offer financial executives a surer sense of the facility’s performance. During the first phase, the mix of operational metrics was aligned with the new profit measure. In phase two, technicians tested the metric for insights into operating performance across the site’s milling and turning operations, and other units. In a third phase that involved new investments in a software based transparency solution: the company installed tablet at the operator stations for intuitive data entry, machine gateways for recording operating data, as well as dashboard monitors that displayed works status and performance both on the plant floor and in senior-management offices. A distributed data-storage system and standardized reporting and data analytics suite allowed reviewing historical data and comparing set/actual values. Extended metrics have allowed full tracking of costs. With additional training of front-line employees and managers alike, it has accelerated problem solving on a real-time basis. Variations in efficiency, previously likely to continue for days, are now eliminated within hours on average thanks to new ways of working across the facility. Costs have fallen by 8 percent in the two years since the new profit standard was adopted, and, coupled with other improvement initiatives, it has resulted in close to an $80 million cumulative increase in earnings. Additional gains are expected as better data analytics open pathways to new process improvements and work flows.
Exploring new horizons
While still in its early days, we’re seeing instances where profit per hour can be applied across multiple company manufacturing sites and even more broadly to supply-chain networks and decisions about how to serve customers. A more accurate, real-time view can help companies understand—among a growing list of possibilities—how to optimize the supply routes to a given finished product, how to most profitably serve customers when several production sites exist, how many products to manufacture from a single production site, and the best combination of make-versus-buy options. Such end-to-end systems could provide companies with unparalleled “postmortem” analysis of where value is leaking across their operations, as well as new ways to simulate the forward impact of strategic decisions.
With the growing capture of unstructured data on human interactions from video and social media, profit-per-hour metrics could soon be applied in nonindustrial settings, such as retail operations. As the quality of IT and analytic skills improves across sectors, and as managers learn to accelerate front-line adoption, productivity levels are likely to increase in a wide range of economic activities.
Digitizing the shop-floor, not just product lines, brings business benefits as it:
Provides people with all the information they need to quickly make their own decisions to complete their daily work, on-time at quality
Reduces the need for active supervision to solve basic issues. In addition, reducing manual reporting time would probably give supervisors back a day a week to coach their people and improve their processes
Avoids the use of paper and workarounds by incorporating the right capability into existing systems, leveraging tailored apps in industrial strength solutions
Embeds your Lean system in your operational systems to create a seamless link for the training, methods and tools to improve performance to manage deviations
Connects cross functional teams through integrated performance management systems, operational plans, shared priorities and actions/escalations
Provides real-time, high-quality data allowing early warning and prediction of issues, making it possible to devise proactive responses rather than firefighting immediate problems.
All digital solutions should be designed to empower the people who actually do the work in the quickest and best way possible, not simply give their supervisors more control over them. Effective digitizing strategies use business intelligence to simplify the information flow to the individual. It says: What questions do you need answered?” The system will give you the answers, so you don’t need training. In a wider sense, the aim is to change the way of working, to accelerate performance and generate greater autonomy. Digital itself won’t do that, but it can be a real catalyst for it. Re-positioning digitization around change management is therefore critical to producing real business value.