Measuring baselines, benchmarks and scale

Introduction

The previous section identified some tools and best management practices (BMPs) linked to Input variables. The ability to separate variables from inputs helps to isolate complex noise in manufacturing processes. This section looks at measurement on the process floor, for product assurance, for maintenance and for robots. Robots can be a solution to labour and some product assurance issues. However, robots also create an added level of maintenance.

The processing floor: labour, skills and training

Measuring the actual time it takes to produce your goods is a valuable source of information. Part of that measurement is the equipment vendor specification (gathered in a digital mapping exercise) compared to an actual processing time study known as a takt time study. This is where the variance between design and actual performance is measured. This also measures labour costs. It is often the case that line speeds and equipment performance change over time. Sometimes this is a legacy issue. For example, perhaps a machine was repaired to keep things running and is missing the correct part which impacts performance. Sometimes the speed of a single machine has been slowed down or sped up creating a bottleneck up or down the process line. Correcting these issues will change energy, ingredient, labour and even product quality performance which, in turn, impacts production costs. Identifying and reducing this type of variance may reposition workers. Where production labour is hard to find, this exercise can yield experienced hands to redeploy. A takt time study can help increase line performance by up to 30%.

Takt time

A starting point for a takt time study is to use McMaster’s School of Engineering Practice and Technology. Their program includes a digital mapping exercise. Masters and PhD level engineers work with a team of undergraduate engineers. The students’ goal is to gain experience to work in advanced manufacturing. Students begin with the analysis of equipment specification manuals and measure utility inputs. The cost is nominal. The digital map they produce becomes your processing line baseline. Your next step is a takt time study.

Where equipment and line speeds are out of balance, behavior management is suggested. Work with production and equipment operator staff to recognize and practice performance optimization. Managers who actively support skills training and provide positive feedback to their staff on the use of those skills will motivate their staff. A positive reinforcement communication style helps to further reinforce desired behaviour.

The monitoring equipment used for leak and loss control also provides real data for costing process inputs. Adding the use of bar codes and the ergonomic re-alignment of a process floor reduces human touchpoints and labour. These kinds of projects change the cost of production and should be reflected in product costing models as they occur.

A best practice for manufacturers is to undertake an annual product costing drill to inform finance, sales and marketing staff of the expected cost of production. Sometimes a company will base product costing models on the commissioning exercise they did when a line was first set up. Variance (noise) is dynamic and only increases over time where the skills and tools to manage it are overlooked. This BMP is also referred to as activity-based costing.

Lastly, consider joining the Excellence in Manufacturing Consortium (EMC). The organization specializes in existing workforce training based on lean. Members are encouraged to share skills, to network and mentor members from different companies who need some help with troubleshooting production and procurement issues.

Quality and product assurance

Quality assurance measures ingredients and finished products. This is another set of baseline data that supports traceability. Two lessons learned in advanced manufacturing include:

• the use of bar codes and hand-held scanners, from receiving through the process floor to inventory and shipping, to take the data entry touchpoints out of record-keeping. Anyone who has spent a frantic week or two manually tracing a recall will appreciate the instant access and granularity of detail that is possible with a bar code system.
• the use of optical sensors at key quality-monitoring points in a process line can be used to identify the time-related volume of defects (based upon colour, shape, heat and more). Some manufacturers, for example, use optical systems to self-correct baking and frying line temperature to manage colour-related defects and improve energy management. Optical systems with time-sensitive capacity generate data useful for the analysis of crewing requirements for sorting and grading, as well as provide data to build a business case for sorting robots and/or casing robots. As with palletizing, process line sorting and casing are repetitive tasks linked to repetitive strain injuries (RSIs).

Sensors, metering and bar-code data require data scrubbing and management skill sets. A data management technician using a digital integration platform can combine data from multiple sources without the need for manual data entry. However, that data will still need to be analyzed and interpreted by a cross-functional team (production, quality assurance and maintenance), then coordinated with procurement, production planning, finance and product costing models.

Maintenance

There are two maintenance strategies. One strategy is to have maintenance on hand to react to critical failures. Another strategy is to ensure maintenance has the skills and tools to practice predictive maintenance, or to fix things before they cause unplanned downtime. The first strategy is effect-based behavior and the second is cause-based behavior. Ideally, an effective maintenance team will be involved in decisions regarding input and process optimization. The skills required for monitoring things such as energy data are key to the predictive maintenance of processing equipment, as measured energy use goes up so does the probability of leaks, losses and equipment failures.

Predictive maintenance schedules for key equipment are another layer of risk protection. Where power quality conditioning equipment reduces the wear and tear on motors, an Energy Management Information System (EMIS) system will track the operating condition of that equipment. As motors degrade, their power consumption increases. The ability to predict an imminent failure provides the time to schedule a repair before a critical failure.

The intent of activities in this step is to reduce the spread between budgeted and actual costs. Create reliable production targets and ensure products can be traced in the event of a recall. These activities also support lean cost-reduction exercises.

Robots

Robots are top of mind in automated manufacturing. Their adoption in the agri-food sectors lags automotive and blow molding sectors. One of the barriers may be that power quality upgrades must precede their adoption.

Since the 1990s, robotics in food manufacturing has meant palletizing and box-making robots. The benefit is to eliminate jobs that cause RSIs. This strategy has two key performance indicator (KPI) impacts, which are:

• reducing Workplace Safety and Insurance Board (WSIB) rates related to RSI
• redeployed workers being an immediate solution to labour shortages

Processing line cobots (slower speed robots that can be interspersed with workers) and warehouse automation are the next likely applications to be adopted. Their capital cost versus benefit varies with facility scale. Some vendor robotics resources include:

• Cobot Palletizer
• Cartesian Palletizer
• Insulfloor Double Pallet Palletizer 45
• Robotiq 7^th Axis Palletizer 11

Table 3 lists BMPs and foundational technologies that are linked to process optimization.

There are several online government resources in Canada and the USA related to energy management, process efficiency and robotics, including:

• Natural Resources Canada (NRCan) Canadian Industry Partnership for Energy Conservation (CIPEC). This site lists cost-share grants, events, tools and technical information including case studies.
• National Institute of Standards and Technology, US Department of Commerce. This site provides case studies and background information on process improvement, robotics, systems integration, monitoring diagnostics and prognostics, process measurement and control.
• Better Plants, US Department of Energy

NRCan’s RETScreen is an advanced clean energy management platform for low carbon planning, implementation, monitoring and reporting. After the leaks and losses in a facility are corrected, RETScreen is a useful planning tool for new equipment purchases and process modelling. It is also the North American gold standard for planning circular and green energy projects.

The US Department of Energy also has an energy/equipment modelling tool for advanced manufacturing called MEASUR. This tool was developed to aid manufacturers in improving their energy efficiency and equipment in manufacturing plants. The American tool is free and user-friendly.