The three ingredients of a Short Interval Control (SIC) sandwich

The team at Commit Works have been implementing Short Interval Control (SIC) systems for over 20 years in mines, workshops, factories and even an insurance company.

The central idea behind SIC is that when supervisors are more AWARE of how their process is performing during the shift, then they will be able to ACT to keep the process on course to hit its target each shift.

This is a simple idea, right? All you do is get supervisors to check at regular intervals throughout their shift if they are on target and to act to improve the situation if they find they are off track. In reality, however, the success of SIC depends on multiple factors.

What’s in the Short Interval Control sandwich?

Whether it’s mining or another industry, there are three key ingredients that go into Short Interval Control – we call it the SIC sandwich.

• The top piece of bread should be an agreed and a realistic frontline plan for all work that the supervisor is responsible for.
• In the centre (the filling) is the tool supervisors or crew use to record (in short intervals) whether they are on track or not.
• On the bottom is the method for knowing how much ore, cubic metres, drill metres, work orders, widgets or insurance claims have been moved or completed at points throughout the shift.

Each of these elements makes the supervisor more AWARE of the performance of their process compared to the agreed plan for the shift. Given this awareness, the supervisor must then ACT appropriately to bring the process back into control and ideally describe what actions they took in a shift report.

The top of the SIC sandwich is the frontline planning and scheduling (or work management) system, which takes plans from systems like SAP, Deswik, Xact, MS Project, rosters, and leave and service schedules and makes them into a coordinated plan that can be committed to and executed on the shift. In most operations this is done via spreadsheets and whiteboards.

The centre (sandwich filling) has, for a long time, been A3 sheets of paper for supervisors to complete at two- or three-hourly intervals during a shift. In general, supervisors dislike these tools with a passion and seldom complete them properly or sustain them after consultants have left. More recently, some major mining firms have attempted to build software tools that supervisors can use in the field. These have been fraught with usability and connection issues, which have prevented most of them from being successful.

The bread on the bottom used to be provided through paper truck counts or radio calls but, more recently, has relied on fleet management systems (FMS) to give up-to-date information about the measurable raw tonnes, metres, cubic metres etc. coming off each machine. To be successful, the data needs to get from machines to the supervisor quickly. In a small opencast mine this can be achieved by the supervisor standing on the highwall observing operations; in a complex underground mine it could require a well-designed system of sensors, tags and communications infrastructure.

Why most Short Interval Control sandwiches fail

In our experience, most SIC sandwiches don’t work because of weakness in the top two layers.

Without a reasonable and agreed shift plan, the crew doesn’t have realistic targets to aim for, so there is no point breaking those targets up into smaller intervals to track against. “But”, you say, “we have the weekly plan (from Deswik, EPS or Xact etc.) which sets the targets.” Dividing a weekly production plan target into 14 even shifts is a convenient and easy shortcut to take but is destined for failure because it doesn’t take into account the variability in the workplace (conditions, maintenance, sick leave etc.) that the supervisor has to cope with.

Dividing the week up into shifts without taking all the other work and conditions into account means the supervisor and crew will never have a plan that actually makes sense on their shift – some shifts will have low targets and others will have unachievable targets, there will be services or sequence work that needs to be done and machines will need to be maintained, making the plan impossible.

Send a crew to work over and over again with a plan that doesn’t make sense and it’s likely they will lose respect for the plan (and their leaders) and choose to do things their own way.

Making SIC work

The holy grail of SIC is to have a single system that enables you to bring all planning information into an integrated shift plan that can be agreed at weekly and daily commitment meetings. This plan can then be:

• reviewed, adapted and committed to before the crew go to work
• used to assign work to people
• used to brief the crew at pre-starts/line-ups.

The same system can either print or deliver the plan to supervisors or crew on a phone or tablet at the face, and throughout the shift the work being done can be “closed off” in short intervals so that the control room, general foreman, shift boss, undermanager etc. and planners know that the right work is getting done.

This can integrate with fleet management systems to bring real-time data back to the supervisor through a tool, or regular radio calls can be made to check in on progress. At the end of shift, the supervisor and crew will have closed out most of the tasks and already written most of their shift report in the app, so a quick conversation around a touchscreen is enough to close out the shift.

All the data collected ends up in simple reports for use in daily review meetings to identify variances and plan corrective actions. This data is then available to business improvement people for analysis and continuous improvement work.

Commit Works has the only enterprise-quality system that makes this possible. It can be set up and implemented on your site in a matter of weeks and fits easily into operational expense budgets.

Global examples

Anglo Dawson OC, whiteboard daily planning meeting to set targets for the shift, paper based A3 SIC sheets, radio calls to each machine and supervisor at 3 hour intervals to say whether they were on plan or not.

Glencore Sudbury, UG Nickel mine planning development sequence work and tracking actuals from the face using an offline app.

Rio Kestrel, Fewzion work management planning system, crib room PC for entering actuals data, view of SCADA system and work orders from trades to tell how shift was progressing.

Anglo, Zibulo – Fewzion work management system, underground WiFi phones with a Fewzion SIC App to record actuals at the face.

Ways Smart Machine Lifecycle Digital Solutions Bolster Industrial Plant Flexibility

Across industries, the global pandemic has altered both consumer demand behaviors and the capabilities of manufacturers to deliver goods to the market. In order to adapt, many enterprises are rethinking the potential role of Mine Digitization and smart machines in helping them to redesign operations to be more agile. Manufacturing flexibility is key as it improves a firm’s ability to conduct faster manufacturing line setups and production changeovers to accommodate frequent and rapid market shifts. The challenge is to introduce these new capabilities without generating excessive costs and without investing in additional human resources.

To support this change, both the OEMs–who design, commission, and support the machines that serve as the backbone of digitized industrial operations– and the end users–who operate these machines– will need to reassess how they apply digitization technologies to a less predictable “new normal”.

How the Merge Model works in InfoWorks WS Pro v 4.0

How do you convert existing Networks and Controls to the Merge model of working? As described below: set the database option to “Use merge version control”, then choose a network and Duplicate it. The duplicated network now uses the merge model.

Version-controlled objects in InfoWorks WS Pro v 4.0 are:

• Network
• Control
• Live Data Configuration
• Alternative Demand
• Baseline
• Mine Digitization Template
• GeoExplorer

All of the above (except the Baseline object) are now available with both Lock and Merge version-control models. The version-control model you want to use is defined at a database level by going to File > Master database settings > Use merge version control.

Once the merge version control is selected, the software will automatically adopt the new Merge version-control model for any new version-controlled object created within that database.

All version-controlled objects defined in previous software releases using the Lock version-control model will continue to use the old model until the user decides to duplicate the object in order to use the new model. This can be done by right-clicking on the version-controlled object and selecting the Duplicate option.

A new set of icons have been introduced to distinguish between objects controlled by the old Lock model and the new Merge models.

The Merge model allows users to associate a default control and demand diagram to a specific network object. Opening the network in the GeoPlan (or population of the Run dialog) will automatically bring in the relevant control object.

Users can now start editing the network. There is no need to “check-out” a local copy. Multiple-users can now edit the same network object and work collaboratively on single models.

All changes are saved locally, with a red exclamation mark on the Model Explorer tree icon highlighting whether changes have been made to the model since it was last committed (saved). All changes applied by local users can be pushed back to the master database by committing the model (right click on the version-controlled object and Commit change to master database).
This action automatically creates a new child version of the version-controlled object, saving all changes that have been made to the parent object. This delivers considerable performance improvements to the software as it is now working with deltas rather than the entire model.

Changes made over time on the entire network are listed in the Commit History. It is easy to select the versions you want to compare and generate reports for the entire network.

Changes made over time to a specific object (pipe, valve, etc.) can be seen from the property sheet.

Multiple runs can be now associated with the same version-controlled object. The run dialog provides information regarding the specific version of the object in use for that specific run.

A new ‘Update to latest’ button tells users about the availability of the most recent version of that specific version-controlled object (possibly committed by a second user) that could be used for the simulation.

Checking the ‘Experimental’ run tick-box allows new runs to overwrite the existing set of results.

Note: all version-controlled objects must be committed before performing any run. The old run with a checked-out object is no longer available within the Merge model.

Having multiple people working on the same model could result in a conflict if the same parameter for the same object is edited with different values. In this case a Resolve Conflict dialog will provide information about the detected conflicts and users will be able to accept/disregard the latest changes when committing the object.

3 Benefits to Digitizing Your Mining Operations

Two of the most popular—and important—trends today are automation and digitization. They have merged to change how we live, work, play, and deliver. Additionally, they are helping accelerate the transition to the 4^th industrial revolution.

Today, technologies that enable digitization and automation are being implemented on a large-scale across industries to create new opportunities for companies of all sizes. Moreover, they are being combined with IoT to enable Industry 4.0 and revolutionize every aspect of the industry. According to a Mckinsey study, the majority of the heavy industry sectors are at the middle stages of digital maturity. This means that they are enabled either by a digital control system or advanced process control. However, to truly modernize their operations, these sectors will have to get to the highest stage of digital maturity, which is the AI-enabled Digital 4.0 revolution.

The good news is that, according to a 2018 survey by Gartner, digitalization is one of the top 10 priorities for businesses across several industries. The mining industry is one of them.   For most mines still using outdated technologies and experiencing the increasing need to push operations deeper underground, it is becoming harder to attain easily achievable savings in costs and productivity. 

At the same time, market forces require the mining industry to be more innovative in areas where automation and Mine Digitization are needed. Greater digitization and automation of mining operations will allow companies to overcome both challenges. To make this happen, mining companies will need to transition from digital 2.0 and digital 3.0 to the AI-enabled Digital 4.0.

What is the benefit of this? AI provides a wide range of benefits to mining companies that helps them deal with the challenges the industry faces. For example, AI can help mining companies to identify and extract resources. Not only can AI collect and analyze data from multiple sources for this, but it can also identify areas to explore using machine learning (ML) algorithms. It is also useful in water and wastewater treatment.

Additionally, with end-to- end visibility enabled by AI technology, mines can identify and maximize efficiencies throughout their operations to reduce expenditures and increase profitability.  If you’re still on the fence about digitizing your mining operations, then you may want to consider the following 3 benefits of doing so.

1. Creating Smart Mines to Minimize Operational Costs

One of the biggest advantages of digitizing your mining operations is creating ‘smart mines’ that allow you to reduce your operational costs. Smart mines are mines where the major assets are digitized via embedded sensors that transmit data to a centralized location through a wireless network. This allows operators of mines to save money not just on labor—since human workers are no longer needed to perform data readings on site manually —but also on cable maintenance because the need for expensive cabling is eliminated.

However, the biggest benefit comes from the use of AI as it allows you to optimize the major assets that are connected to a centralized location through wireless network and embedded sensors. AI allows you to link your daily operations to the objectives of your company and goals specific to the mining site that you can easily monitor and predict.

Another major benefit of AI’s use in mining operations is minimizing the ecological impact of mining operations. Incorporating AI technology into pre-existing mining operations allows the sector to minimize its energy requirements and lower the environmental footprint. Additionally, ML can predict peaks in energy to allow companies to take appropriate precautions.

Lastly, AI can track environmental parameters such as temperature and groundwater to help assess the impact of your mining operations.

2. Efficiently Monitoring Operational Assets

Predictive maintenance and wireless monitoring, facilitated by operational intelligence (OI) solutions, enables companies operating mines to track operating assets, such as dams, remotely and continuously to ensure that they are not damaged.

The use of technology that allows capturing data digitally and in real-time is slowly but surely replacing the costly readings of key operational data that take place manually. An example of this key operational data is Pore Water Pressure, the amount of which can have a major effect on the resources within a very short timeframe. If the pressure of pore water unexpectedly increases, a dam can split very quickly. This is where intelligent and automated water management in mining can help.

Operators can constantly monitor the pressure levels of pore water using digital real-time readings. This increases the ability of the operators to incorporate measures to lower the pore water pressure levels, and in turn protect their major assets, and their workers from any immediate threat.

Critical decision related to mining operations no longer need to be made by operators themselves after a careful analysis of complex parameters. Instead, they can use AI for this purpose. Whether you need to determine the right time for turning pumps on or off, come up with the right amount of chemical dosages, or find out the frequency with which assets need to be maintained, AI can provide you with intelligent recommendations driven by machine learning.

3. Effective Water Management in Mining

The primary use of water in mining is to transport slurry, suppress dust, process minerals, and meet the needs of onsite workers. This need for water is met in mining operations through commercial water service providers, groundwater, lakes, streams, and lakes and rivers.

However, mine sites are often situated in locations where water is already limited and where local people and officials, unsurprisingly, try to stop mines using water from these sources. In addition to the procurement of water, extracting water sustainably is also a problem for mines.

The good news is that digitization of the mining industry is helping to overcome this problem. For instance, emerging mining treatment technologies are allowing operators to recirculate process water to minimize water intake, tailing storage, and effluent discharge volumes.

By enabling intelligent water and wastewater operations, AI can save up to 30% on operational expenditures (OPEX). It can do this by minimizing energy costs, optimizing the usage of chemical for treatment, and ensuring the efficient management of assets.

At Innovyze, we make water management in mining a breeze through AI-enabled technologies that not only facilitate water conservation in mining but also enhance mining water management performance to ensure sustainable mining operations. Get in touch with us today to know more about how we can help you.

Readmore Digitization, Urbanization & Industrialization