Lean Data Computing

Ecological

When we take about waste we think of the plastic soup , or materials are not optimal reused leaving us with rubbish. Yet the signs were already given long time ago about plastic pollution and even in the deepest points of the ocean plastic is found.

Waste in Lean Data Computing

In computing we also collect waste. Datacenters of the World will consume 1/5 of earths power by 2025, we will refer to this as data soup.

In organizations we also have this in data processes that are responsible for 8 sorts of waste that bring costs to be removed.

Defects for Data

One of the most easily recognizable wastes in lean data manufacturing is the production of Defects.
Examples of Defects in data manufacturing include waste such as code snippets , automation systems that require rework, or infrastructures that are missing details of software and hardware positioning.
P@ssport gives through creating engineering spaces for the processes the possibility to test ” what if ” scenarios. By adding simulation to the processes a company can also use it to train workforces and simulate what-if scenarios. to avoid non utilized talent waste by training people and transfer knowledge on processes and production of the current operations.

  1. Defects are often considered to be one of the most significant manufacturing wastes because they can actually lead to the generation of additional wastes such as Overproduction, Transportation, and Excess Processing.
    P@ssport mitigates defects by using preventive maintenance modules and a secure by design network analytics will be done on your premisse so early warnings a machine will go defect can be dealt with before hampering production .
    When services do not conform to a customer’s requirements on quality than the service needs to be refurbished or redesigned. Also features that are built in can be part of the package but are not asked for by the customer .
    These extras can have impact on performance, security and availability, and even on the environment.

Excess Processing of data

By adding a scalable Triple D platform you are becoming the sole provider of inhouse dataservices that can be offloaded to cloud environments , vendor independent and with your own encryption keys instead of keys that are handed out to you and you don’t own. Why is ownership important?
P@ssport has been an advocate for Absolute Enforcement where Control Networks can use data for their daily operations – real time and share data with the corporate domain for historical data without being compromised due to weak protection measurements these environments have by nature.
By using more than needed your company will be producing more than needed. From holding more data lakes and systems than needed or more subscriptions to cloud services than needed or more process being handled by different means and producing the same data in the end ( historical, near time, real time) is waste.

Overproduction

P@ssport gives your company the possibility to design and implement services all connected through the same trustworthy resources effectively paying for what you need , not what others think you need. The current Infrastructures are now being build out without clear measurable targets on performance and functionality. And don’t have room for greenfield technologies like sensors and actuators since the standards never envisioned them to be connection

Waiting

As we all know more devices get connected and await instructions that data that cannot be processed can give you dip in your analytics not even accounting for the collecting of that data in difficult to reach locations.
Waiting is a process where due to inactivity data will only be transported and not processed .This will give extra costs before the data can actually become enriched through components further up the value chain.

Not being able to unlock that data will give a setback since overhead costs like more tear and wear, more energy consumption cannot be managed from an analytical point of view. Waiting to unlock that data strips potential revenue away and will lead to (non) predictable cost in maintenance and operation.

P@ssport mitigation measure to waiting

Queuing datastreams that will happen once all these connected devices are storming up demanding connectivity and bandwidth too will cut on our reaction speed. Since without the proper bandwidth we need to keep into account we are always reactive , and we cant guarantee signals or alarms to get to the event broker.

Place the collectors, beads, transporters and computing resources as close as possible to your own company Network to the edge if possible. This will eliminate the latency one experiences sending data into the cloud and will take away complexity if that data needs to be processed outside that silo.

Inventory

Inventory is a manufacturing waste because it is value that is being held at a cost. In the most literal sense, Inventory is valuable that is waiting either to be sold to the customer or further transformed into something of greater value. The entire time a data sits in Inventory, its profit margin is reduced because overhead must be paid to maintain the data in Inventory. Maintaining Inventory requires the addition of Motion and Transportation wastes. P@ssport Triple D doesn’t have additional fee for virtualization, high availability ,storage or routers and switches with their licensing fees and operating systems found in blades and external storage. Also you will use less electricity power which will help you become more sustainable.

Transportation -Moving data from on-premisse and to the Cloud and back

Moving data costs money, which is why Transportation is classified as a waste category. Unless value-added data transformation is performed to the data during transport, the Transportation of a data to a silo and is also valuable elsewhere in the organization is a wasteful activity. Great amounts of resources and time are consumed moving data from one silo to another while no value is being added to sell to the customer. Data collected on premisse , and transported, collected on premisse resources don’t need to be transferred to one cloud environment and later transported back to the company to receive the same data which in the end is not yours anymore.

Transportation leads to increased Motion lean waste because resources are required to move without generating value.
P@ssport makes data integration flexible, scalable and secure by design. By enabling the last mile secure data collection possible real time data can be enriched on premisse for investigation, tuning and of course optimalisation. Like a walk in the park, simple but a secure last mile.

Motion

When Motion occurs, value is not added to a product or material being manufactured. Motion happens when data is collected on the spot like regular maintenance on pumps and valves.

The employee’s efforts are not only being wasted on physical measurements like acces cards and admitment procedures but Motion can also result in physical injury to employees ( traffic accidents, on premisse perils) which results in even greater cost to the business. Motion waste is closely related to wasted employee potential, commonly referred to as Non-Utilized Talent. Motion here means maintenance people driving to check upon assets that could give their status also by electronic means so human resources their effort and time are being wasted. Inefficiënt network layouts, and improper hard to reach equipment can contribute to unnecessary motion.

By using our Triple A jumping host remote access ( used on 27 remote oil platforms in the North sea since 2012 ) can be given granular to network switches – and dropped when someone tries to get more privileges. Also you can use role based access on devices that are not in your active directory domain and in the mean time record all actions taken – for auditing and 4-eyes principles. Even with infected systemintegrators devices they will reach your devices without bringing their mayhem on your network- secure by design.

Non-Utilized Talent

The only lean data manufacturing waste that is not data-proces specific, but rather data management related, is Non-Utilized Talent. This type of manufacturing waste occurs when data management in a manufacturing environment fails to ensure that all efforts to collect, transform and produce data to become rich data that can be used for smart analytics are being utilized.

Non-Utilized Talent also refers to management’s ability to utilize the experience of elder employees and add these in simulated environments .This will add to continuous improvement feedback from employees to improve a lean computing manufacturing process. If management does not engage with manufacturing employees on topics of continuous improvement and knowledge preservation and allow employees to influence change for the better, it is considered manufacturing waste.

If you are building out a traditional network the cost will be in your maintenance and maintain the skillsets needed for this maintenance .

In relationship to Motion waste, if a department is aimlessly moving data around production and distribution area without adding value their efforts are being wasted where they could be performing value-added activities instead.

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