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Date posted: October 14, 2015

As a company grows, so too does the challenge of its fixed asset management.

A recent column on Wasp Buzz points to five best practices for fixed asset management that prove useful in protecting asset value and minimizing the stress on administrative staff responsible for this function.

  1. Always use the best tools for the job.
    Take a critical look at the software being used: legacy systems may be counterproductive. Using the best tools will save time and money. The software needs to be scalable as well as reliable. Your asset management system must have the ability to grow with your company; otherwise, you may find yourself in the awkward and delicate condition of having to move between asset tracking systems— at considerable cost.
  2. Ensure accurate depreciation tracking.
    Without accurate tracking of asset depreciation, companies pay too much for both taxes and insurance. Ideally, asset management software should ensure accurate calculation of depreciation as long as purchasing information is accurate. A further concern here— failing to depreciate assets appropriately could lead to violations in regulatory compliance. (This especially so for companies that work with government funds or grants.)
  3. Start your tracking out right.
    Establish a solid and accurate baseline. If you begin with inaccurate numbers, your numbers will always be inaccurate. Never trust an old system. Go back to the physical inventory of your items to ensure they have all been cataloged and that they have been cataloged correctly. Part of this involves removing “ghost assets,” assets that still exist on the books but are no longer company property. These may emerge if an asset has been broken, stolen, or even sold but not properly recorded in your books due to improper tracking, system shortcomings, or less than diligent procedures.
  4. Streamline hardware and software.
    When managing fixed assets, both the correct hardware and software are needed, and the two need to be well integrated. They should work well in tandem without any compatibility issues.
  1. Customize your fixed asset reporting.
    Too often, companies rely on generic, boilerplate reports for fixed asset reporting. A schedule of fixed assets should be tailored both to a company and its industry; otherwise, it may prove difficult to glean relevant and important information for effective fixed asset management.

Remember, if fixed assets comprise the bulk of an organization’s capital base, they must capitalize on them to drive competitive advantage. This should help the focus on fixed asset management best practices.

 

Date posted: October 12, 2015

The global reliability and maintenance management consultant IDCON has posted in their resource library a series of seven questions to help in checking best practices for preventive maintenance. They’re worth reviewing here.

  1. Do you have a definition for preventive maintenance (PM)?
    Ask people in maintenance and operations to define what is included in preventive maintenance. Those companies employing best practices will have a definition of preventive maintenance that is documented, understood, and well communicated across the plant.
  2. Do you know how satisfactory PM is done today?
    Ask the plant manager, maintenance manager, and operations manager for the PM improvement plan. If there is one, is it specific with timelines? When best practices are employed, plant management is aware of strengths and weaknesses of the PM program. The plant therefore has specific plans and timelines in place for improvement actions.
  3. Do you have an alignment standard, and is it followed?
    Ask for an alignment standard and check the quality of the standard. Go look at equipment for signs of good or poor alignment. When best practices are followed, there is a well-documented alignment standard; more importantly, the standard is followed. In a world-class reliability and maintenance organization, all alignments are done to 0.002 in. (0.05 mm) for equipment running below 3600 rpm and 0.001 in. (0.025 mm) for equipment running above 3600 rpm. There is a well-defined alignment standard explaining how to set up, clean, check for pipe strain, check for soft foot, and so on.
  4. Do you have a lubrication standard, and is it followed?
    The standard should include storage, handling, filtering, and cleanliness of lubricants. Storage and handling areas should be visually checked for cleanliness. When best practices are employed, there is a well-documented lubrication standard— one that is followed. The cleanliness standard for each piece of equipment should match the clearances in the equipment’s lubricated surfaces. Further, in order to reach the right cleanliness levels of lubricants, oil and grease have to be stored, handled, and filtered correctly.
  5. Are inspections (condition monitoring) done where it is cost-effective to do so?
    Go through inspection lists, check for the level of detail, and make sure the route is actually completed. When best practices are employed, there are inspection routes for all mechanical, electrical, and instrumentation equipment (where it is cost effective to have inspections). In a top-notch plant, inspections are documented and completed according to schedule. The plant is using an inspection list or a handheld computer. The list or handheld computer describes exactly what to do for each inspection. The inspections are a combination of measuring condition and subjective (i.e., look, listen, feel, smell) inspections.
  6. Is detailed cleaning of equipment done well?
    Take a walk in your plant and visually check the cleanliness and condition of the equipment.  If best practices are employed, detailed cleaning of equipment is done consistently. Dirty areas are redesigned in order to protect equipment from contamination. Detailed cleaning can be checked easily. For example, a clean hydraulic unit can be inspected for leaks in about 10 seconds by taking a quick look at the pan underneath the hydraulic unit. A dirty hydraulic unit would take 20-30 minutes to check for leaks.
  7. Is an ultrasonic or vibration monitor used when greasing bearings?
    Check lubricators’ equipment. If best practices are used, vibration or ultrasonic levels are checked while greasing in order to apply the correct amount of grease.

Date posted: October 8, 2015

One of the more interesting businesses to emerge in recent times is Uber. It seems you see them everywhere these days.

Uber Technologies Inc. is an American international transportation network company headquartered in San Francisco, California. The company develops, markets and operates the Uber mobile app, which allows consumers with smartphones to submit a trip request, which is then routed to Uber drivers who use their own cars. By May 28, 2015, the service was available in 58 countries and 300 cities worldwide. Since Uber’s launch, several other companies have copied its business model, a trend that has come to be referred to as “Uberification”

Uber is part of a larger trend where established business models are being turned on their heads. Think Bitcoin. Think open online education.

In a fascinating article in the McKinsey Quarterly, Marc de Jong and Menno van Dijk explore how established businesses “can disrupt traditional ways of doing business by reframing the constraining beliefs that underlie the prevailing modes of value creation.” They point to a five-step process of business-model innovation that incumbent market players can employ to leverage the power of disruptive models:

  1. Outline the dominant business model in your industry.
    Define the long-held core beliefs about how to create value.
  2. Dissect the most important long-held belief into its supporting notions.
    What underpins the most important core belief— notions about customer interaction, technology performance or ways of operating, for example.
  3. Turn an underlying belief on its head.
    Formulate a radical new hypothesis, one that no one in your industry currently wants to believe.
  4. Sanity-test the hypothesis.
    Many reframed beliefs will not make sense. Applying a proven reframe from another industry may succeed. Unlike product and service innovations, business-model innovations travel well from industry-to-industry.
  5. Translate the reframed belief into your industry’s new business model.
    Once you arrive at a sensible reframe, the new mechanism for creating value pretty much suggests itself; just take the reframed belief to its logical implications.

The authors point to four areas where reframing beliefs may prove valuable— customer relationships, activities, resources and costs. In each of these areas there is positive transitional movement that innovation can drive: in customer relationships, from loyalty to empowerment; in activities, from efficient to intelligent; in resources, from ownership to access; and in costs, from low cost to no cost.

A Nobel laureate once said, “Progress is impossible without change, and those who cannot change their minds cannot change anything.” This is something business leaders should consider to keep their business models from becoming idées fixes that may end up as millstones around their necks.

The rapid emergence of new business models should also have their attention.

 

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Date posted: October 6, 2015

The explosion of unstructured data is rapidly changing the data landscape. A recent white paper by Impetus describes unstructured data and the opportunities it presents:

Broadly defined, ‘unstructured data’ refers to information that either does not have a pre-defined data model or is not organized in a pre-defined manner. The vast majority of information captured from nontraditional sources contributes towards unstructured data— and this data has immense potential. That said, it is also difficult to interpret, and its processing is a laborious, time-consuming task. Nearly all data existing beyond the realms of a database is unstructured, unrefined and largely indeterminate. What is equally worrying is the sheer volume of unstructured data: At least 80 percent of all digital material operable in our daily lives. Mining this data for insights can give your company a huge competitive advantage.

 Data Lakes are the means to manage this avalanche of data. A data lake is an enterprise-wide data management platform for analyzing disparate sources of data in their native format. What makes them unique is their ability to unify and connect. Again, from the white paper:

It helps you access your entire body of data at the same time, unleashing the true power of big data — a correlated and collaborative output of superior insights and analysis. It presents you with a dynamic scenario where one can dictate a variety of need-based analysis made possible by this unstructured repository.

While enterprise data warehouses (EDW) have traditionally been the foundation for business intelligence and data discovery, they increasingly fall short in the new data landscape, where unique insights are only possible when enterprise data isn’t limited by structures.

Impetus describes the four major capability pillars of Data Lakes as follows:

  • The Active Archive
    An active archive provides a unified data storage system to store all your data, in any format, at any volume, indefinitely. It helps you to address compliance requirements and deliver data on demand to satisfy internal and external regulatory demands.
  • Self-Service Exploratory Business Intelligence
    Often enterprises need to access a plethora of data for reporting, exploration, and analysis. An enterprise Data Lake allows users to explore data with full security by using traditional interactive business intelligence (BI) tools via SQL and keyword search.
  • Advanced Analytics
    A fundamental element for effective analysis is the capacity to search or analyze data on a large scale and at a microscopic or granular level. It helps to keep your company’s vision and understanding of core functionalities at an optimum. Data lakes can deliver this level of fidelity.
  • Workload Optimization and Transition Management
    Most companies run their Extract, Transform and Load (ETL) workloads on expensive systems. These can now be migrated to the enterprise Data Lake, therefore harnessing costs, driving efficiency, and ensuring timely delivery.

For a detailed technical discussion of what’s involved in implementing an enterprise Data Lake, the Impetus white paper is an excellent resource to “dive into the lake.” Again, you can get it here.