The AGILE Approach to Manufacturing Analytics

AGILE methodology was originally designed with an intent to improve software development processes and deliver additional value to the customers through working software but the “Agile Principles” can be applied to many other processes beyond software development.  One of the reasons for this approach was the realization that linear, phased and gated approaches were not working. The earlier approaches did not consider that the assumptions and environmental conditions at the start of a project were very likely to change as the project progressed and the situation at the end could be drastically different. In today’s dynamic business environment, conditions change rapidly…hence it is important to continuously monitor business and customer priorities and act accordingly in an agile manner.

Isn’t that similar to what we expect from a manufacturing analytics solution? It should help us deliver business value according to current priorities by monitoring the manufacturing environment. The solution is expected to help organizations take right decisions at the right time by responding in an “agile” way.

The Agile approach focuses on delivery of high business value using sound practices. It emphasizes tight alignment and collaboration between business and technology. One of the key deviations from traditional approaches is that Agile embraces changes in requirements and business priorities. Focus is on delivering value in iterative short time-boxed development cycles. Agile values individuals and interactions over processes and tools. Finally, Agile emphasizes continuous improvement with every iteration through frequent planning.

When deploying a manufacturing analytics solution, an Agile approach, as summarized above, proves extremely useful. A manufacturing analytics strategy which is responsive and flexible is more likely to succeed than one which is fixed and not open to changes. A Big Bang approach is likely to get a lot of attention but it is even more likely to take a longer time to succeed, if at all. Such a traditional approach typically assumes that we have answers to all the questions when embarking on the analytics journey and we will see successes when we are done with the implementation 12-18 months or more down the line. Due to nature of the planning and implementation, a lot of upfront investment is required in such strategies. Upfront purchase of an IOT platform plus additional customization projects for many months may be required prior to deployment.

Adopting Agile principles of iterative, time-boxed, progressive and continuous improvement using sound practices and tight collaboration gives us better visibility and responsiveness to business realities. What does it mean when applying these principles to a manufacturing analytics implementation on the shop floor?

Let us look at the key elements in the Agile manifesto and see how they relate to manufacturing in general and specifically, manufacturing analytics.

  • Individuals and Interactions over processes and tools

The key element of Agile is team collaboration, empowerment and employee engagement. Bringing visibility and transparency between management and equipment operators through common dashboards, automatically and accurately tracked devices and empowering the shop-floor personnel is one of the foundations of an agile manufacturing analytics deployment

  • Working machines over manual reporting

The primary job of a machine operator is to keep the machines running in top condition and churning out good quality parts at top speeds. The job of management is to empower them to do this, remove any obstacles in their paths, provide them the right tools to take the right decisions and actions at the right time.

  • Customer collaboration over contract negotiation

The visibility and transparency culture brought in by a correct deployment of an automated machine monitoring and manufacturing analytics solution enables an organization to adopt the same open approach with customers, focusing on addressing customer requirements and problems in a collaborative manner than focusing on contract negotiation. Any design changes needed from the customer to improve performance and quality should be communicated upfront and solutions agreed on through proactive collaboration. Part of the benefits gained through manufacturing analytics application in customer projects can be transferred to customers leading to customer delight.

  • Responding to change over following a plan

This is the one of the outcomes of employee engagement and empowerment. Once employees are provided with the right tools to take right decisions at the right times and management & shop-floor personnel are working towards the common goals of improving availability, performance and quality, organizations can respond to change faster and become more agile. Each actively engaged employee focuses on continuous improvement and looks out for such opportunities rather than only following a daily pre-defined routine.

When figuring out the right way to approach agile in a manufacturing environment, it is important to start from the goals. As discussed in earlier articles discussing manufacturing analytics and OEE, we saw that organizations are typically interested in improving machine/tool performance, quality and asset/machine availability. Which manufacturing parameters impact these items? Which parameters will help us understand the reasons for current levels of performance, availability and quality? Which are the machines/assets which are under-performing and need higher level of attention?

Answers to these and related questions will help us decide which equipment/machines/devices and associated parameters need immediate attention and this information will lead us to our iterative approach. Each iteration will focus on specifically identified machines and parameters and measurable value linked to business priorities. Forthcoming iterations will build upon earlier successes progressively including additional machines and parameters as dictated by the business requirements. In case there are any learnings, these will be carried over to the upcoming iterations thus ensuring continuous improvement. It is important to plan the iterations such that they are time-boxed – this helps stay focused on a defined scope to be achieved within a timeline.

Ensuring success with this strategy requires choice of a manufacturing analytics solution which satisfies the requirements. The solution needs to have the capability to capture real-time machine data and display it in ready-to-use dashboards with no custom development. To move iteratively and incrementally, one cannot invest a large capital amount in IT infrastructure and personnel to manage this solution. Similarly, a large capital investment in the solution may also pose hurdles given the unknowns in today’s business environment. At the same time, the solution should not be rigid but configurable based on user roles at the machine, section or plant level. A cloud based manufacturing analytics solution with real-time machine monitoring and subscription pricing at asset level addresses these requirements perfectly. A cloud deployment means that the organization does not need to worry about software or hardware infrastructure. This along with subscription pricing means that a very large upfront capital investment can be avoided.

MINTVIZOR is the manufacturing analytics solution with the right ingredients to suit such an AGILE approach. Are you willing to go the AGILE way? Write to and bring agility on your manufacturing floor.

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