How do you optimize performance in a changing environment?

Observability is the most important aspect of understanding the current capacity. If something is not measured, it can't be understood and optimized. Service level indicators (SLIs) play a key role in observability metrics. As IT leaders, one must understand the observability from the customer perspective, than many internal metrics. If these SLIs with respect to the customer is rightly set, you can access the current capacity accurately which will in turn help you to predict future trend.

To optimize performance in a changing environment, there are several strategies you can consider: 1. Adaptability: Embrace a mindset of adaptability and agility. 2. Continuous Monitoring: Regularly monitor performance metrics and key performance indicators (KPIs) to identify areas that need improvement. 3. Flexibility: Be flexible in resource allocation. 4. Effective Communication 5. Continuous Learning: Encourage a culture of continuous learning and professional development. 6. Collaborative Problem-Solving: Foster teamwork and collaboration among employees. 7. Embrace Technology: Embrace technology solutions that can automate repetitive tasks, streamline processes, and improve efficiency

The most critical factor to being successful in an environment riddled with a multitude of variables is to be open minded. You have to learn into the uneasiness and the uncomfortability. Remain limber. Be fluid. Pivot. Until you control what the customer picks up off the shelf or place into their cart, you must reconcile the fact that plans change. Don't be emotionally attached to a strategy that you can't pivot if it isn't yielding the desired results. Don't be afraid to fail as you search for new footing. Lastly, don't envision your future based on past accomplishments and successes. Today is new day. Embrace the challenges that come with it. Just because you haven't done something before, doesn't mean that you can't. Be first always.

In a MFG operation: .SFC data such as WC output and productivity, yield and shrinkage factors, machine/Labor availability, lead time (move time, wait time , machine time ..) and all data elements related to SFC must be accurately measured or estimated, and periodically updated. .the best forecasting and simulation methods for demand (backlog,back order) is chosen by the order mgt. so a realistic and close to accurate forecasted usage through the planning time fence is created. .integration and synchronization of inventory records (minimum 95% accuracy )and, BOM data with preferably 100% accuracy with the SFC data, must be established ,and updated periodically based on demand fluctuations. .Use of MRP2 methodology.

Before assigning new work, it's important to assess and comprehend each member of the project team's capacities while taking into account their backlog. The creation of a dashboard that displays the present workload at the individual level will aid in providing information on the additional workload that can be supported. This process will be eased by measuring the work in story points. This analysis is also going to assist in a proper resource utilization within the team.

In the world of AIs ( machine learning and artificial intelligence) predicting future trends can be done more accurately than the previous statistical methods. The future demand prediction must be a multi-dimensional mathematical model powered by a machine learning algorithm involving all parameters as a dimension to the existing model ( such as customer feedback, market trends, business results, and competitor analysis). Dimensionality should be kept as optimal as possible to avoid either overfitting or underfitting the model.

Future demand does not just refer to fresh external opportunities. It also means to include fresh prospects within the structure already in place. Planning for the future will be made easier by comprehending the organization's vision and developing long-range plans (LRPs) that can be further divided into yearly OKRs. In order to keep up with the constantly shifting market demand, it is crucial to comprehend the current market trends as well as technology and incorporate them into this process.

In the world of cloud, resource planning and allocation should be dynamic and fitted with event-driven architecture. it starts with observability and converting the observability metrics as an event to trigger the scale-up/down of the resources dynamically. AI-enabled Operational platforms (AIOPS) can be of great use to dynamically scale up/down the resources and optimally allocate the resource to meet the dynamic demand in real time.

The current workload of an individual, his expertise or skill set, and the maximum amount of workload that the individual can support must all be taken into consideration when planning the allocation of resources. One will be able to understand the team's capacity and predict the deliverables once we develop this for each member.

Implementing the capacity plans can be orchestrated by automation tools, As I explained earlier, it should be event-triggered/driven architecture. Given trigger event, the capacity argumentation should be automated using automation tools, by which we can reduce the cycle time of capacity agumentaiton

After the capacity is planned in the previous step, we need to execute the planned allocation. Capacity utilization is an on-going process, hence it needs to be monitored regularly. Changes needs to be accommodated to the capacity plan whenever required and when a recourse it under/over utilized. The decision to change is taken based on the dashboards, reports and other metrics that are created to monitor the utilizations.

Regular review of the dashboards/reports needs to be done to understand the updated information on capacity utilization. Changes/updates to the plan needs to be done based on the utilization and sometimes also due to the project demands.

This is a closed feedback loop mechanism with error correction in real time. Cycle 1: Observability triggers the event --> Event -- Fed to --> AIOPS --> Automation executes the augmentation. Cycle 2: Again starts with Obserbiity which triggers a fine-tuning of the event to further optimize the capacity augmentation This cycle will continue until the error become zero

Capacity augmentation and performance optimization should be dynamic, intelligent, and agile. Age-old methods of static prediction and argumentation will not work in the ever-changing environmental conditions. Prediction by AI ML methods, dynamic event-triggered augmentation and performance turning by automation tools is the key to success

This document does not answer the question it raises, only suggests capacity planning techniques, and nothing on performance