MaxInsighter’s EVIPIM methodology is a structured data management approach that begins with defining the problem and validating it against business constraints. It involves ideating solutions, creating a proof of concept, implementing the solution, and monitoring its effectiveness. This process ensures tailored improvements in data management.
MaxInsighter uses its own EVIPIM methodology, which is a comprehensive data management approach designed to streamline and optimize your data processes. It begins with Explore & Identify, where we define the problem statement to scope the solution accurately. Next, we Validate and Diagnose by breaking down the problem using a top-down approach, ensuring we address each issue at its smallest level of granularity while validating against your business constraints. We then Ideate Solutions by proposing wireframe solutions for your future state (To-Be) compared to your current state (As-Is). Following this, we create a Proof of Concept (POC) to prototype the solution. The Implementation phase involves deploying the solution along with all project deliverables. Finally, we Monitor & Evaluate to ensure the solution meets its intended purpose and make necessary adjustments. This structured approach ensures that our solutions are tailored to drive meaningful improvements in your data management.
Define the problem statement scoping a solution
Have a deep understanding of the current challenges faced, and develop an as-is state of the processes/data points to develop a scope for the solution
Now that we understand the problem, let’s validate and evaluate it to get a benchmark
Doing a thorough validation will allow us to assess the required effort to solve and also to have a benchmark KPI to measure the effectiveness of the solution:
- Ensure that the problem genuinely exists, and we have covered all bases
- Identify the magnitude of the problem allowing us to set targets
Identify and evaluate possible solutions
We explore and provide multiple options, break down the pros and cons of all the possible solutions, and suggest a scalable recommendation that fits your needs and budgets.
Develop a minimal viable product before the final solution
We want to prove the basic functionality is acceptable and solicit changes early on before working extensively on the finished solution:
Iteratively build the final solution
Now that our prototype is approved, we iteratively work on the entire solution:
Ensure the solution meets the intended purpose and measure success
Once a solution is implemented, we closely monitor and evaluate it against the benchmark KPIs set during the “validate and diagnose” phase.
Example Case Study Using Our EVIPIM Methodology
Let’s take the case study of LaxmiKant, a growing mid-sized running shoe manufacturing company. Laxmikant faces significant data management challenges, leading to inefficient production planning and inventory control. Their existing systems are outdated, causing frequent errors and delays in decision-making.
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1. Explore & Identify
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2. Validate and Diagnose
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3. Ideate Solutions
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4. Proof of Concept (POC)
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5. Implementation
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6. Monitor & Evaluate
In the Explore & Identify phase, we work closely with Laxmikant to define the problem statement and scope the solution. This involves conducting interviews with key stakeholders, analyzing current workflows, and identifying pain points such as inefficient production planning and inventory control. By understanding the root causes of these issues, we can accurately scope a tailored solution to address their specific challenges.
During the Validate and Diagnose stage, we break down Laxmikant's data management problems using a top-down approach. We drill down to the smallest level of granularity to identify where errors and inefficiencies are occurring within their production planning and inventory control processes. This involves validating these findings against Laxmikant's business constraints to ensure that any proposed solutions align with their operational needs.
In the Ideate Solutions phase, we propose wireframe solutions for Laxmikant's future state (To-Be) by comparing it with their current state (As-Is). This involves designing new workflows and systems that integrate modern data management tools to enhance production planning accuracy and streamline inventory control. By visualizing these solutions through wireframes, we can better communicate how Laxmikant can transition from their outdated systems to more efficient ones.
The Proof of Concept (POC) stage involves prototyping the proposed solutions to test their feasibility and effectiveness. For Laxmikant, this means setting up a pilot project that demonstrates how new data management tools can improve production planning accuracy and reduce inventory errors. This pilot helps validate assumptions and identifies any potential issues before full-scale implementation.
During the Implementation phase, we deploy the validated solution along with all necessary project deliverables. This includes training Laxmikant's staff on new systems, integrating the solutions with existing infrastructure, and ensuring a smooth transition from their outdated systems. We also establish clear documentation and support processes to facilitate ongoing use and maintenance of the new data management tools.
In the final Monitor & Evaluate stage, we continuously assess whether the implemented solution is meeting its intended purpose of improving production planning and inventory control at Laxmikant. This involves tracking key performance indicators (KPIs) such as error rates, decision-making speed, and overall operational efficiency. Based on these evaluations, we make any necessary tweaks or adjustments to ensure that the solution continues to drive meaningful improvements for Laxmikant's data management processes.