Analytics Strategic Consulting

We firstly consult with key stakeholders within your organization to capture their needs.

We then design a robust analytics infrastructure that addresses multiple business challenges concurrently, ensuring it will serve as the backbone of your organization by offering both high level and granular views to the strategic, tactical and operations levels in your company.  

We use DMAIC, Root Cause Analysis, and FMEA, along with business analysis techniques like interviewing, process attachment, observation, and job shadowing.

We apply Lean Six Sigma principles to improve efficiency, automate tasks, and ensure quality throughout the analytics adoption process.

The first step is to identify key initiatives and processes that require support through advanced analytics.

In your multi-annual and annual objectives, we will find key initiatives that will be solved by changing a fixed cost into a variable one. Insourcing or outsourcing a service. These to not require data analytics.

But there will be others that will need heavy data analytics support such as  reducing the working capital. For a problem like this we will design an automated system that launches paid media campaigns for low moving inventory and inventory that risks obsolescence. 

Our process for designing an analytics infrastructure is thorough and tailored to ensure it aligns perfectly with your business objectives and scales with your needs. Here’s a detailed overview of our approach:

1. Analyze Objectives and Requirements

We begin by analyzing your strategic objectives and the specific problems your analytics architecture needs to solve. This involves:

• Consulting STRAP and AOP Plans: Understanding your Strategic Plan (STRAP) and Annual Operating Plan (AOP) to identify key initiatives that require data analytics support.
• Stakeholder Engagement: Engaging with key stakeholders to gather insights and requirements, ensuring that the analytics infrastructure aligns with your business goals and priorities.

2. Evaluate Existing Technologies

Next, we assess the technologies you currently use. This evaluation helps us:

• Leverage Existing Investments: Make use of your existing technology stack to avoid unnecessary costs and disruptions.
• Integration: Ensure that the new analytics infrastructure can seamlessly integrate with your existing systems.

3. Scalability and Growth Considerations

To future-proof your analytics infrastructure, we:

• Growth Rate Analysis: Calculate the rate of growth for data storage and usage to anticipate future needs.
• Scalable Technologies: Choose technologies that scale efficiently, with variable costs that adjust as your usage increases or decreases.
• Cost Management: Ensure that the selected technologies offer cost-effective scalability options to manage expenses as you grow.

4. Data Warehouse Design

Designing a robust data warehouse is crucial for effective analytics. Our approach includes:

• Critical Data Identification: Identifying and prioritizing the critical data needed for key business processes.
• Granularity: Designing the data warehouse to hold different granularity views of the same data, enabling comprehensive reporting from the executive team to the operational team.
• Metric Consideration: Paying special attention to additive and non-additive metrics to ensure accurate and meaningful analytics.

For example, in a supply chain context:

• Executive Reporting: A VP may need to see aggregated backorders across all business lines.
• Operational Reporting: A supply chain data analyst will require detailed, day-by-day views of backorders to manage supplier relationships effectively.

By coupling these views, we ensure that company objectives cascade from top-level executives to warehouse operators, all measured with consistent KPIs.

This alignment ensures everyone in the organization is working towards the same goals.

5. Implementation and Continuous Improvement

Once the design is complete, we move on to:

• Implementation: Deploying the infrastructure with careful attention to detail to ensure it meets all design specifications and business requirements.
• Monitoring and Optimization: Continuously monitoring the performance of the analytics infrastructure and making adjustments as needed to optimize efficiency and effectiveness.

We analyze reports to diagnose top challenges and understand the root causes of performance gaps, focusing on the most impactful areas.

We deploy techniques such as predictive analytics, machine learning, and AI to address root causes of performance gaps and drive strategic growth.

By aligning analytics projects with business objectives, engaging stakeholders, and designing solutions that yield significant efficiency gains and ROI.

We use process mapping, optimization, waste reduction, and continuous improvement methodologies (Kaizen) to enhance business processes.

At Witanalytica, we prioritize automating decision-making processes to enhance efficiency and accuracy. After carefully mapping, understanding, and improving the decision-making rules, we automate them because algorithms and machines consistently outperform humans in finding optimum solutions and solving complex problems. Here are several examples of how we incorporate automation in various scenarios:

1. Recommender Systems:

Example: We develop and deploy advanced recommender systems that suggest products to customers based on their browsing history, purchase patterns, and preferences. This personalized approach increases customer engagement and boosts sales.

Benefit: Automating product recommendations helps increase average order value and improves customer satisfaction by providing tailored shopping experiences.

2. Campaign Management:

Example: We use data analytics to determine the most effective marketing campaigns to launch. By analyzing customer data and past campaign performance, we automate the selection and timing of marketing emails, social media ads, and promotions.

Benefit: This automation ensures that marketing efforts are timely and targeted, maximizing ROI and customer reach.

3. Route Planning:

Example: We implement automated route planning systems that optimize delivery routes based on factors such as traffic conditions, delivery windows, and fuel efficiency. These systems use real-time data to adjust routes dynamically.

Benefit: Automated route planning reduces delivery times, lowers fuel costs, and improves overall logistics efficiency.

4. Inventory Management:

Example: We automate inventory management by integrating predictive analytics to forecast demand and adjust inventory levels accordingly. This includes automating reordering processes and stock level adjustments based on sales trends and seasonality.

Benefit: This ensures optimal inventory levels, reducing the risk of overstocking or stockouts, and improving supply chain efficiency.

5. Fleet Management:

Example: We deploy automated fleet management systems that monitor vehicle performance, maintenance schedules, and driver behavior. These systems use data analytics to predict maintenance needs and optimize fleet utilization.

Benefit: Automating fleet management reduces downtime, extends vehicle life, and enhances operational efficiency.

6. Production Planning:

Example: We implement automated production planning systems that schedule and adjust production processes based on real-time demand, supply chain status, and resource availability.

Benefit: This leads to more efficient production cycles, reduced waste, and better alignment with market demand.

7. Dynamic Pricing:

Example: We implement dynamic pricing algorithms that adjust prices based on market demand, competition, and inventory levels. These algorithms can be applied to shipping rates, product pricing, and service fees.

Benefit: Automated dynamic pricing maximizes revenue by capturing the highest possible price customers are willing to pay while remaining competitive.

8. Predictive Maintenance:

Example: We utilize predictive maintenance systems that use machine learning algorithms to analyze equipment data and predict failures before they occur.

Benefit: Automating maintenance schedules based on predictive analytics reduces downtime, lowers maintenance costs, and prevents unexpected equipment failures.

By incorporating automation into these areas, we help our clients achieve higher efficiency, lower operational costs, and enhanced decision-making capabilities. Our automated solutions are designed to adapt and scale with the evolving needs of the business, ensuring long-term success and competitiveness.

We actively engage stakeholders through interviews, workshops, and continuous communication to ensure their needs and insights are incorporated.

At Witanalytica, we serve any industry that generates and utilizes data, especially those that are looking to move beyond the limitations of Excel. Our analytics consulting services are designed to be versatile and adaptable, meeting the needs of a wide range of sectors. Here are some examples of how our solutions can be applied across different industries:

E-commerce, Retail, Affiliate Marketing, and Advertising

Recommender Systems:

• E-commerce: Suggest products to customers based on their browsing history and purchase patterns to increase sales and customer satisfaction.
• Retail: Personalize in-store promotions and online recommendations to drive higher engagement and loyalty.
• Affiliate Marketing: Optimize offer placements and recommendations to increase conversions and maximize affiliate commissions.
• Advertising: Tailor ad content and placement based on user behavior and preferences to improve ad performance and ROI.

Supply Chain, Logistics, and Transportation

Operations Research Algorithms:

• Balancing Consignment Stocks: Use algorithms to optimize inventory levels across various locations, ensuring products are where they are needed most.
• Route Optimization: Apply the same algorithms to plan efficient delivery routes, reducing travel time and fuel costs.
• Production Planning: Schedule and adjust production processes based on real-time demand and resource availability, ensuring efficient use of resources and timely product delivery.

Healthcare, Finance, and Manufacturing

Database and Warehouse Design:

• Our approach to database and data warehouse design is industry-agnostic. We focus on:
  • Critical Data Identification: Ensuring all necessary data is captured and easily accessible.
  • Data Quality: Implementing robust data governance to maintain accuracy and reliability.
  • Scalability: Designing systems that can grow with your business, accommodating increasing data volumes and complexity.

Examples:

• Healthcare: Creating a centralized data warehouse that integrates patient records, treatment plans, and medical research to improve patient care and operational efficiency.
• Finance: Designing a data warehouse that consolidates financial transactions, customer profiles, and market data to support risk management and strategic decision-making.
• Manufacturing: Developing a warehouse that tracks production metrics, inventory levels, and supply chain data to optimize manufacturing processes and reduce costs.

Cross-Industry Applications

Automation:

• Dynamic Pricing: Implement algorithms that adjust prices based on real-time demand, competition, and inventory levels, applicable in sectors like retail, travel, and hospitality.
• Predictive Maintenance: Use machine learning to predict equipment failures and schedule maintenance proactively, useful in industries like manufacturing, logistics, and utilities.

At Witanalytica, our analytics consulting services are tailored to unlock the full potential of data, no matter the industry. Whether you’re looking to enhance customer experiences, optimize operations, or drive strategic growth, we have the expertise to help you achieve your goals. Our solutions are designed to be scalable, ensuring they grow and adapt with your business, delivering long-term value and success.

You can start by booking a data analytics strategic consulting call with us, where we will discuss your business objectives and how we can support your analytics adoption journey.