The Data Intelligence Unit: Transforming TGC’s Operations Through Data Intelligence, Predictive Power, and Operational Excellence
The Data Intelligence Unit: Transforming TGC’s Operations Through Data Intelligence, Predictive Power, and Operational Excellence
A Proposal for the Introduction of the Monitoring, Evaluation, and Performance Unit for Timadix Geomin Consulting Limited
Prepared For: The CEO, Timadix Geomin Consulting Limited
Table of Contents
1. Introduction
2. Executive Summary
3. Current Operational Challenges
4. The Strategic Value of Data in Mining Operation
5. Proposed Data Intelligence Unit Framework
6. Key Deliverable & Value Contributions
7. Data Collection & Management Framework
8. Dashboard, Forecasting & Decision Support Systems
9. Financials & ROI Projections
10. Risks & Mitigation Strategies
11. Conclusion & Recommendation
1. Introduction
Project Title: The Data Intelligence Unit: Transforming TGC’s Operations Through Data Intelligence
Company: Timadix Geomin Consulting Limited
Document Type: Strategic Business Plan & Investment Proposal
Confidentiality Notice: This document contains proprietary and confidential information. It is intended solely for the use of the individual or entity to whom it is addressed.
2. Executive Summary
Timadix Geomin Consulting Limited is at a crossroads today in the volatile commodities market, where profit and loss are reduced to a razor-thin single-digit percentage point or two in efficiency.
We have a lot of useful operational data daily, a ton really, from the time blasting starts until the last weighed-loaded truck drives away, but we are not using that data! We are operating in a reactive, antiquated manner; we are missing millions of Naira in lost value, not to mention the operational and financial risks we are exposing ourselves to. The industry recognition in the National Bureau of Statistics 2007 report on the lack of a national mining database is a systemic industry problem we are better positioned to solve.
TGC’s mining operations, blasting, sorting, weighing, warehousing, selling, and logistics operations collect a lot of useful data every day. The data is in whole or in part underutilized, collected in manual, sub-optimal, disconnected operational systems, both operational data and mine waste data, which ultimately prevents either operational insight or strategic foresight from being achieved.
This proposal will provide for a possible 2-person Data Intelligence Unit (Data Analyst and/or Data Scientist) to:
✅ Improve operational efficiency
✅ Reduce costs (fuel, repairs, downtime, labor, logistics)
✅ Improve lithium grade consistency
✅ Forecast market prices and demand
✅ Provide for faster, smarter decision making
This proposal outlines the rationale for a strategic, lean, and effective investment in a Data Intelligence Unit (DIU) that consists of a Data Analyst and a Data Scientist. This two-member team will use our raw operational data as key input and convert it to strategic assets that will enable Timadix Geomin Consulting Limited to leverage its assets to become a more productive calculate drafter and a more proactive predictive drafter.
The Main Problems: Ineffective exploration and extraction, reactive equipment maintenance, poorly negotiated prices, and transportation bottlenecks are overall costing at least 5-10% of the potential margin per year, or an obvious calculated opportunity for profit with every ton of lithium ore mined and sold.
The Solution: The Data Intelligence Unit (DIU) will implement systems that provide:
Predictive Maintenance: Predicting when excavation/truck/weighbridge will break down and result in unplanned downtime and repair costs.
Resource Optimization: Improving the consistency of mined ore grades and optimization of extraction from multiple pits will ensure we can as consistently meet/ exceed a 5% Li₂O quality target.
Price & Market Forecasting: Based on SMM lithium benchmark prices and local supply and demand curves, maximize revenue on every shipment of ore.
Logistics & Supply Chain Optimization: Optimize logistics by reducing transportation costs and turnaround time for inbound (eg. fuel, siding stores) and outbound (material delivery) logistics.
Monitor Productivity and Profitability: Monitor productivity via the percentage of manual labor done on picking, bagging and loading measures.
The Investment:
Total Annual Personnel Cost: ₦18M (Data Scientist: ₦12M, Data Analyst: ₦6M)
One-time Setup (Software/Hardware/Sensors): ₦5m
The Return:
An estimate conservatively that annual cost savings & value creation will net ₦55m within the first 12-24 months, resulting in a projected ROI of > 300%. This is not a cost center, but a high ROI profit engine that is critical to sustaining our competitive edge, giving Timadix Geomin Consulting Limited a leading say in the graphite market in Nigeria.
The Problem (The High Cost of Operating Blind):
Timadix Geomin Consulting Limited is operating in complex global & local markets without a data intelligence function - it is like trying to drive with an incomplete map, the implications of which create serious and quantifiable downsides for Timadix Geomin Consulting Limited that impact our bottom line, often in the form hidden costs at every stage of our mining and trading processes.
3. Current Operational Challenges
Issues we can quantify and improve via data:
High equipment breakdowns and unplanned downtime
Variation in lithium ore quality (rejections due to sub-5% Li₂O)
Fluctuating production rates across different pits daily
Reactive maintenance regime
Informal and sub-optimal logistics, fuel inefficiency, and route scheduling
Manual stock and bag count discrepancies
Market prices and price movements are increasing the likelihood of trade losses
Poor visibility on site-wide performance trends
Labor cost variability ( manual pay and productivity)
Lack of predictive analytics for weather, market price, and equipment failure
The Application and Impact of a Data Intelligence Unit:
Proactive & Cost-effective Maintenance: Our excavators, tipping trucks, and weighbridge are returned to optimal operational condition before they break down. It will help reduce costs associated with unplanned downtime, unproductive halted production periods, and unexpected repair costs. We currently cannot predict failures when they will occur, and this is when moving blasted ore and loading trucks is delayed, and equipment downtime simply stagnates the operation. The breakdown rates (higher some days/weeks) are currently unexplained, and we don’t have proactive management for them.
Under-utilization of Resources & Quality Management: While our quality is confirmed by a geologist, our current approach does not effectively use geological survey data on a real-time basis to design the mining plan for our 2-3 pits. As a result, we can end up with stockpiles of inconsistent quality after sorting, higher costs of processing due to inefficient gangue removal with calcine, and possibly leave behind desirable lithium in the ground. We see that the amount of spodumene that we mine per day varies, though we do not investigate to understand the root causes.
Both Poor Market Timing & Revenue Loss: We are primarily going off the SMM lithium benchmark prices as a basis for the local Nigerian prices for lithium. However, we do not have an analytic function to see how local supply and demand can affect current spot prices, as we typically sell commodities by spot without the ability to project short-term price movements. A price improvement of 2% on an average shipment, or even just more cogent timing for sales, would result in millions of naira in pure profit that we are not now able to systematize.
Effective Use of Logistics & Supply Chain: Inefficient shipping routes for our own deliveries, fuel usage for equipment, and minimum turnaround times for buyers’ trucks, reliant upon a static contract and historical averages. We’re not dynamically optimizing our shipping distances based on real-fuel costs, rural road conditions, or even the best loading methodologies for up to 30-45 ton trucks, which is a significant reduction in transportation costs and potential demurrage costs. We keep track of fueling, equipment depreciation, and salaries daily, but we do not use it actively for efficiency. In summary, the overall workflow of semi-skilled, unskilled, and manual laborers (the use of human capital) is up for improvement, as everyone is within critical cost centers (i.e., sorting gangue, daily wage for laborers, bagging 50kg cement bags, etc). Time stamping these jobs is difficult without data analysis/dedicated resources, thereby impeding improvement opportunities, efforts wasted, and applicable insights to address the bottleneck (sorting/bagging cycles). Questions surrounding “what is the time it takes each day to mine” or “why day x has a higher amount than ushered material”remain largely unanswered, further limiting the company’s potential towards better scalability.
Increased Safety and Environmental Risks: While safety is critical, we currently identify hazards reactively from historical incidents. A predictive model of operational data would provide alerts for significant risk conditions prior to an incident and therefore protect the workforce and the environment.
Lack of comprehensive control over costs: From the blasting cost to scheduled and unscheduled repairs during down times, to the cost of labourers’ daily wages, to the cost of operating on multiple pits - all of these costs are recorded, but the cost for decision-making purpose is not combined in a meaningful analytical framework that identifies significant opportunities to cut costs.
In summary, we are making critical business decisions based on gut feel, historical average, and disparate data, a practice that our competitors are quickly abandoning in favor of data-led approaches. This “operating blind” practice directly translates into lower profitability, greater operational friction, and no opportunities in an aggressive, competitive environment.
4. The Strategic Value of Data in Mining Operations
Mining is an inherently data-rich environment, and our future value propositions are potentially driven by:
📊 Operations Data - Blasting schedules, ore movement, sorting rates, bagging records
⚙️ Equipment Data - fuel consumption, vibration readings, operating hours, breakdown reports
🛣️ Logistics Data - truck movements, weighbridge records, demurrage time
📝 Financial Data - costs (blasting, repairs, labor, haulage, overhead)
🌦️ Environmental Data - Weather patterns, dust levels
📈 Market Data - SMM lithium price reference point, local supply-demand metrics.
Without a system to capture, clean, analyze, and act on these data-critical inefficiencies will remain, costing the business millions.
The Solution: The Data Intelligence Unit (DIU) outlines the creation of a lean, two-person Data Intelligence Unit. This is not a large, bureaucratic department, but embedding highly targeted expertise where it can make the fastest and most profound impact on the profitability & efficiency of Timadix Geomin Consulting Limited.
Role Definition:
The Data Analyst (The Historian & Reporter)
Focus: What happened & why? One place that shows a "single source of truth" for all operational & financial data.Activities:
Data Collection & Integration: Connects to historic data sources (e.g., weighbridge logs, production sheets, lab results, fuel consumption records, repair logs, financial transactions, HR records for daily laborers on pay). It has to include digitizing some records that are not presently digital (e.g., daily pit reports, blasting logs) & complete their collection efforts.
Data Cleaning & Structuring: Ensures data quality consistency & completeness. This is important to avoid “garbage in, garbage out.”.
Develop Dashboards: Builds insightful, real-time dashboards. An example is:
Live Production Tracker: tons mined daily by pit, tons processed, tons bagged, tons shipped.
Assay Results Dashboard-Visualizes Li₂O percentages, impurity profiles, & trends from lab data so quality can be monitored against 5% target.
Equipment Performance Monitor: Monitors uptime, downtime events, repair cost, and maintenance schedule of excavators, trucks and weighbridge.
Trade Profitability Dashboard: detailed breakdown of income, costs (mining cost, logistics, labour, amortisation, fuel), and net margin for every shipment.
Logistics & Haulage Dashboard: Monitors truck turnaround time, fuel efficiency of company vehicles, and haulage costs externally.
Labour Productivity Tracker: Monitors sorting & bagging team inefficiencies (e.g., tons sorted per hour per employee, bags filled per hour).
KPI Reporting & Trend Analysis: Regular KPI reporting + identifying past trends, anomalies, and their potential future impact, and future areas to act upon immediately.
The Data Scientist (The Forecaster & Optimizer):
Focus: What will happen & how can we do better? Use past data to create a forward-thinking plan.Activities:
Predictive Model Development: Leverage the analyst’s cleaned dataset into complex predictive models. Some examples are:
Predictive Equipment Maintenance Model: examines telemetry data (if available from equipment manufacturers), historical failure patterns, number of hours of usage, and repair logs to create a “risk of failure” score for each critical asset (excavators, trucks, weighbridge). predicts when maintenance should occur before failure occurs.
Forecasting Model for Lithium Prices: Produces time-series forecasts for SMM CIF China prices, which integrate with local demand/supply characteristics to predict the best-selling windows in order to obtain better bargaining before entering contracts.
Ore Quality Prediction Model: Based on geological survey data and initial assay data, to predict the likely Li₂O content from different areas of the pits to allow for a strategic approach to extraction and blending.
Production Throughput & Delays Modeling: Models the effect of various factors, including weather, equipment conditions, and the availability of labor, to predict likely daily production and identify potential delays to production.
Optimization Models: Develops models to improve efficiency:
Haulage Route Optimization: Optimizes the internal haulage between the pits to the sort area, plus the external haulage from the sort area to the buyers, including fuel costs, road conditions, and truck load capacity.
Stockpile Blending Optimization: Recommends optimal blending ratios between ores from different pits or batches to achieve the 5 % Li₂O target consistently whilst minimizing waste.
Loading Efficiency Optimization: Analyzes weighbridge records and loading durations to determine a more efficient manual loading process for buyer trucks to decrease turnaround time.
Simulation & Scenario Planning: Carrying out “what-if” simulations (for example, the effect of a 10 % increase in fuel price, the effect of weather conditions on production output, the optimal production output by market price forecast) to help inform strategic decisions.
Real-time Benchmarking: To create a point of reference, we will look at our performance in comparison to industry benchmarks and internally set targets for productivity, efficiency, and cost reduction.
This interdependent relationship is important: Data Analyst covers the ‘what happened’ (past and present), identifying the accepted “truth.” A Data Scientist builds on this “truth” to build a more profitable and process-efficient future of Timadix Geomin Consulting Limited by predicting and optimizing potential future outcomes.
5. Proposed Data Intelligence Unit Framework
| Role Responsibilities | Value Contribution | |
|---|---|---|
| Data Analyst |
|
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| Data Scientist |
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6. Key Deliverable & Value Contributions
The DIU will provide Timadix Geomin Consulting Limited with useful tools and actionable outcomes, NOT JUST REPORTS! The project below will start and be operational within 12 months, addressing directly Timadix Geomin Consulting Limited’s measurable impacts:
✅ Real-time Production Dashboard - tonnes mined, tonnes sorted, tonnes bagged, tonnes dispatched
✅ Assay Quality Tracker - daily average Li₂O %, rejection rate, pit wise
✅ Equipment Downtime Predictor - predict failure days based on operational hours and vibration trend analysis
✅ Labor Productivity Metrics - tonnes per laborer per day, bagging speed, idle time
✅ Trade Profitability Dashboard - cost per internal cost, truckload price, and margin, real-time
✅ Predictive Lithium Price Model - Future CIF China Price Forecast (30–90 day)
✅ Logistics Optimizer - timing of dispatches, best routes, demurrage avoidance.
Live Production & Delivery Tracker
Function: An all-in-one dashboard that will show in real-time tonnes blasted, tons mined by pit, tons loaded by excavator into tipping trucks, tons delivered to sorting, tons processed, tons bagged, and tons shipped (company vehicles and buyers’ trucks) with daily, weekly, and monthly aggregates included.
Value: Instantaneously flag any bottlenecks in our production chain (pit to warehouse). It provides visibility for the Mine Manager and Operations team to identify and react to any delays (i.e., slow loading, long sorting periods, where the trucks are, etc.) and take corrective actions immediately. This will directly help to reduce the time of production per ton.
Assay Result & Quality Control Dashboard
Function: Visualizes real-time and historical ore grade (Li₂O percentage) and impurity (method) from lab data for every batch, linking back to pit and potentially blast. Each sample will show deviations from the minimum 5% Li₂O quality target.
Value: Enables geologists and processing managers to adjust their blending strategy to optimize consistent product quality thus greatly reducing uncertainties of rejection from buyers. It will also help to determine which pits give the best quality ore and strategize around more consistent methods of extraction.
Cost Analysis & Trade Profitability Dashboard
Function: A comprehensive waterfall chart that displays each shipment’s revenue, net margin, and all related expenses (mining: blasting, fuel, amortization, equipment repair, labor; logistics: internal and external transport; bagging, warehouse storage, salaries). Costs per ton sold and per ton mined will be monitored.
Value: The Sales, Finance, and C-Suite teams are able to make more informed decisions about pricing, target markets, and operational cost management thanks to the radical transparency it offers into our most and least profitable trades. This is essential for reducing the production cost as a whole.
Predictive Lithium Price Model (Local Market & SMM)
- Function: A three-month time-series forecast for SMM CIF China lithium prices, along with an examination of local Nigerian supply and demand dynamics to forecast the best local prices at which they are sold.
- Value: Enables the sales team to strategically time sales, negotiate better terms, and hold or sell inventory at the best times. A price increase of just 1% on our total yearly sales can result in profits of over millions of naira, which directly boosts revenue and profitability.
Equipment Maintenance Risk Indicator
Function: A model that analyzes telemetry data (if available), historical breakdown records, hours of use, and environmental factors (e.g., weather conditions that impact equipment stress) to generate a “risk of failure” score for each critical asset (excavators, tipping trucks, weighbridge).
Value: Shifts maintenance from reactive to predictive, preventing catastrophic failures and drastically reducing unplanned downtime and associated repair costs. By predicting failures, we can schedule maintenance during non-operational hours, ensuring continuous production and improving overall efficiency. Reducing unplanned downtime by just 10% can save millions of Naira annually in direct repair costs and lost production.
Logistics & Weighbridge Efficiency Optimizer
Function: Analyzes weighbridge data (tare weight, loaded weight, loading time, truck type), fuel consumption records, and internal/external haulage routes. It will identify bottlenecks in truck loading processes and recommend optimal routing for internal ore movement and external deliveries.
Value: Reduces fuel costs, optimizes truck turnaround times (for both our trucks and buyer trucks), and minimizes potential demurrage charges, leading to significant savings in logistics expenses and improved efficiency.
Labor Productivity & Throughput Analysis
Function: Analyzes daily laborer pay, hours worked, and tons sorted/bagged by different teams. It also examines the impact of weather conditions on daily production.
Value: Identifies the most productive teams and methodologies in sorting and bagging, allowing for the replication of best practices. It helps optimize labor allocation, increase quality and quantity of material processed per laborer, and forecast the impact of weather on production, enabling proactive planning.
In addition to suggesting the best routes for internal ore movement and external deliveries, it will pinpoint bottlenecks in truck loading procedures.
Analysis of Labor Productivity & Throughput
Function: Examines daily laborer pay, hours worked, and tons that are sorted or bagged by various teams. It also looks at how the weather affects daily output.
Value: Enables the replication of best practices by identifying the most effective teams and bagging and sorting techniques. It facilitates proactive planning by forecasting the effects of weather on production, improving the quality and quantity of material processed per laborer, and optimizing labor allocation.
Exploration & Pit Optimization Insights
Function: Combines production data from our two to three pits, drilling results, and geological survey data. Understanding the effects of various pit characteristics on extraction costs, ore quality, and overall productivity is beneficial.
Value: Offers information to help us decide which pits to concentrate on, direct future exploration activities, and make sure we effectively extract the most valuable ore.
In order to guarantee ongoing development and optimal profitability, the DIU will offer real-time assessment, monitoring, evaluation, comparative analysis, and benchmarking against set standards/targets in each of these areas.
Channels & Customer Segments
Our internal departments are the DIU’s “customers.” Successfully meeting their unique information needs and incorporating insights into their workflows are essential to the unit’s success.
C-Suite (CFO, COO, and CEO):
Requirements: Strategic, high-level summary of overall profitability, business health, market position, possible growth prospects, and important operational risks. They must see how data affects the “big picture.”
Channel: Quarterly Strategic Briefings, ad hoc custom reports for crucial decision-making, and Executive Summary Dashboards (e.g., consolidated P&L, overall operational efficiency, and market price forecasts).
Managers of Operations and Mines:
Requirements: To manage daily activities, spot bottlenecks, streamline procedures, and guarantee a smooth production flow from blasting to bagging, tactical, real-time data is required.
Channel: Pit-specific performance dashboards, Equipment Maintenance Risk Indicator (sent as automated SMS/email alerts), Live Production Tracker (daily/hourly updates), and direct consultations.
Sales & Finance Teams:
Requirements: Data for profitability analysis of individual shipments, accurate price negotiation, inventory management, and financial forecasting.
Channel: Trade Profitability Dashboard, Predictive Lithium Price Forecast (with confidence intervals), Sales Performance Dashboards, and joint strategic planning sessions.
Geology & Exploration Teams:
Requirements: Tools to better understand ore body characteristics, optimize extraction from different pits, and ensure consistent quality control.
Channel: Assay Result Dashboard, advanced geological modeling support, and custom reports on pit performance and ore blending effectiveness.
Logistics & Haulage Teams:
Requirements: Real-time information on truck movements, loading/unloading times, fuel consumption, and optimized routes.
Channel: Logistics & Haulage Dashboard, automated alerts for delays or inefficiencies, and route optimization recommendations.
Security Teams:
Requirements: Proactive identification of security risks based on operational patterns or external data.
Channel: Alerts for anomalous activity patterns and integrated security incident dashboards (if pertinent data is available).
By taking a focused approach, the DIU’s insights are not lost in reports but instead are sent straight to the decision-makers who need them most, allowing them to enhance their own operations and support the success of the business as a whole.
7. Data Collection and Management Framework
Information to be gathered every day:
Tonnes were mined, loaded, sorted, and bagged.
Equipment: Fuel consumption, hours worked, breakdown logs, and use of spare parts
Sorting: Team output per shift
Weighbridge: Tare and loaded weights per truck
Warehouse: Bag count, stock-in/out records
Finance: Daily expenses, blasting cost, labor cost, and repairs
Lab: Assay results per stockpile
Market: Local market prices and the daily SMM lithium price
Weather daily log
Administration Method:
Create a centralized operational data warehouse
Put data governance and validation procedures in place
Collect data in real-time from weighbridges, equipment logs, and production logs.
8. Dashboard, Forecasting & Decision Support Systems
Tools
Live production, logistics, assay, and profitability dashboards
Programming models for route optimization, maintenance forecasting, and price forecasting
Real-time Alerts: SMS/Email for assay problems, market price fluctuations, and equipment at risk
Examples of Use
Real-time pit operations adjustments based on production rates and quality
Prevent breakdowns to cut downtime by 20–30%
Adjust the haulage schedule and fuel purchase according to price trends.
To maximize sales timing, forecast price trends (1–3% price increase on yearly sales).
Figure 1 shows an example of an executive dashboard
Figure 1: Image shows an example of an executive overview dashboard for a cobalt company
9. Financials & ROI Projections
i. Investment & Personnel Costs
This is a lean, high-impact investment designed to yield rapid returns. The primary cost is expert human capital, reflecting the competitive market rate for these high-demand skills in the Nigerian/West African market.
| Item | Annual Cost (₦) |
|---|---|
| Data Scientist Salary | 12,000,000 |
| Data Analyst Salary | 6,000,000 |
| Software/Cloud Computing | 3,000,000 |
| Initial Hardware/Sensors | 2,000,000 |
| Total Annual Investment | 23,000,000 |
For simplicity, the initial hardware/sensors cost is included in the first year’s “investment” to cover immediate needs, even though it is a one-time setup that may depreciate over several years. Costs in subsequent years would mostly be related to staff and software/cloud.
ii. Projected ROI
The return on this investment will be realized through direct cost savings and enhanced revenue generation across various operational areas. We present a conservative estimate based on industry benchmarks for data science initiatives in mining, tailored to Timadix Geomin Consulting Limited’s specific operations.
| Area of Value Generation | Estimated Annual Savings/Boost (₦) |
|---|---|
| Fewer Assay Rejections & Improved Quality | 30,000,000 |
| Fuel Cost Reduction (Logistics & Equipment) | 10,000,000 |
| Price Optimization & Better Sales Timing | 15,000,000 |
| Reduced Unplanned Downtime & Repair Costs | 20,000,000 |
| Increased Labor Productivity & Throughput | 5,000,000 |
| Total Estimated Annual Value | 80,000,000 |
| Year | Total Annual Investment (₦) | Total Annual Value Generated (₦) | Net Gain (₦ | ROI (%) |
|---|---|---|---|---|
| 1 | 23,000,000 | 80,000,000 | 57,000,000 | 247% |
| 2 | 18,000,000 | 100,000,000 (Conservative growth) | 82,000,000 | 455% |
Projected ROI within 24 months exceeds 300%, demonstrating that this initiative is an exceptionally high-yield profit engine.
10. Risks & Mitigation Strategies
While the benefits are substantial, it’s crucial to acknowledge and mitigate potential risks to ensure the successful implementation and maximum impact of the Data Intelligence Unit.
Potential Risks & Mitigation Strategies:
Poor Data Quality (“Garbage In, Garbage Out”): Our mining operations, from manual sorting to weighbridge records, generate a lot of data, but its quality and consistency might vary.
- Mitigation: The Data Analyst’s first and most critical task will be a comprehensive data audit. This involves identifying all data sources (digital and paper), assessing their current state, and establishing robust data governance standards and protocols from day one. We will prioritize digitizing key manual records and implement systematic data entry procedures. We will start by building models with the cleanest, most impactful data sources (e.g., financial data, weighbridge data) to demonstrate early wins and build confidence.
Resistance to Adoption & Cultural Shift: Managers and teams accustomed to traditional methods might resist new, data-driven approaches or feel threatened by increased transparency.
- Mitigation: The DIU will be positioned as a supportive service and a strategic partner, not a policing unit. We will launch pilot projects in receptive departments (e.g., logistics, a specific pit operation) to create powerful internal case studies and demonstrate immediate value. Training and workshops will be conducted to help teams understand how data empowers them, rather than replaces their expertise. Emphasizing how the DIU helps them achieve their goals will be key.
Inaccurate Models & Over-Reliance on Forecasts: Predictive models are not crystal balls and can be wrong, leading to misguided decisions if not properly understood.
- Mitigation: All models will be rigorously back-tested against historical data to assess their accuracy. Forecasts will always be presented with confidence intervals (e.g., a “likely range”) to manage expectations and communicate uncertainty. The DIU will foster a culture of continuous model refinement and validation, adapting to new data and market conditions. Decision-makers will be educated on how to use forecasts as powerful tools for informed decision-making, not as absolute truths.
Integration Challenges with Existing Systems: Connecting disparate data sources (e.g., manual records, specific equipment logs, accounting software) can be complex.
- Mitigation: The DIU will adopt a phased approach, starting with readily available data and gradually integrating more complex sources. They will leverage flexible, scalable tools (like Python for scripting and Power BI for visualization) that can connect to various data types. Collaboration with any existing IT personnel will be crucial.
11. Conclusion & Recommendation
This proposal offers quantifiable value. This is not a hire; this is a fundamental enhancement to how TGC makes decisions, optimizes operations, and competes.
The decision before us is not merely the hiring of two additional personnel. It’s a decision to make a fundamental, strategic shift in our operating philosophy at Timadix Geomin Consulting Limited. We can continue to drive our complex marketplace by looking in the rearview mirror, making reactive choices on gut and incomplete information, or we can seize the opportunity to look ahead, anticipate difficulties, and capitalize on market inefficiencies that are presently unknown to us.
Investing ₦23,000,000 annually to release over ₦80,000,000 of annual value is, by a wide margin, the most compelling, high-return opportunity for the business. The Data Intelligence Unit is the fulcrum to transform Timadix Geomin Consulting Limited into a leaner, more intelligent, and demonstrably more profitable Nigerian lithium market leader. It will allow us to:
⦁ Decrease the Cost of Production: Through predictive maintenance, optimized logistics, and efficient labor management.
⦁ Enhance Quality and Quantity of Material: Through optimizing extraction, improving sorting operations, and reducing rejections.
⦁ Reduce Time of Production Per Ton: By identifying and eliminating bottlenecks in the entire mining and trade value chain.
⦁ Improve Overall Efficiency: With real-time monitoring, actionable insights, and proactive problem-solving.
⦁ Drastically Reduce Down times and Equipment Failures: Moving from reactive to predictive maintenance.
As a Data Analyst and Data Scientist, my commitment is to deliver this value with excellence, ensuring that every naira invested in the DIU translates into multiple naira of profit and operational advantage. This unit will be a catalyst for continuous improvement, setting new benchmarks for productivity and profitability within our industry.
We strongly suggest expedited approval to personnel and fund this critical unit. This is an investment in our future, our competitiveness, and our leadership position.
“DDDM becomes particularly useful when businesses have access to large interconnected data sets from past and current operations. Extrapolating from this, the ability to collect large data sets can be a DDDM enabler, assuming that the required analytical capabilities are also present.” (Bisschoff and Grobbelaar 2022)
Recommendation
⦁ Appointment of Data Analyst and/or Data Scientist
⦁ To begin Phase 1 Data Collection (to collect primary data and data transformation) within 20 working days
⦁ To begin Phase 2 (Live Production Dashboard, Assay Quality Tracker, Trade Profitability Dashboard) within 20 working days
⦁ Complete DIU infrastructure live afterwards (dependent on date of employment)
The business world is moving to data-driven mine management. If TGC moves first, it wins. If it lags, it loses.
Investing ₦23M to unlock ₦75M a year is a strategic no-brainer.
This Data Intelligence Unit will render TGC a smarter, leaner, more profitable market leader in Nassarawa and beyond.