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TekMetrix Supply Chain Optimization & Revenue Enhancement
"SCORE"
Introduction
In the rapidly evolving landscape of supply chain management, the TekMetrix Supply Chain Optimization & Revenue Enhancement (SCORE) tool set is at the forefront of assisting corporations with driving technological innovations that reshape traditional practices and pave the way for more efficient, transparent, and responsive operations. TekMetrix SCORE diagnostics assist corporations to identify where, when, how and what are the business processes and targeted KPIs to leverage advanced technologies such as artificial intelligence (AI), blockchain, and the Internet of Things (IoT). TekMetrix SCORE diagnostics and SAP tools improve demand forecasting accuracy, ensuring real-time visibility, and facilitating seamless collaboration across the supply chain.
Revenue Growth Management
TekMetrix revenue enhancement tools provide diagnostics for pricing waterfall and pocket margin, price leakage, customer buying patterns, market patterns, SAP SD pricing conditions and recommend optimal pricing processes enhancing revenue and margins. As businesses strive to stay competitive and meet growing customer expectations, TekMetrix knowhow and SAP's comprehensive suite of supply chain management solutions, providing the data, is not only improving operational efficiency but also fostering sustainability and resilience in supply chain networks..
Transformational Processes
TekMetrix customer experiences, SCORE diagnostics, knowledge kits and tools in cohert with SAP SE's supply chain management solutions software (IBP or vendor equivalents) incorporate a range of advanced technologies to streamline operations and enhance decision-making.
- SAP IBP
- Customized AI adoptions to your supply chain
- Blockchain
- Transportation Management
- SAP EWM
- Leonardo
- Supply Chain Digital Twins
- S&OP
- AOP
- Trade Promotion Management
For instance, SAP Integrated Business Planning (IBP) uses AI and machine learning to provide accurate demand forecasting and optimize inventory management. SAP's blockchain technology ensures transparency and traceability across the supply chain, reducing fraud and errors. In logistics, SAP's transportation management solutions integrate with autonomous vehicles and drones, improving delivery times and reducing costs. SAP's Extended Warehouse Management (EWM) system leverages robotics and automation to speed up order fulfillment and minimize human error. The IoT capabilities within SAP Leonardo enable real-time tracking and monitoring of shipments, enhancing customer satisfaction through accurate delivery information. Additionally, SAP's digital twin technology allows businesses to create virtual replicas of their supply chains for better planning and scenario analysis. Emphasizing sustainability, SAP's applications support the use of renewable energy and eco-friendly materials, aligning supply chain operations with environmental goals. These SAP tools provide the data for advanced optimization with TekMetrix supply chain SCORE diagnostics.
Current Future State
TekMetrix is an SAP innovation partner. The future of supply chain management with TekMetrix SCORE, and SAP applications lies in the continuous adoption and integration of these cutting-edge technologies. From AI-driven demand forecasting to blockchain-enabled transparency, and from autonomous logistics to sustainable practices, TekMetrix's comprehensive SCORE solutions contribute to a more efficient, responsive, and resilient supply chain. As businesses navigate the complexities of the global market, leveraging SAP's innovative technologies will be crucial for maintaining a competitive edge and meeting the ever-evolving demands of customers. Using TekMetrix SCORE and SAP's roadmap for future innovations, companies can ensure their supply chains are not only efficient and cost-effective but also adaptable and sustainable, ready to face future challenges head-on.
TekMetrix SCORE
TekMetrix SCORE Diagnostics has been developed as a referencable library to assist supply chain consultants and their customers with supply chain productivity improvement and transformation. The reference library consists of 250 integrated supply chain diagnostics all working from a common data model and a common managerial profit loss, cash flow and balance sheet statement.That is, using TekMetrix SCORE software applications, each diagnostic is integrated with date, industy, country, company, strategic business unit, product and customer SKU level data formalized as a P&L. This represents our approach to continous improvement, transformation monitoring and adaptation of new capabilities. All diagnostics are delivered using a single application software platform, TekMetrix Diagnostic Software.
What is a TekMetrix Supply Chain Analysis & Design (A&D)
A TekMetrix Analysis and Design (A&D) refers to a critical phase in the project management process where our consultants analyze the current state of a business or a specific function and then deliver a solution design or strategy to address the identified issues and improve performance.
Analyis:
- Scope of TekMetrix Diagnostics used to guide the A&D
- Diagnostic project plan, timing, deliverable
- SAP Data Collection: Gathering relevant data and information about the current operations, processes, or systems.
- Current State Assessment: Understanding the existing situation, identifying problems, inefficiencies, and challenges.
- Gap Analysis: Comparing the current state with the desired future state or best practices to identify gaps that need to be addressed.
- Diagnostic Analysis: Determining the underlying causes of the identified problems or gaps:
- Financial Diagnostics
- Cost structure
- Cash flow
- Revenue and profitability
- Operational Diagnostics
- Business process capabilities and efficiency diagnostics
- Suppply chain management
- Resoure utilization
- Market Diagnostics
- Competitive analysis
- Market trends
- New product introduction, PLM
- Customer segmentation
- Buy till you die (BYTD) diagnostics
- R&D productivity and effectiveness
- Technology and Innovation
- Enterprise Architecture
- Data and data governance
- Innovation
- Ambidextrous capabilities
- Strategy diagnostics
- Example TekMetrix diagnostics
- Financial Diagnostics
Delivering SAP IBP
Supply Chain Optimization & Revenue Enhancement Diagnostic Library
TekMetrix Business Diagnostics Result in a Complete Supply Chain Improvement, Pricing Strategy and Technical Implementation Design. TekMetrix Diagnostics Guide the Deployment of New Business Models, Capabilities and Metrics.
TekMetrix SCORE Diagnostics integrate performance measures across all supply chain analytics into an overall framework linked to shareholder value. Opportunities and initiatives can be evaluated indivdually and as a group to assess their individual or group impact on shareholder value.
SAP Integrated Business Planning Project Management
Supply Chain A&D and Project Implementation Plan:
CPM
PERT
Example Excel Based Gantt, CPM, PERT project plan
Master Data Cleansing
IBP Planning Areas
IBP Planning Levels
IBP Planning KPIs
Characteristics Based Planning
Scenario Planning
Data Fabric
Datasphere
Signavio
Extreme Gradient Forecasting
Joule Use Cases: what if scenario planning, explanation of optimization runs, execution assistant to create orders
Generative AI - forecast explainability
Tableau
PowerBI
Microstrategy
Risk Predictions
PLM Integration
Digitized Supply Chain
Adaptive Supply Chain
Automouous Supply Chain
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Implementing Machine Learning in Your SAP S4 BW4 Platform
There are four components that have advanced the use cases for AI:
- AI software development and AutoML tools:
- TensorFlow
- Python
- R
- Julia
- H20.ai
- AutoGluon
- Automl-docker
- TekMetrix Skills
- Scientists and engineers using smart tools
- Strategy and analytics
- Computation
- Accelerated AI ML computers (CPU, GPU, DPU)
- Cloud providers of an AI stack
- Data
- SAP and corporate enterprise data as training data
- Internet data as training data
Machine learning is about creating an algorithm that can provide a prediction. These algorithms are based on mathematics. The mathematics are linear algebra, statistics, calculus and probability. Statistics is at the core of machine learning. Calculus determines how to learn and optimize a prediction; linear algebra allows the algorithm to run on large data sets and probability predicts the likelihood of an event occurring. There are variants to these algorithms that need to be understood so that the best Machine Learning model can be applies to a particular problem. These Machine Learning models are:
- Linear Regression
- Logistic Regression
- Decisions Trees
- SVM
- KNN
- Dimensionality Reduction
- Random Forecast
- K-Means
- Naïve Bayes
- Others
Machine learning use cases applied to SAP:
- Intelligent Automation and Process Optimization:
- ML can automate repetitive tasks, enhance accuracy, and reduce manual effort.
- There are 100's of use cases which include invoice processing, supply chain optimization, and workflow automation.
- Demand forecasting, AOP, S&OP and profitability management:
- ML models on S4 data, IBP, APO, BW4 Hana can predict demand patterns, optimize inventory levels, and improve supply chain efficiency. We write these in R on the Hana database.
- Benefits are improved production planning, inventory replenishment, improved trade promotion management, SKU level demand forecasting, and warehouse management.
- Customer Experience and Personalization:
- ML enables personalized recommendations, sentiment analysis, customer life time buy, BTYD, retention strategies, marketing campaigns, and customer segmentation.
- Enhance sales, marketing, and customer service processes.
- Financial Fraud Detection and Risk Management:
- ML algorithms can detect anomalies, identify fraudulent transactions, and assess credit risk.
- Improve financial processes, credit scoring, and fraud prevention.
- Quality Control and Maintenance:
- ML helps monitor product quality, predict equipment failures, and optimize maintenance schedules.
- Enhance production, maintenance, and quality assurance
- Supplier Relationship Management:
- ML assists in evaluating supplier performance, predicting delivery delays, and optimizing sourcing decisions.
- Streamline procurement and supplier collaboration.
- Human Resources and Talent Management:
- ML can analyze employee data, predict attrition, and recommend personalized learning paths.
- Optimize workforce planning, talent acquisition, and employee development.
- Supply Chain Visibility and Logistics:
- ML provides real-time insights into shipments, route optimization, and delivery tracking.
- Improve logistics, transportation management, and supply chain visibility.
- Energy Management and Sustainability:
- ML models analyze energy consumption patterns, optimize resource usage, and reduce environmental impact.
- Support sustainability initiatives and energy efficiency.
- Sales and Revenue Optimization:
- ML assists in pricing strategies, sales forecasting, and cross-selling recommendations.
- Enhance sales performance, pricing, and revenue management.
There are many uses cases for Machine Learning in business for SAP. Tools in SAP include PAL, APL, R, and PAi-ISLM. We leverage BTP for deep neural networks. First, however, the best Machine Learning model should be selected for the job.
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Optimizing Trade Promotion Management
The Consumer-Packaged Goods industry is a digital intensive industry. Manufacturing, Supply Chain and Finance processes are automated under one SAP enterprise platform. There is a central warehouse for reporting and analytics at the SKU level. During the daily selling process trade discounts are given to various retailers. These trade discounts are complex and involve legacy discounts, inflated rebates, loyalty programs, price changes, allowances, on and off invoice rebates, fines, freight, swell and others. Some large global CPG organizations have 100+ promotion expense and spend types.
These promotion and expense types must be planned annually with Key Account Managers who are responsible for the retail stores. This is a complex data intensive process to setup and approve annual trade budgets. P&G had net sales of $76.118 Billion in 2021 and spends 20% of sales on promotions. With the Global CPG companies selling well over $1 Trillion annually trade spend is a large cost. Not to forget the labor cost and lost productivity of creating and maintaining annual trade plans.
Incremental Volume and Trade Promotion Spend
Approximately 3 months before the beginning of the new fiscal year Key Account Managers, Finance, Sales Leadership and Supply Chain leaders must submit their data. Supply chain management submits their baseline volume that they expect to manufacture and sell. Key Account Managers organized by responsibility area (customer) layer on top of the base line volume plan their promotion types, validity dates, expense types, sell in, sell out and uplift expectations. These are negotiated annual contracts that the Key Account Managers must maintain. The promotion expenses are accrued as a lump sum and at an account, product level. As the year progresses, selling occurs and on the OTC process the trade discounts executed and settled.
For Many CPG organizations this process is Excel intensive for each account manager. Where promotions are setup in enterprise software the effectiveness of the spend is not readily apparent. Billions of records are stored in data warehouses. AI can be used to optimize the effective use of trade promotion spend.
Hierarchies |
Sales Volume Forecast |
Plan Analytics |
| Pricing Architecture | Baseline Volume Forecast (IBP, JDA) | Volume Metrics |
| Trade Funding Guidelines | Promotion Volume Forecast | Revenue Metrics |
| Product Hierarchy | Trade Promotion Types | Reporting Structures |
| Customer Hierarchy | ATMA Optimizations | |
| Responsibility Areas |
Optimization Model Hypothesis
Trade promotion spend is for a customer product SKU combination. There is a 12 month forecast for TPM ROI and an actual TPM ROI based on execution of the trade promotion. The mathematical algorithms of AI can be used to optimize the ROI of trade promotion spend using the set of equations below:
- Baseline Volume from Previous Year and Adjusted to Current Forecast Year
- Incremental Volume Planned and Tracked Using Latest Estimates, Actualized as Promotion Executes
- Baseline Product Price is Planned for the Forecast Year
- Incremental Revenue = Incremental Volume x Baseline Product Price
- ROI = (Incremental Profit – Promotion trade spend) / Promotion Trade Spend
- Incremental Profit = (Incremental Volume) x (Variable Contribution Margin)
- Promotion Trade Spend = (Total Volume) x (Variable Costs) + Fixed Costs
- Variable Contribution Margin = Customer Specific Price - (Material + Labor + Operating Expense + Freight Expense + Warehouse Allocation)
- Labor and Operating Expenses are Variable and Allocated
Experimental Design and Testing of Hypothesis
The mathematics of AI are readily available in most CPG companies enterprise software. It remains unused as most users in the domain do not understand the technology. To test the AI optimizers a team of a few Key Account Managers, Plant representatives and Finance SMEs need be organized with specific roles and responsibilities. We will choose a few brands, top sellers and less value-add brands to include in the test. An application prototype will be built and the test would be months in duration and most effectively performed for all 4 seasons.
Testing will be by variance analysis. That is, what was the ROI of the spend in previous years and what is it with the TekMetrix AI optimizer in place for the same customer, product, plant combinations. The objective is to maximize incremental sales. The optimizing decision variables in the equations are:
- Trade Promotion Spend for Each Customer Product Combination
- Incremental Volume due to the Promotion
- Baseline Customer Product Sales without Promotions
- Total Sales for the Customer Product During the Promotion
- Cost of Trade Promotion by Discount Type
Constraints are:
- Trade Promotion Spend
- Customer Specific Price
- Incremental Sales = Total Sales - Baseline Sales
- Total Sales During Promotion = Baseline Sales + Incremental Sales
- Baseline Volume
- Material Costs
- Labor Costs
- Operating Expense
- Freight Expense
- Warehouse Allocation
What discount type is most effective in improving incremental volume? Are the customers actually using the promotions offered (usage verses offered)? After the ROI is clearly demonstrated a larger project can be organized and funded for global rollout.
TPM Processes
Opportunities for Trade Promotion Management Optimization lay not only in manufacturing but across all the TPM processes.
- Budgeting and Allocation:
- Three months before the new fiscal year, this initial phase involves creating trade budgets and strategically allocating them across specific accounts
- Promotion Planning and Forecasting:
- A promotional calendar forecast for each retail customer and product SKU. This involves sales, expenditure, and profits, creating a comprehensive business plan with an SKU P&L
- Execution:
- The promotional plans are communicated to retailers, provide them with necessary materials, and meticulously track the outcomes. Larger retailers with purchasing power communicate their terms for TPM.
- Reconciliation:
- Match the spend with the corresponding activity. This phase validates and balances financial deductions associated with these expenses in the system.
- AI and ML Modeling:
- Our tools fine-tune predictions, optimize resource allocation, and harmonize financial accountability
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TekMetrix Diagnostic and Optimization Software
Innovations and Research
Hypothesis
Corporate innovation and analytic projects can often be a challenge for project teams due to various pain points, including lack of data-driven insights, slow and inefficient decision-making processes, and difficulties in implementing recommendations. Additionally, traditional consulting methods can be expensive and time-consuming, leading to a lack of scalability and limited ROI. These issues can result in missed opportunities for growth and improvement, causing frustration and hindrance to a company's success. By addressing these pain points with diagnostic driven analytical software consulting firms can improve their client’s business operations and help them stay ahead in today's fast-paced and competitive market.
TekMetrix Solutions
TekMetrix business consulting diagnostic software leverages advanced AI technologies such as artificial intelligence, machine learning, and data analytics to provide organizations with a comprehensive analysis of their operations, challenges, and opportunities. TekMetrix software consists of 250 integrated business diagnostic algorithms layered on pre-configured TekMetrix data models. The data model master and transaction data used for analysis and optimization derived from SAP S4 and not SAP ERP systems. Our software optimizes business processes deployed in corporate ERP systems.
TekMetrix software uses a combination of automated data collection, analysis, and inference engines to deliver actionable recommendations and insights that are tailored to each individual line of business across multiple industries. The software eliminates the need for manual data modeling, data analysis and interpretation, freeing up valuable time and resources that can be redirected towards implementing solutions and driving growth. Real-time data integrated with corporate AnyERP, S4, AnyDB and SAP HANA provides executive management, comptroller, financial and supply chain analysts with a shared analytical ecosystem. TekMetrix innovations value is comprosed of 4 variables. Value created (VC)= F(Concept, Execution, External Factors). Value Created is used as metric to guide the success of deploying TekMetrix Diagnostic and Optimization software.
Algorithms Integrated WithTekMetrix Software
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Regression Analysis: Regression models help predict numerical outcomes based on input features. For financial data, linear regression, polynomial regression, and other variants are often employed to model relationships between variables.
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Time Series Analysis: Time series models capture patterns and trends in data over time. Techniques like ARIMA (AutoRegressive Integrated Moving Average) and exponential smoothing are useful for forecasting financial metrics.
-
Machine Learning Algorithms:
- Random Forests: An ensemble method that combines multiple decision trees to make predictions. It’s useful for feature importance analysis and risk assessment.
- Gradient Boosting: Another ensemble technique that sequentially builds decision trees to improve prediction accuracy.
- Neural Networks: Deep learning models, such as feedforward neural networks and recurrent neural networks (RNNs), can learn complex patterns from financial data.
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Clustering Algorithms:
- K-Means: Used to group similar data points together. In finance, it can help identify customer segments or detect anomalies.
- Hierarchical Clustering: Creates a tree-like structure of clusters, useful for portfolio diversification.
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Optimization Techniques:
- Linear Programming: Solves optimization problems with linear constraints. It’s used for portfolio optimization, resource allocation, and supply chain management.
- Integer Programming: Extends linear programming to handle integer variables, suitable for discrete decision-making.
-
Graph Theory Algorithms:
- Network Analysis: Graph algorithms like centrality measures help analyze financial networks (e.g., interbank lending networks).
-
Statistical Tests:
- Hypothesis Testing: Determines whether observed differences are statistically significant. Common tests include t-tests, ANOVA, and chi-squared tests.
- Correlation Analysis: Measures the strength and direction of relationships between variables.
TekMetrix Diagnostic Software Model
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SAP ERPIII
TekMetrix Consulting Services assist our clients in strategizing, monitoring external technological developments that can impact your organizations competitiveness and setting up change and transformation initiatives. Each initiative must have its own business case. We have a unique blend of technical expertise, strategic vision and leadership skills as well as soft skills. Our goal is to clearly to explain to our clients how we will innovate new solutions for them.
We work with all types of business across multiple industries assisting with their technology architectures and roadmaps for vertical or horizontal integration activities. We are leaders in assisting SAP customers on their continuous ERPIII journey. ERP implementations 25 years ago took customer needs and implemented. Today's paradigm is to move from a moment of achieving an implementation towards a continuous journey. Technology change is rapid and ERP systems will need to continuous change and innovation. We move our clients from a fit to gap mentality to a fit to standard approach as ERP systems have moved from monolithic systems to modular systems.
SAP Clean Core is a strategic approach that emphasizes maintaining a standard, unmodified SAP core system while using external platforms and modular extensions to meet specific business needs. This approach aims to simplify upgrades, reduce complexity, lower costs, and enhance agility, stability, and compliance. By adopting SAP Clean Core, organizations can ensure their SAP systems remain scalable, maintainable, and adaptable to future innovations and changes.
Manufacturing systems have been transforming from mrp to MRP, MRPII to ERP to ERPII now ERPIII systems since the 1950s with the advent of computers and software. There is an obvious need and transition to a new ERP system given today’s technological capabilities. Competition is fierce and innovation capabilities must be a part of every organization. We are now entering the age of ERPIII systems. ERPIII systems are the next generation of Enterprise Resource Planning. ERPIII systems will fully digitize and automate the enterprise and edge processes. Edge processes constitute supply chain vendors, engineers, designers, customers and markets. ERPIII systems not only promise to have AI ML functionality built in all their business processes and analytics, they will naturally evolve to run the enterprise Autonomously.
Technical Innovations
TekMetrix creates novel solutions. Novel solutions are derived from product innovations, business process innovations, entering new markets and entering new industries. We provide a balance between Exploitation of existing technologies and Exploration for new novel solutions. Below are our work products:
SAP ERPIII and Ecosystem Platform Technology Strategy
We strategize with our clients. We identify an enterprise transformation program. It can be as simple as a new SAP platform, cloud version, or a complex deployment of multiple SAP systems, such as CRM, BW, SAC and S4 for trade promotion management. We look at their business problems, their current future state capabilities and desired future state capabilities and work internally to develop a business case for developing or implementing new technologies.
- Technology Advancements and Change
- TekMetrix Synegy Methods
- SAP Innovations
- Generating and Evaluating Commercial Ideas
- Entry Strategies
- Strategic Adaptation and Renewl
- Ecosystems
Trends in Technology
We monitor technology from all the global resources. We have developed tremendous assets and skills in basic science, basic research, design and engineering. Examples are IOT sensors, AI, ML embedded in SAP applications. We monitor applied research. We also have a marketing organization which maintains access to databases around the world where information is gathered on new and existing patents, new company startups, and who, in terms of what corporation, is implementing what technology.
- Machine Learning
- AI and Competitive Implications
- Blockchain and Cryptocurrencies
- Technology Policy
- Global Technology Trends
- The role of Open Source
- ESG Impacts
- SAP Roadmap
Execution with Initiatives
We execute by performing a finance and operational risk assessment against detailed project work breakdown structures. We determine with our client if they should be early adaptors or not of a new technology. We may or may not recommend the new technology. We train ourselves by accessing new technical capabilities from large successful SAP organizations through their training programs. We follow all the best practives of establishing a project from initiation to final closure and lessons learned.
- Scaling
- Descriptive, Predictive, Prescriptive Analytics
- Leading Innovation and Change
- Process View of Innovtion and Innovation Tournaments
- Internal and External Processes of Innovation
- Platform Acquistions
