Complex Use Case with scientific Data analysis:

Let us elaborate, what the next step will be from a basic S&OP/IBP application to a highly sophisticated IBP/Supply Chain Optimization for a very complex use case.
  • Before we start with this use case, please consider, that our consultants do not intend to overrun and overrule your team, by impressing them with our specialized knowledge.
  • Their might be exceptions, but normally this knowledge is not available that often in a company. – But it can be learned quite fast and easy.
Your teams will be trained by us, to run the projects and us, your consultants, if you like! We want them to be and stay the owner of the company:
  • We will need to understand You and Your Teams first. What is the company standing for and what is the teame capable of to do. We support you in this step with our workshops.
  • Please expect your teams to be able to deliver only a part of the data at a good definition and understanding. These are complex mathematical theories and we want to prepare your teams to understand them, before we are going to implement them.
  • From our experience the way of developing, aligning and understanding of such data for such a special planning project is huge and needs time. Maybe in all of your long experience of wins and failures, you made the same observation, that time used upfront, will pay off hundreds times during the project implementation with a successful ending, instead of a fail.
  • Our experience advises us to neglect any offer for a project, which is not preparing the workforce for such a huge step, as we are not going to jeopardize our reputation on this. Our business model allows us low fix costs, as we do not have to sell our teams to keep them busy.
Fails in Digitization in Mio. USD
Your Team running a Digitization Project with us

Use Case for a Global Supply Chain Optimization

In this case a technical example with mathematical focus for planning solutions!

1. Key aspects of the company situation:

1.1 Company Profile

  • Industry: Advanced technology, specifically semiconductors and IoT devices
  • Scale: Multinational, with operations across multiple continents
  • Market: Serves diverse markets with rapidly changing demand patterns

1.2 Current Challenges

  1. Global Supply Chain Complexity: Managing a supply chain that spans multiple continents
  2. Demand Volatility: Dealing with rapidly changing demand patterns across diverse markets
  3. Product Complexity: Producing advanced semiconductors and IoT devices, which require sophisticated supply chain management
  4. Supply Chain Optimization: Struggling to optimize their global supply chain effectively

1.3 Solution Requirements

The company is seeking a comprehensive solution that integrates:

  1. Advanced Planning: To better manage their complex global supply chain
  2. Real-time Analytics: For immediate insights into supply chain performance and market demands
  3. Machine Learning: To enhance predictive capabilities and automate decision-making processes

1.4 Objectives

  • Streamline Production: Optimize the production of advanced semiconductors and IoT devices
  • Enhance Supply Chain Resilience: Improve the ability to respond to disruptions and changes in demand
  • Increase Efficiency: Optimize operations to reduce costs and improve performance
  • Improve Forecasting: Better predict demand patterns across diverse markets

1.5 Planning Scenario Environment

  • 5 semiconductor fabrication plants (2 in Asia, 2 in North America, 1 in Europe)
  • 8 assembly and testing facilities (3 in Asia, 3 in North America, 2 in Europe)
  • 15 distribution centers globally
  • Over 1000 suppliers across 30 countries

2. Solution Approach

The goal is to find the optimal inventory levels and ordering policies at each echelon that minimize the total cost while meeting service level requirements.

3. Models used for this approach

The models we use provide a sophisticated approach to demand forecasting and inventory optimization, incorporating multiple factors and considering the entire supply chain structure.

3.1 Demand Forecasting Model

The demand forecasting model is represented as:

Dt=f(Ht,Et,Mt,St)+ϵtDt​=f(Ht​,Et​,Mt​,St​)+ϵt​

This is a comprehensive model that combines multiple factors to predict demand:
  • DtDt​: This is the dependent variable, representing the demand at time t. It’s what we’re trying to predict.
  • f(⋅)f(⋅): This represents a function that combines the input variables. The exact form of this function can vary depending on the specific forecasting technique used (e.g., linear regression, neural networks, etc.).
  • HtHt​: Historical data at time t. This could include past sales data, seasonality patterns, and trends. It’s crucial because demand often follows historical patterns.
  • EtEt​: External factors at time t. These are variables outside the company’s control that can influence demand, such as:
    • Economic indicators (e.g., GDP growth, inflation rates)
    • Competitor actions (e.g., product launches, pricing changes)
    • Regulatory changes
    • Weather patterns (for weather-sensitive products)
  • MtMt​: Market intelligence at time t. This represents insights gathered from market research, customer surveys, and industry reports. It could include:
    • Customer preferences
    • Market trends
    • Upcoming events that might affect demand
  • StSt​: Social media sentiment analysis at time t. This factor incorporates the impact of social media on demand, which can be particularly important for consumer products. It might include:
    • Sentiment scores from social media posts
    • Viral trends
    • Influencer activities
  • ϵtϵt​: The error term at time t. This represents the difference between the predicted and actual demand, accounting for factors not captured by the model or random fluctuations.

3.2 Multi-Echelon Inventory Optimization Model

3.2.1 Model Overview

A comprehensive approach to minimizing total cost across a supply chain network. By optimizing this function, engineers can determine the ideal inventory levels and ordering policies for each location in the supply chain, minimizing overall costs while maintaining desired service levels.

3.2.2 Formula Explanation

TC=∑i=1N(hi⋅E[Ii]+bi⋅E[Bi]+Ki⋅E[Oi])TC=∑i=1N​(hi​⋅E[Ii​]+bi​⋅E[Bi​]+Ki​⋅E[Oi​])Where:

  1. TCTC represents the total cost across all locations in the supply chain.
  2. ∑i=1N∑i=1N​ indicates summation over all N locations in the network.

3.2.3 Cost Components

The model considers three main cost components for each location:
  1. Holding Cost: hi⋅E[Ii]hi​⋅E[Ii​]
    • hihi​ is the per-unit holding cost at location i
    • E[Ii]E[Ii​] is the expected inventory level at location i
    • This term represents the cost of storing inventory
  2. Backorder Cost: bi⋅E[Bi]bi​⋅E[Bi​]
    • bibi​ is the per-unit backorder cost at location i
    • E[Bi]E[Bi​] is the expected backorder level at location i
    • This term represents the cost of stockouts or delayed orders
  3. Ordering Cost: Ki⋅E[Oi]Ki​⋅E[Oi​]
    • KiKi​ is the fixed cost per order at location i
    • E[Oi]E[Oi​] is the expected number of orders placed at location i
    • This term represents the cost associated with placing orders

3.2.4 Engineering Considerations

  1. Interdependencies: The model accounts for the relationships between different echelons. For example, inventory levels at one location can affect backorders at another.
  2. Stochastic Nature: The use of expected values (E[]) indicates that this model considers variability in demand and lead times.
  3. Trade-offs: The model balances the costs of holding too much inventory against the costs of stockouts and frequent ordering.
  4. Optimization Challenge: Minimizing this function is computationally complex, often requiring advanced algorithms or simulation techniques.
  5. Data Requirements: Implementing this model requires accurate data on holding costs, backorder costs, and ordering costs for each location, as well as demand patterns and lead times.

3.3 Supply Chain Network Optimization

The mixed-integer linear programming (MILP) model allows engineers to determine the optimal network configuration, including which facilities to open and how to route shipments, while minimizing total costs and meeting all supply and demand requirements.

3.3.1 Objective Function

min⁡Z=∑i,j,kcijkxijk+∑jfjyjminZ=∑i,j,k​cijk​xijk​+∑j​fj​yj​

This function aims to minimize the total cost Z, which consists of two parts:
  1. Transportation costs: $\sum_{i,j,k} c_{ijk} x_{ijk}$
    • Sums the cost of all shipments across all origins (i), destinations (j), and transportation modes (k)
  2. Fixed facility costs: $\sum_j f_j y_j$
    • Sums the fixed costs of operating open facilities

3.3.2 Constraints

3.3.2.1 Supply Constraint

  • ∑j,kxijk≤si∀i∑j,k​xijk​≤si​∀i
Ensures the total quantity shipped from each origin i does not exceed its supply capacity $s_i$. This applies to all origins i.

3.3.2.2 Demand Constraint

  • ∑i,kxijk≥dj∀j∑i,k​xijk​≥dj​∀j
Ensures the total quantity shipped to each destination, if j meets or exceeds its demand $d_j$. This applies to all destinations j.

3.3.2.3 Facility Operation Constraint

  • xijk≤Myj∀i,j,kxijk​≤Myj​∀i,j,k
Links shipments to facility operations:
  • If $y_j = 0$ (facility j is closed), no shipments can be made to or from that facility
  • If $y_j = 1$ (facility j is open), shipments are allowed up to the value of M
  • M is a large number, typically set to the maximum possible shipment size

3.3.2.4 Variables and Parameters

  • $d_j$: Parameter for the demand at location j
  • $x_{ijk}$: Continuous variable representing the quantity shipped from i to j using mode k
  • $y_j$: Binary variable (0 or 1) indicating whether facility j is open or closed
  • $c_{ijk}$: Parameter for the unit cost of shipping from i to j using mode k
  • $f_j$: Parameter for the fixed cost of operating facility j
  • $s_i$: Parameter for the supply capacity at location i

3.4 Risk Management and Resilience

We might decide for a Bayesian network model to assess and mitigate risks with a combination of a Monte Carlo Simulation.
In practice, these models allow us to update our risk assessments based on new information. For example, if we observe supply chain disruptions (evidence), we can calculate the updated probability of a major risk event occurring. By combining these techniques, engineers can create a sophisticated risk management system that not only assesses current risks, but also predicts potential future scenarios and evaluates the effectiveness of various mitigation strategies.

3.4.1 Bayesian Network Model

The Bayesian network model is represented by the equation:

P(Risk∣Evidence)=P(Evidence∣Risk)⋅P(Risk)P(Evidence)P(Risk∣Evidence)=P(Evidence)P(Evidence∣Risk)⋅P(Risk)

​This formula is Bayes’ theorem, which calculates the posterior probability of a risk given observed evidence. Let’s break it down:
  • P(Evidence)P(Evidence): The total probability of observing the evidence, acting as a normalizing constant.
  • P(Risk∣Evidence)P(Risk∣Evidence): The probability of a risk occurring given the observed evidence. This is what we’re trying to calculate.
  • P(Evidence∣Risk)P(Evidence∣Risk): The likelihood of observing the evidence if the risk is present.
  • P(Risk)P(Risk): The prior probability of the risk occurring, based on historical data or expert knowledge.

3.4.2 Monte Carlo Simulation

Here’s how it works:
  1. Model Setup: Define the Bayesian network structure, including risk factors, their relationships, and initial probabilities.
  2. Scenario Generation: Use Monte Carlo methods to randomly sample from the probability distributions of each risk factor. This creates numerous potential risk scenarios.
  3. Probability Calculation: For each scenario, use the Bayesian network to calculate the probability of various risk outcomes.
  4. Mitigation Strategy Evaluation: Implement potential mitigation strategies in the model and re-run the simulation to assess their impact on risk probabilities.
  5. Analysis: Aggregate results across all simulations to get a comprehensive view of risk probabilities and the effectiveness of different mitigation strategies.
This approach offers several advantages for supply chain risk management:
  • It captures complex interdependencies between different risk factors.
  • It allows for the incorporation of both historical data and expert knowledge.
  • It provides a quantitative basis for comparing different risk mitigation strategies.
  • It can be updated in real-time as new evidence becomes available, allowing for dynamic risk assessment.

3.5 Collaborative Planning and Execution

By integrating the following features, we create a unified platform that enables real-time visibility, proactive decision-making, and improved collaboration across the entire supply chain.

3.5.1 Scenario Planning and What-If Analysis

This feature allows users to model different supply chain scenarios and assess their potential outcomes.
  • It uses advanced algorithms to simulate various conditions, such as demand fluctuations, supply disruptions, or changes in production capacity.
  • Engineers can adjust parameters like lead times, inventory levels, or production rates to see how they affect the overall supply chain performance.
  • The system likely employs Monte Carlo simulation techniques to account for uncertainties and provide probabilistic outcomes.

3.5.2 Supplier Collaboration and Order Management

This functionality streamlines communication and coordination with suppliers.
  • The platform might integrate with suppliers’ systems using APIs or EDI (Electronic Data Interchange) for seamless data exchange.
  • It provides a shared platform where both the company and its suppliers can view and update order information.
  • The system may use blockchain technology to ensure data integrity and traceability across the supply chain.
  • Engineers can implement automated workflows for order processing, approval, and tracking.

3.5.3 Exception Management and Alert Generation

This functionality helps identify and manage deviations from the planned supply chain operations.
  • The system continuously monitors key performance indicators (KPIs) and compares them against predefined thresholds.
  • When a KPI exceeds its threshold, the app generates an alert, notifying relevant stakeholders.
  • Engineers can set up custom alert rules based on specific business logic or operational constraints.
  • The alert system might use machine learning algorithms to detect anomalies and predict potential issues before they occur.

3.5.4 KPI Tracking and Performance Management

This feature provides real-time visibility into supply chain performance.
  • It aggregates data from various sources (ERP systems, IoT devices, etc.) to calculate and display KPIs in real-time.
  • Engineers can create custom dashboards to visualize KPIs relevant to their specific roles or departments.
  • The system likely uses data warehousing and OLAP (Online Analytical Processing) techniques to enable quick analysis of large datasets.
Project Plan