🦘Can we jump from Excel to Optimization & AI?

YES.

🤚Is it a good idea? It depends on the maturity of the business processes.

The key elements for a successful optimization project are:

📈 data consistency & literacy
👷🏻‍♀️ planning tool adoption
🛠️ parameters fine-tuning.


🪜Best practices recommend a phased approach.

1️⃣ ➡️ 2️⃣ We start transitioning from Excel to an APS tool (advanced planning and scheduling). Such tools bring the IT infrastructure, integrated automated processes and manual planning features.

This facilitates step-by-step data architecture development without concern over missing data elements. In the same time, users can become familiar with the new functionality while planning.

2️⃣ ➡️ 3️⃣ Implementing a heuristic is the initial move towards optimization. Establishing clear rules for optimization means challenging current practices and increasing awareness about the business ways of working.

Rule-based algorithms simplify result interpretation, enabling quick and precise adjustments.

3️⃣ ➡️ 4️⃣ The next phase involves simulations using scenario-based tools for predictive analysis and decision-making by modeling supply chain aspects under varying conditions.

4️⃣ ➡️ 5️⃣ Once we have data consistency, user-friendly & efficient tools, and we have understanding of the system behavior, we can use the insights from simulations to create a robust optimization model.

This ensures that the model aligns with the real-world dynamics and constraints identified during the simulation phase.

🎯 In brief, (mathematical) optimization is an add-on to a planning tool. We first ensure the tool is configured to support the planning business processes (S&OE, MPS, RCCP, S&OP, IBP). They key users are comfortable with the tool. Afterwards we start bringing in the optimization elements.

🎤 In your experience, is there another stage that needs to be added?