Blogs to share thoughts, experiences, and knowledge.

Using Ollama as a Practical Local LLM and How to Tune It Better I have been looking at Ollama because it gives you a way to run LLMs locally without having to depend on cloud APIs for every test. For me, the value is simple. You can build, test, and experiment with AI on your own machine. You are not paying for every prompt while you are still trying to figure things out. You also have more control over the data because the model is running locally. Ollama is not going to rep

Over the past year, almost every conversation I’ve had with clients eventually turns to AI. Leadership knows AI matters.They know competitors are investing.They know it’s coming into enterprise systems whether they plan for it or not. But sooner or later, someone says something very honest: “We don’t really understand what the AI is doing.” And in my opinion, that concern is completely valid. The Issue Isn’t AI — It’s Trust After spending years working inside enterprise envir

In most SAP systems, the real problem isn’t lack of functionality. It’s weight. Years and years of transactions, documents, and attachments accumulate in the core database. Nothing is technically “wrong,” but everything becomes heavier: upgrades take longer, reports slow down, migrations get riskier, and storage costs quietly climb. When companies start planning an S/4HANA move, this weight suddenly matters a lot. You can technically migrate everything, but that usually makes

What Actually Helps After working through a few ECC to S/4HANA migrations, you start to see the same pattern repeat. The data usually moves.The tools usually work. But sooner or later, someone asks the question that slows everything down: “How do we know this is right?” That question doesn’t come from IT. It usually comes from Finance, Audit, or leadership — and it’s rarely answered well by load logs alone. Over time, one approach has proven consistently useful: Adding a smal

Overview In every industry today—finance, retail, government, tech, even small business—the real insight often isn’t found in dashboards or formal reports. It lives out in the open: in news headlines, discussion threads, product reviews, and social media conversations. If you can gather that information and understand the tone behind it, you get a clearer picture of what people are thinking long before the trend becomes obvious. That’s the value of combining web scraping wit

Overview This solution reads SAP’s built-in DVM statistics (from /SDF/DVM_* tables) using ABAP, exports the data to CSV, and feeds it into a Python analytics engine for: Predicting table growth (using Linear Regression) Detecting anomalies (via Z-Score) Recommending archiving or retention actions Visualizing everything in a Streamlit dashboard PART 1 — ABAP Extractor (ZDVM_EXPORT) Purpose Export SAP DVM statistics (table sizes, growth) from Solution Manager or connected syste

Extending SAP Joule with prescriptive intelligence. When SAP Joule appeared, it gave supply-chain teams a new kind of visibility — the ability to talk to their data.A planner can simply ask: “Which materials in US01 haven’t moved for 180 days?”“What’s the stock value by age band?” and Joule answers instantly from live SAP tables.That’s powerful — but it’s only the first step. AIS-IO™ (Inventory Optimizer) picks up where Joule leaves off.It adds predictive insight and opti

When SAP Joule arrived, it gave enterprise users something that had been missing for years — a natural conversation with SAP. For the first time, finance teams could simply ask: “What’s our DSO this quarter?” “Which customers are behind on payments?” and Joule would respond — instantly, contextually, and conversationally — using real SAP data. That’s a leap forward.But after working with Joule, I began to notice something deeper. Joule can tell you what happened. But it does

In every manufacturing project, planners face the same question:“How do we keep production aligned when demand keeps changing?” SAP Joule gives us a conversational window into enterprise data. You can ask questions like: “What’s our current backlog?” “Which finished goods are trending up in demand?” Joule answers well — but it stops at describing what’s happening .The next step is to predict what will happen — and to recommend an actionable plan. That’s where AIS-DPP™ (Deman

When SAP Joule came out, it felt like the beginning of a new conversation between people and systems.For the first time, I could literally ask my SAP system things like: “What’s our DSO this quarter?”“Which suppliers are behind on payments?” and Joule would respond with live insights — right from SAP’s own data. That’s powerful.But after working with it, I realized something: Joule tells you what happened .Finance teams also need to know what’s about to happen . That’s whe

Overview Enterprises generate vast volumes of financial, transactional, and compliance data every year. As systems mature, this information must be archived to maintain system performance, ensure auditability, and meet retention obligations. Traditionally, these archives have been static — created to satisfy compliance requirements but disconnected from analytical value. Archive Intelligence transforms this paradigm by turning archived SAP data into an active, intelligent, a

Why It Matters? Migrating from ECC to S/4HANA is one of the biggest transitions SAP customers face. Beyond technical conversion, one of...

Why This Matters Basis teams often need to know: Which BAPIs, RFCs, or custom Z* modules are heavily used? Where are the performance...

The Market Reality SAP is ending support for ECC by 2027. Every ECC customer must migrate to S/4HANA to remain supported. For many, this...

Data: The Deciding Factor in AI Development When artificial intelligence comes up in conversation, the focus almost always falls on...

Introduction I’ve worked on data archiving projects long enough to see a common pattern: many companies treat archiving like a quick...

The Hidden Costs of Not Archiving in SAP: Why Data Bloat Kills Performance Every SAP system grows quietly in the background. Month after...

Artificial Intelligence has made remarkable strides in recent years, but the field is still split into two very different approaches:...

Introduction When most people think of Artificial Intelligence today, they picture neural networks, machine learning models, or even...

Potential clients often approach me with the statement, “We want to archive our data.” What many don’t realize is that archiving isn’t...