Integrating ERP Systems with Supply Chain

🔗 Demand Planner vs. Supply Planner – The Real-World Balance In supply chain, we often hear about the roles of Demand Planners and Supply Planners. On paper, it looks simple: one forecasts demand, the other ensures supply. But in reality, the challenges are far more complex. ✨ Demand Planning Challenges • Forecasts are never 100% accurate – sudden market shifts, promotions, or competitor actions can throw predictions off. • Convincing sales and marketing teams to align with data-driven forecasts instead of “gut feeling” can be a daily struggle. • Overestimating demand leads to excess stock; underestimating means lost sales and dissatisfied customers. ✨ Supply Planning Challenges • Raw material shortages, supplier delays, or sudden capacity constraints disrupt even the best-laid plans. • Striking the right balance between inventory cost and service level is a constant juggling act. • Distribution bottlenecks (ports, transport strikes, regulatory changes) can derail supply plans overnight. 📌 The Reality: A demand planner may forecast 50,000 units, but if raw materials are delayed or production capacity is limited, the supply planner must adjust fast to minimize business impact. 🚀 True supply chain excellence comes when both roles work hand-in-hand—balancing market uncertainty with operational realities. #SupplyChain #DemandPlanning #SupplyPlanning #BusinessChallenges #Collaboration

NOT every miss is the demand planner's fault. This infographic shows what demand planners can and cannot control: Forecasting Accuracy ↳ Can control: adjusting forecasts based on data, trends, & customer inputs ↳ Cannot control: sudden market shifts, competitor moves, and black swan events S&OP Alignment ↳ Can control: aligning forecasts with sales, marketing, operations, finance ↳ Cannot control: overselling or underselling against the aligned S&OP plans Sales Collaboration ↳ Can control: working with sales teams to fine-tune demand plans ↳ Cannot control: last-minute sales-driven changes that disrupt planning Promotion Planning ↳ Can control: providing demand potential during promotions based on past promos performance ↳ Cannot control: final decisions on promotion timing and scale Demand Analysis ↳ Can control: analyzing early demand signals to adjust forecasts ↳ Cannot control: sudden demand surges or drops towards the month-end Forecasting Models ↳ Can control: choosing and refining forecasting models and methods ↳ Cannot control: inaccuracies due to unanticipated events or data limitations Seasonality Adjustments ↳ Can control: incorporating historical seasonal trends into demand forecasts ↳ Cannot control: unexpected seasonal changes, like delayed winters Product Launch Planning ↳ Can control: forecasting demand for new products based on trends of the like products ↳ Cannot control: launch timing, competitor response, product market, and pricing fit Demand Consensus ↳ Can control: driving consensus on demand plans across sales, finance, marketing and other related teams ↳ Cannot control: discrepancies between sales goals and realistic forecasts Any others to add?

What connects Industrial IoT, Application and Data Integration, and Process Intelligence? During my time at Software AG, my attention has shifted in line with the company's strategic priorities and the changing needs of the market. My focus on Industrial IoT, moved into Application and Data Integration, and now I specialise on Business Process Management and Process Intelligence through ARIS. While these areas may appear to address different challenges, a common thread runs through them. Take a typical production process as an example. From raw material intake to finished goods delivery, there are countless interdependencies, processes and workflows, and just as many data sources. Industrial IoT plays a key role by capturing real-time data from machines and sensors on the shop floor. This data provides visibility into equipment performance, production rates, energy usage, and more. It enables predictive maintenance, reduces downtime, and supports continuous improvement through real-time monitoring and analytics. Application and Data Integration brings together data from across the value chain, including sensor data, manufacturing execution systems, ERP platforms, quality management systems, logistics, and supply chain management. Synchronising these systems with integration creates a unified, reliable view of production operations. This cohesion is essential for automation, traceability, quality management and responsive decision-making across departments and geographies. Process Management, including modelling, and governance, risk, and controls, takes a different yet equally critical perspective. Modelling helps design optimal process flows, while governance frameworks ensure controls are in place to manage quality, risk, and enforce conformance for standardisation. Process mining uncovers bottlenecks, rework loops, and compliance deviations. It focuses on how the production process actually runs, rather than how it was designed to operate. Despite their different vantage points, each of these domains works toward the same goal: aggregating, normalising, and structuring data to transform it into information that can be easily consumed to create meaningful, actionable insights. If your organisation is capturing process-related data through isolated tools, such as diagramming or collaboration platforms, quality management systems, risk registers, or role-based work instructions, it is likely you are only seeing part of the picture. Without a unified approach to integrating and analysing this data, the deeper insights remain fragmented or out of reach. By aligning physical operations, applications & systems, and business processes, organisations can move beyond surface-level visibility to uncover the root causes of inefficiency, unlock hidden potential, and govern change with clarity and confidence. #Process #Intelligence #OperationalExcellence #QualityManagement #Risk #Compliance

My finance team was drowning in manual work, churning out late reports and missing insights. Sound familiar? As a CFO, I turned that chaos into a powerhouse. Here’s how. I joined a firm whose finance crew was stuck in spreadsheet hell. Errors were up, morale was down, and we missed a $500K savings. The fix? One word: automation. We implemented a cloud-based ERP system to streamline reporting. It cut processing time by 30% and freed the team to focus on strategy, like spotting a pricing tweak that boosted margins 5%. The bold insight: a team’s output reflects its systems, not just its people. A 2023 PwC study shows that automated finance teams are 25% more likely to drive strategic wins. Pick one process to automate this month. Start small, maybe with invoice reconciliation, and test a tool. It’s like giving your team a turbo boost. What’s slowing your finance team down? Share in the comments, or tag a leader ready to revamp their systems!

ERP is the only project where success feels like failure. When it works, nobody cheers. Orders ship. Invoices post. Reports run. Business carries on. But the road to get there? Brutal. Two years of “my job is different.” A month of execs realizing half their “urgent” reports were never opened. Entire departments learning deadlines actually mean… deadlines. Here’s the paradox: the more an ERP forces change, the more people resist. And yet, the biggest wins are the ones you never see: ✅ 14 spreadsheets vanish overnight ✅ Month-end close shrinks from 10 days to 1 ✅ Customers stop calling—because nothing goes wrong ERP success doesn’t end with applause. It ends with silence. And in operations, silence is the loudest signal of progress. That’s the silence we build for finance teams at DualEntry .

CPG brands are losing millions to expired inventory. But with lot tracking, FEFO, and FIFO, you can stop the bleed—if used correctly! 🚀 Lot number tracking is your quality control superhero. When problems arise, locate the exact batch in seconds. FEFO gets fresh perishables flying off shelves while reducing waste and delighting customers. FIFO ensures non-perishables rotate smoothly without gathering dust. Together, these strategies minimize expirations and maximize profits! But remember: implementation matters! A clunky system turns tools into roadblocks. Seek a 3PL partner that integrates effortlessly into your operations—one that truly understands CPG inventory management. Don’t let expired stock cut into your profits. Embrace smart practices for soaring efficiency! Your inventory is crucial; treat it accordingly! #InventoryManagement #SupplyChainEfficiency #WasteReduction #QualityControl #BatchTracking #FreshnessMatters #OperationalExcellence #PerishableGoods #NonPerishableStrategy #LogisticsSolutions

I’ve made mistakes in demand planning—mistakes that cost time, resources, and accuracy. If I could go back, here’s what I would tell my younger self: ↳ Don’t treat forecasting as a numbers game. Early in my career, I was obsessed with statistical models. I thought if I fine-tuned the algorithm enough, I’d get the perfect forecast. But I missed one thing—forecasting is as much about people and market insights as it is about data. Now, I ensure that numbers tell a story, not just a trend. ↳ Ignoring real-world disruptions is a dangerous game. Once, I confidently predicted demand for a product based on past trends. But then, a sudden regulatory change disrupted the entire market. My forecast? Completely irrelevant. Since then, I’ve learned that supply chain issues, economic shifts, and geopolitical events can break even the best predictions. ↳ Your best data source isn’t always in the system. I once dismissed a sales team’s warning about a shifting customer preference, thinking, "If the data doesn’t show it, it’s not real."" I was wrong. The sales team had firsthand insights from customers—insights that never made it into my spreadsheets. Now, I make sure to tap into sales, marketing, and customer service teams before finalizing forecasts. ↳ Gut feelings can be useful—but only if measured. I’ve seen leaders make brilliant intuitive calls—and also some that backfired spectacularly. The real lesson? Don’t discard intuition, but validate it. I now use Forecast Value Added (FVA) analysis to measure whether a human override improves or worsens accuracy. Data and experience must go hand in hand. Demand planning is a mix of art and science. What’s one forecasting lesson you learned the hard way? Let’s discuss.

A few years ago, I sat with the COO of a regional dairy brand. They were exporting to 9 countries. And one shipment got flagged for contamination in Saudi. The ERP had no direct trace from batch → invoice → customer. QC approvals were outside the system. Lot numbers were typed manually on dispatch labels. All because their ERP couldn’t answer one question in 60 seconds. Since then, I’ve made this a non-negotiable test before touching any ERP. I call it “The 30-Minute Inventory Truth Audit”. Here’s how I run it: 1. Batch-to-Invoice Trace (<60s) Can you pick any batch number and instantly see: All customer names Invoice numbers & dates Dispatch details 2. UOM Cross-Check (KG → PCS → ML) Do you buy in one unit, store in another, and sell in a third? Can your ERP auto-convert across all layers — even for partial returns and backflush? 3. Expiry Auto-Block Can an expired batch be accidentally picked or shipped? Does the ERP hard-block movement based on expiry rules? 4. FG to RM Reverse Trace Given a finished lot number, can you trace: Raw material batch Supplier GRN QC result? 5. Real-Time QC Status Integration Are QC results inside your ERP or stuck in spreadsheets? Can your store team see QC status at GRN, picklist, and dispatch? 6. Manual Overrides Visibility Can users edit batch numbers or expiry manually during dispatch? Are such edits logged, audited, and locked based on role? 7. One-Screen Traceability Is there a single screen where you can go: Batch → Lot → Customer → Invoice → Raw Material? 8. Test it Under Stress Run this when one of your team is on leave. Run it during high-volume days. Run it when your auditor walks in. This is not about software features. It’s about response speed when things go wrong. Because when the regulator, the press, or your board asks, “Who got this lot?”, you don’t get to say “We’re checking.” You get to say: “Here’s the screen. It’s all there.”

Day 25: Demand Planning is the only job where you apologise for being right! and being wrong! Sounds funny, but it’s true. If demand comes lower than forecast, 'Why did we over forecast?' If demand comes higher than forecast, 'Why didn’t we see this coming?' Either way, planners stand in the middle #balancing expectations, pressure, and limited visibility. But here’s the part people often miss: Forecasting is not about predicting perfectly. It’s about #preparing intelligently. A few years ago, we created 3 demand scenarios for a key product: - Pessimistic (P10) - Realistic (P50) - Optimistic (P90) Supply used this to build a #flexible plan: capacity triggers, material buffers, and a contingency slot. Guess what happened? Actual demand landed nowhere near P50, it fell almost exactly in between P10 and P90. But we still met service. Why? Because the goal wasn’t accuracy. The goal was #readiness. And that’s the evolution companies need: - Stop expecting the forecast to be perfect - Start building systems that can absorb volatility - Use scenarios, not single numbers - Plan capacity based on ranges, not guesses - Make supply and demand planning one team, not two functions The most successful businesses aren’t the ones with perfect forecasts. They’re the ones with #adaptive plans. So if you’re a demand planner reading this: Don’t fear being wrong. Fear not preparing the business and supply for what could happen. #DemandPlanning #Forecasting #SupplyChain #S&OP #Leadership #PlanningExcellence #Collaboration

Target Architecture for a Manufacturing Company (Integrating ERP, MOM, PLM, and IIoT into a Unified Platform)   Key Principles ·     Business-Outcome Driven: Focus on measurable KPIs like OEE improvement, downtime reduction, and cost optimization. ·     Hybrid and Scalable: Leverage edge and cloud for optimal performance and compliance. ·     Secure by Design: Implement Zero Trust and end-to-end security. ·     Open Standards and Interoperability: Use protocols like OPC-UA, MQTT, and ISA-95. ·     Data Governance First: Ensure data harmonization, lineage, and quality control.   Key Functions A. Capabilities and apps layer Apps covering specific use cases, e.g., predictive maintenance or automated error detection, that build upon standardized platform functionality   Apps provided by a third party or platform provider and available via an app store, e.g., overall equipment effectiveness for machines   B. Analytics and data platform Standardized (self-service) reporting, analytics, visualization, or location services available via API to all apps utilizing best-in-class algorithm libraries   Integration and harmonization of data, taking semantics of different protocols and machines into account   C. Operations services Highly scalable services handling basic platform functionalities such as device management (e.g., rights and roles, access management), service hosting, deployment and administration (e.g., activity monitoring, resource use), connectivity, and security (e.g., encrypted data exchange, key public infrastructure, certificates) available to all sites based on microservices and API   D. Integration into enterprise IT systems Interface to enterprise-level software, e.g., ERP, SCM, PLM, or CAD, via aggregating data and information generated in the app or analytics and data platform layers in formats pro- cessable by enterprise-level software   Enterprise-level software with access to the analytics and data platform and potentially also apps via API to perform processing that is not natively available   E. Integration of the IIoT platform with MOM Integration of the IIoT platform with the MOM layer to enable detailed scheduling of production, shifts, orders, and overall lines, and configuration and status information—input for operations analytics (quality, asset maintenance, overall equipment effectiveness) and other custom apps   F. SCADA, edge gateways, and machine-level connectivity Data routing and exchange with edge devices and machines, incl. data flow prioritization engines for forwarding raw or preprocessed data to the cloud   Data routing, prioritization, and storage enabled by on-site processing and storage within edge gateways   Easy integration of devices into the platform via plug and play     "Target Architecture Readiness Checklist is available with Team Transform Partner, if anyone wants to have access."   Source: Some inputs from McKinsey   Transform Partner – Your Strategic Champion for Digital Transformation