Dynamic Priority Setting

Continuing the conversation from my previous post, I wanted to dive deeper into how budgets actually behave once the year begins. The budget we start the year with is rarely the one we end up executing. Plans shift, cash tightens, priorities reshuffle — and suddenly the numbers need to move with the business, not against it. In regulated settings, this movement is normal. Mid-year or even quarterly reviews often reveal new cost realities or cash pressures that annual planning couldn’t predict. Rather than resisting these shifts, the priority is disciplined recalibration within regulatory and long-term boundaries. This is what that looks like in practice: - Rolling reforecasts aligned with operational performance and real-time cost movements - Cashflow-driven reprioritization when liquidity tightens - Stage-gate approvals for projects introduced mid-year to validate feasibility, urgency, and regulatory defensibility - Portfolio shifts based on safety, customer impact, asset health, ROI, and compliance risk - Risk-based governance — deeper scrutiny on uncertain or high-value projects, faster clearance for predictable ones But continuous adjustments have a real impact: execution capacity slows when priorities keep shifting.Teams pause for revised approvals, vendors wait for confirmation, and timelines stretch — not due to inefficiency, but because responsible financial management demands careful, traceable decisions. Still, when dynamic budgeting and disciplined execution work together, organisations stay responsive without compromising regulatory credibility. How does your organisation manage shifting priorities without letting execution suffer? #RealTimeBudgeting #CashflowManagement #DecisionMakingInFinance #BusinessResilience

A friend working in B2B order fulfillment recently faced a challenge where bulk orders were frequently delayed, leading to frustrated clients and lost business. The operations team assumed the issue was with slow dispatch times, but a deeper analysis using SQL and data analytics revealed hidden inefficiencies in order processing. Optimizing Order Fulfillment with SQL 1️⃣ Pinpointing Where Delays Occur We first examined which stages in the order lifecycle were consuming the most time. SELECT order_stage, AVG(time_spent) AS avg_time_spent FROM order_tracking GROUP BY order_stage ORDER BY avg_time_spent DESC; 🔹 Insight: The biggest bottleneck wasn’t in dispatching but in order verification and invoicing. 2️⃣ Finding High-Risk Orders Prone to Delays Next, we identified which types of orders were most likely to be delayed. SELECT order_type, COUNT(order_id) AS total_orders, COUNT(CASE WHEN delivery_time > expected_time THEN order_id END) AS delayed_orders, (COUNT(CASE WHEN delivery_time > expected_time THEN order_id END) * 100.0 / COUNT(order_id)) AS delay_percentage FROM orders GROUP BY order_type ORDER BY delay_percentage DESC; 🔹 Insight: Large corporate orders had a higher delay rate due to manual approval processes. 3️⃣ Optimizing Dispatch Prioritization We then tested a dynamic dispatching strategy based on order urgency. UPDATE orders SET priority = CASE WHEN order_type = 'corporate' AND delivery_time < expected_time + INTERVAL '2 hours' THEN 'high' WHEN order_type = 'retail' AND stock_available = 'low' THEN 'medium' ELSE 'low' END; 🔹 Insight: Assigning priority levels dynamically ensured critical orders were processed faster. Challenges Faced Manual Approvals Delayed Fulfillment: Some orders took hours to pass verification. Warehouse Picking Inefficiencies: Large orders required multiple pickers, slowing down operations. Lack of a Dynamic Prioritization System: Orders were fulfilled on a first-come-first-serve basis, rather than based on urgency. Business Impact ✔ 25% improvement in order fulfillment speed by automating verification workflows. ✔ Better prioritization of bulk orders, reducing high-value client dissatisfaction. ✔ Fewer last-minute order escalations, improving warehouse efficiency. Key Takeaway: Order fulfillment isn’t just about faster dispatch—it requires data-driven prioritization, workflow optimization, and automation. Have you solved logistics challenges with SQL? Let’s discuss!

𝗛𝗼𝘄 𝗜 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗲𝗱 𝘀𝗽𝗿𝗶𝗻𝘁 𝗽𝗹𝗮𝗻𝗻𝗶𝗻𝗴 𝘂𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗞𝗻𝗮𝗽𝘀𝗮𝗰𝗸 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 📦 What do factories and software development have in common? I decided to take a concept I’d used in manufacturing and apply it to sprint planning. Well, let’s just say it worked out better than I expected… 𝗧𝗛𝗘 𝗖𝗛𝗔𝗟𝗟𝗘𝗡𝗚𝗘 🎯 In Scrum, you break a project into smaller chunks called sprints, which are time-boxed, typically 2-4 weeks long. Each sprint has a list of tasks from the product backlog, each with an estimated development time and priority set by the product owner 📋.  The goal is to figure out which tasks to take on in the sprint, given the fixed time, to maximize value. This is essentially a knapsack problem, where the sprint is the knapsack with limited capacity (in this case, the number of available days), and the backlog tasks are the items, with their time estimates as weights and their priorities as values ⚖️. 𝗧𝗛𝗘 𝗦𝗢𝗟𝗨𝗧𝗜𝗢𝗡 💡 To solve this, I modeled the problem using dynamic programming. Here’s how it works: 1. Break down the tasks: each task has a time to complete and a priority. 2. Set constraints: the total time available for the sprint (let’s say 10 days) is the capacity of the “knapsack.” 3. Calculate values: I used dynamic programming to decide which tasks provide the highest value without exceeding the available time ⏱️ For example, let’s say we have six tasks with the following times and priorities: Task Time (days) Priority 1     10        6 2     6         5 3.    6         4 4     4         3 5     3         2 6     2         1 The goal is to select the tasks that maximize value without exceeding the 10-day limit 🏁 𝗧𝗛𝗘 𝗢𝗨𝗧𝗖𝗢𝗠𝗘 🎉 Through dynamic programming, I calculated the optimal combination of tasks that fit within the 10-day sprint. The most valuable tasks to prioritize were tasks 2 and 4. This allowed the team to focus on high-priority work right at the start of the project, optimizing the sprint without overloading the team 💪 It was a fun experiment to see how problem-solving techniques used in other industries can be applied in software development. And it’s a great reminder that optimization isn’t just about finding the best solution — it’s about making the process more efficient and getting the most value out of the resources at hand 🔧

From Static Grid to Dynamic Compass: How CROs Turn Volatility into Strategic Advantage In today’s fast‑moving world, new regulations, cyber threats, and market shocks appear daily, making the annual risk matrix obsolete. CROs must now master agility—sensing shifts, recalibrating priorities, and redeploying resources before the next wave hits. Why the Risk Matrix Needs Constant Touches A traditional risk matrix plots ' likelihood' vs. 'impact', putting risks into low, medium, or high boxes. But volatility blurs those lines—a “low‑impact” risk can turn critical after a supply‑chain hiccup or sudden regulation. The matrix isn’t broken; it just needs to be 'dynamic', not static. Rebalancing Priorities: A Practical Playbook for CROs 1. Continuous Horizon Scanning Use real‑time feeds (regulatory alerts, threat‑intelligence, market sentiment) and trigger an instant mini‑review when a signal hits a preset threshold, instead of waiting for quarterly reviews. 2. Fluid Risk Buckets Ditch rigid categories. Use resizable 'risk buckets' (e.g., a _Cyber‑Exposure Bucket_ for cyber‑fraud and third‑party breaches). When a ransomware surge hits, the bucket expands and pulls resources from lower‑priority areas instantly. 3. Scenario‑Based Prioritisation Run lightweight “what‑if” scenarios (sudden geopolitical tension, rapid tech adoption). Rank risks by _velocity_ (how fast they materialize) and impact, prioritizing high‑velocity, high‑impact threats for immediate action. 4. Resource‑Flex Model Maintain a flexible 'risk‑talent pool'. Cross‑train analysts in credit, operational, climate, and ESG risks, enabling quick re‑allocation without long hiring cycles. 5. Governance with Speed Simplify approvals: empower a small 'Risk Action Committee' (CRO, CFO, CIO, business head) to quickly approve risk‑limit or capital reallocations. The Human Angle Agility is about people, not just dashboards. CROs should foster a culture where anyone can flag risks, using quick‑pulse surveys or a dedicated Slack channel. Visible, swift action makes risk awareness spread organically. The Bottom Line In a volatile world, the CRO orchestrates a living risk ecosystem—embedding continuous scanning, fluid buckets, velocity‑based prioritization, flexible talent, and lean governance—to turn uncertainty into a competitive edge. The 2024 EY‑IIF Global Bank Risk Management Survey shows CROs are shifting to agile frameworks to tackle rising cyber, geopolitical, and operational risks. 75 % flag cyber as the top threat. Key moves: scenario planning, AI integration, financial‑risk recalibration, talent diversification, and organizational redesign. 49 % are prioritizing AI for risk detection and compliance; 63 % want digital‑acumen hires to complement tech‑driven resilience. This signals a move from reactive to anticipatory governance across global banks. 

Welcome to the first #Automation Alphabet on Linkedin. Today; I had trouble finding anything related to AMRs starting with the letter J… But here it is: J- like Job scheduling As always, starting with a definition: Job scheduling for Autonomous Mobile Robots (AMRs) involves allocating and prioritizing tasks or "jobs" that AMRs need to perform within a defined workspace, such as a warehouse, manufacturing floor, or healthcare facility. In a larger scope this means to define the perimeter of work that the robot is doing; that’s the groundwork. But there is also a second layer which can impact performance quite a lot. The distribution of tasks and orders/ transport jobs that have to be done day in day out… Who is deciding which robot takes what pallet? And when? How is prioritization done? Here are the main takeaways: 1. Task Assignment Job Allocation: Jobs can include specific tasks like picking up materials, delivering items to a workstation, or scanning inventory. Job scheduling decides which AMR is assigned to which task based on factors like proximity, availability, and battery level. Task Priority: Some tasks may have higher priority (e.g., delivering materials to an assembly line) and need to be scheduled first. Prioritization ensures time-sensitive tasks are completed within their required timeframes. Dynamic Task Allocation: In dynamic environments, task requirements may change due to delays, obstacles, or urgent requests. AMRs should be rescheduled dynamically to adapt to these conditions, maintaining an optimal workflow. 2. Optimization Goals Minimizing Travel Time: Reducing the distance each AMR needs to travel to complete its tasks, which saves energy and minimizes wear on the robot. Energy Efficiency: Balancing AMR tasks so that robots with low battery levels are scheduled for shorter tasks or sent for recharging if necessary. Collision Avoidance and Congestion Management: Scheduling needs to consider paths of other robots and any stationary obstacles, avoiding potential bottlenecks and collisions. 3. Multi-Robot Coordination Swarm Intelligence: In environments with multiple AMRs, coordination and synchronization are crucial. Job scheduling should minimize instances where AMRs are idle or waiting for other robots to complete tasks. 4. Resource Constraints Battery Management: The schedule should account for battery levels, ensuring that AMRs can complete assigned jobs before needing a recharge. Station and Docking Resource Availability: In cases where AMRs have designated docking or storage points, the job scheduling may also need to include queue management for these resources. Machine Learning for Optimization: Some AMR fleets use AI-based scheduling, which learns from historical data to predict optimal task sequences and improve scheduling over time. Watch how a #Swarm of 30 AGILOX ONE help BMW Group build 1300 cars per day in their Regensburg plant. Without the need of any #fleetmanager… #AMRs #AGVs

Seeker 2.0 Released After Seeker 1.0, 1.1, and 1.2, we now make our fourth release of Seeker! What is new: 1. Dynamic Variable Priorities. You can now hint the solver to look primarily at variables that, under the current assignment, appear most important to achieve improvements. The priority values are dynamic. You define how to compute the priorities dynamically! 2. Dynamic Variable Blocks. You can tell the solver, via a dynamic Boolean term, not to look at some variables under the current assignment but to focus on the unblocked variables only. 3. Nested Equation Systems. Just as you can infer the value of continuous variables by using nested linear programs, you can now determine their values by solving nested equation systems. No need to search for values that can be computed efficiently, right? 4. Delayed Evaluation. For expensive terms, such as nested LPs and EQs, you can tell the solver under what conditions a recomputation is warranted and when it may skip it, using a dynamic Boolean term. How to get started: pip install insideopt-seeker Or use our free version on Google Colab: pip install seekerdemo Example: https://lnkd.in/ev7TS3NA Documentation: https://lnkd.in/ezbK-xFv YouTube Tutorials: https://lnkd.in/e9EXzR6Z Happy Optimizing!

Post 19: Real-Time Cloud & DevOps Scenario Scenario: Your organization’s Kubernetes-based microservices faced a production outage due to a misconfigured pod overusing CPU and memory, causing resource starvation. As a DevOps engineer, your task is to prevent such issues and maintain system stability. Step-by-Step Solution: Set Resource Requests and Limits: Define resources.requests and resources.limits in pod specifications to control CPU and memory usage. Example: yaml Copy code resources: requests: memory: "500Mi" cpu: "250m" limits: memory: "1Gi" cpu: "500m" Enable Namespace Resource Quotas: Use ResourceQuota objects to restrict the total resource consumption within a namespace. Example: yaml Copy code apiVersion: v1 kind: ResourceQuota metadata: name: namespace-quota spec: hard: requests.cpu: "4" requests.memory: "8Gi" limits.cpu: "8" limits.memory: "16Gi" Leverage Horizontal Pod Autoscaler (HPA): Use HPA to scale pods dynamically based on CPU, memory, or custom metrics. Example: yaml Copy code apiVersion: autoscaling/v2 kind: HorizontalPodAutoscaler metadata: name: example-hpa spec: scaleTargetRef: apiVersion: apps/v1 kind: Deployment name: my-app minReplicas: 2 maxReplicas: 10 metrics: - type: Resource resource: name: cpu targetAverageUtilization: 80 Implement Pod Priority and Preemption: Assign priority classes to pods to ensure critical workloads get resources during contention. Example: yaml Copy code apiVersion: scheduling.k8s.io/v1 kind: PriorityClass metadata: name: high-priority value: 1000 globalDefault: false description: "Priority for critical workloads" Monitor and Analyze Resource Usage: Use tools like Prometheus, Grafana, or Kubernetes Metrics Server to monitor CPU and memory usage trends. Set up alerts for resource usage thresholds. Implement Node Affinity and Taints: Use node affinity and taints/tolerations to distribute workloads effectively across nodes, avoiding resource bottlenecks. Audit Configurations Regularly: Periodically review and update resource configurations for pods and namespaces. Conduct load tests to validate performance under different conditions. Enable Cluster Autoscaler: Use Cluster Autoscaler to add or remove nodes dynamically based on overall resource demand.This ensures sufficient capacity during peak loads. Outcome: Improved resource allocation prevents single pod failures from impacting other services. The system becomes more resilient and scales dynamically based on demand. 💬 How do you handle resource contention in your Kubernetes clusters? Let’s discuss strategies in the comments! ✅ Follow Thiruppathi Ayyavoo for daily real-time scenarios in Cloud and DevOps. Together, we learn and grow! #DevOps #Kubernetes #CloudComputing #ResourceManagement #Containers #HorizontalPodAutoscaler #RealTimeScenarios #CloudEngineering #LinkedInLearning #careerbytecode #thirucloud #linkedin #USA CareerByteCode

Not all shipments are created equal. Some deliveries are urgent, with customers counting the minutes until they arrive (... me with Amazon deliveries). Others have more flexibility. So, how do you decide what gets priority? Well, the final piece of the route planning and optimization puzzle this week are customer requirements and prioritization. It's about balancing what's urgent with what's efficient, ensuring that every shipment gets where it needs to be, exactly when it needs to be there. But here's the catch: prioritization doesn't work in isolation. It only works when paired with data-driven decision-making, dynamic routing, and optimized loads & vehicle utilization. Here's how it all connects: 📦 Customer requirements are the starting point. By understanding delivery windows and shipment urgency, planners can categorize shipments into priorities. Critical shipments can then be routed through the most time efficient paths while less-urgent shipments can balance time and cost. 📦 Data-driven decision making backs these priorities by insights. Real-time data helps planners adapt to disruptions that can impact high priority shipments, while historical data informs strategies to prevent delays in the first place. 📦 Dynamic routing allows flexibility when things don't go as planned. Need to reroute a time-sensitive shipment? No problem! Dynamic plans help keep high-priority deliveries on track without throwing hte rest of the schedule into chaos. 📦 Load optimization & vehicle utilization make sure that every truck is working efficiently, even when prioritization shifts the focus to critical shipments. Grouping similar loads and assigning the right vehicles create the flexibility to adjust without wasting resources. By aligning customer needs with operational strategies, companies can do more than just meet delivery windows. At its core, prioritization is about creating value by aligning customer needs with broader supply chain goals. Think about how meeting delivery windows impacts production, warehousing, inventory, other shipments... Every shipment is another step toward an interconnected, responsive, and resilient supply chain. #supplychain #logistics #transportation #routeplanning

🔍 ServiceNow Case #Study: #Automating Impact, Urgency, and Priority Fields Using #Data #Lookup #Definitions — No #Code Involved! Today, I worked on an #insightful #ServiceNow use case that aimed to #automatically populate the fields Impact, Urgency, and Priority based on the selected values of Sub-category and #Component — and I achieved this without writing a #single line of code! 🎯 Objective: To #dynamically set the Impact and Urgency fields based on predefined combinations of Sub-category and #Component, and derive the Priority field based on the resulting Impact and Urgency — all using native platform #capabilities. To implement this, I leveraged #Data Lookup #Definitions — a powerful yet #underutilized no-code feature in ServiceNow. 📘 What are Data Lookup Definitions? #Data Lookup Definitions in ServiceNow allow you to automatically populate or modify field values based on #conditions, ensuring consistency and reducing manual intervention. A typical Data Lookup rule: #Matches certain input field values (e.g., Sub-category = Software, Component = Email) #Searches a #matcher table for a corresponding rule Automatically sets #target fields (e.g., Impact, Urgency, Priority) based on the rule ⚙️ Step-by-Step Implementation: ✅ Step 1: Created a custom table named "#Business #Support", and designed the ticket form using Form #Builder, Form #Design, and Form #Layout. ✅ Step 2: Created another custom table called "#BS #Priority #Lookup", extending it from the Data Lookup #Matcher #Rules table (sys_data_lookup_rule). 🔎 This table holds all the #logic to #evaluate conditions and apply field values based on #matches. ✅ Step 3: On the #BS #Priority #Lookup table, I configured #form fields to capture the Sub-category, Component, Impact, Urgency, and Priority #mappings. ✅ Step 4: Navigated to the Data Lookup #Definitions module and created a new #definition. ✅ Step 5: Set the following: #Source Table: Business Support #Matcher Table: BS Priority Lookup #Matcher Fields: Sub-category & Component #Setter Fields: Impact, Urgency & Priority 🔁 How It All Comes Together: When a user submits a form in the #Business #Support table: The platform checks for matching rules in the #BS #Priority #Lookup table If an exact match is found on Sub-category and Component, it auto-sets the Impact, Urgency, and Priority fields — streamlining #triage without custom #scripting! 💡 This approach not only #promotes #scalability and #consistency but also reduces maintenance overhead by avoiding #scripted #logic. If you're exploring ways to deliver #smarter #workflows in #ServiceNow without code, I highly recommend #experimenting with #Data #Lookup #Definitions. #ServiceNow #NoCode #Automation #WorkflowDesign #PlatformConfiguration #ServiceManagement #ProcessImprovement #DataLookup #ImpactUrgencyPriority #LinkedInLearning

A high-octane business environment with double-digit growth objectives presents a challenge of handling the "problem of plenty." Strategic prioritization, focus, and discipline are crucial in navigating this scenario effectively. Prioritization Frameworks: 1.Eisenhower Matrix to categorize initiatives based on urgency and importance. 2.MoSCoW Method to label initiatives as Must-Haves, Should-Haves, Could-Haves, and Won't-Haves. 3.Kano Model to classify initiatives based on customer delight, meeting expectations, or fulfilling basic needs. Focus on High-Impact Initiatives: 1. Identify Key Performance Indicators (KPIs) to concentrate on initiatives with significant impact. 2. Conduct Cost-Benefit Analysis to evaluate ROI and resource requirements. 3. Ensure strategic alignment by assessing initiatives against the organization's goals. Resource Allocation and Optimization: 1. Align resource allocation with initiative priorities through Resource Capacity Planning. 2. Manage initiatives as a portfolio with Portfolio Management for effective resource utilization. 3. Implement Agile Methodologies for flexibility and rapid response to changing priorities. Governance and Decision-Making: 1. Define clear decision-making criteria for evaluating and prioritizing initiatives. 2. Establish a governance structure to ensure strategic alignment and resource allocation. 3. Regularly review progress, adjust priorities, and reallocate resources as needed. Cultural and Behavioral Aspects: 1. Foster a culture that values prioritization, focus, and discipline. 2. Encourage open communication for understanding and alignment of priorities. 3. Recognize and reward effective prioritization and focus among employees. By incorporating these strategies, businesses can effectively address the challenges posed by a dynamic business environment, driving growth while maintaining focus and discipline. #DProblemofPlenty Manjushree Technopack Limited (MTL) Gladys Caligagan