Optimizing Solution Delivery

The easiest part of building #optimization and #decisionintelligence solutions is writing the code. Yet, I've found few references dealing with the more critical parts of successfully delivering the right solution. Here's my step-by-step approach to increasing the likelihood of delivering a solution with high business impact. 1) Understand the business process: Expanding your view from the problem presented to the process in which it is embedded allows for more holistic solutions and de-risks solving the wrong problem. 2) Interviews with business users and stakeholders: Understanding how users perceive their business process gives a better picture of the communication flow. This is important for change management as you roll out your solution. In addition, it provides a first glimpse to assessing the client's "tech maturity" which influences how you architect your solution. 3) Present an initial solution in plain English: Write a document with a clear problem statement, a high-level description of the solution, and the expected metrics improvements without technical jargon. This serves a double function as an exercise to have mental clarity and a means to #communicate and align with stakeholders. 4) Build a "scrappy" prototype and get it to stakeholders: This is one of the best ways to keep stakeholders engaged, validate that it's on the right path, and streamline change management. The prototype should include the solution, a method to evaluate the relevant metrics, and an interface for stakeholders to interact with your solution. 5) Build a metrics tracking mechanism: Create a dashboard that will be used to review the latest performance metrics of interest so that you can clearly build the story of how your solution is improving them over time as you iterate. 6) Build a CI/CD pipeline: After the prototype's initial validation, build a pipeline that allows you to ship new releases quickly to stakeholders. Establish cadenced checkpoints and demos to get feedback and review metrics. This is an important part of your change management. 7) Pilot: Once the metric improvements have been achieved, run a pilot where you follow how your solution is used as part of the business process. Make any final necessary tweaks to secure adoption. 8) Documenting and closing: Once adoption is satisfactory, close out the project by properly documenting your artifacts for your stakeholders. Include a section identifying other potential improvements to the process and an estimate of their impact for future work. Successful projects go far beyond models and algorithms, they ensure business impact and adoption. This is how we'll make #decisionintelligence the most widely adopted #AI in business. What steps would you also include?

We solved half the problem & thought we bridged the gap. Ever worked on a solution that looked perfect on paper… but ended up creating more problems than it solved? That’s exactly what happened when I was called in to review a telehealth solution. It was well-designed, checked all the cybersecurity boxes, & allowed patients to consult doctors remotely. The project requirement was clear: enable remote consultations. And the solution delivered exactly that. But here’s the thing: While healthcare systems often operate in silos, patients experience their care as one continuous journey. And this solution missed critical parts of that journey: 🔸 No easy way to book follow-ups. Patients had to call, leading to missed care. 🔸 Medication collection still required hours of travel, making the platform’s convenience meaningless. 🔸 Administrative staff were overloaded, causing delays in care coordination. We solved one problem & unintentionally created three more. The solution was designed for the system’s convenience, not the patient’s journey. To shift the perspective, we expanded the conversation to include voices we hadn’t considered: 🔸 Pharmacists: To integrate medication delivery into the process 🔸 Community Health Workers: To provide local, hands-on support 🔸 Family Caregivers: To highlight logistical & emotional challenges at home 🔸 IT Teams: To automate follow-ups & reduce administrative burden 🔸 Local Transport Providers: To enable last-mile delivery of medications With these insights, we redesigned the solution into a comprehensive care experience: ✅ Patients could book follow-ups easily & get automated reminders ✅ Medications were delivered directly to their homes ✅ Caregivers & community workers ensured patients didn’t fall through the cracks I later learned that: 🔸 Missed follow-ups dropped by 40%. 🔸 Medication adherence & health outcomes improved significantly. The redesigned platform didn’t just connect patients to doctors, it completed the care journey. Next time you’re working on a solution, consider these points: 1️⃣ Patients see one journey While systems operate in silos, patients experience care as a unified process. 2️⃣ Identify all stakeholders Both direct & indirect voices like caregivers, pharmacists & community workers, are essential to closing gaps. 3️⃣ Design for continuity Address every touchpoint in the patient’s journey, ensuring nothing falls through the cracks. Have you worked on solutions where overlooked stakeholders made all the difference? What’s one gap you discovered that changed everything? #DigitalHealth #Innovation #HealthcareTransformation #PatientExperience #Collaboration 💡This post is part of 'Rethinking Digital Health Innovation' (RDHI), empowering professionals to transform digital health beyond IT and AI myths. 💡Find the ongoing series and resources on our companion website (URL in comments). 💡 Repost if this message resonates with you!

A client came to me spending $47,000 monthly on shipping costs for their e-commerce business. Six months later? They cut that down to $31,000. Same volume. Same delivery standards. Different approach. The problem wasn't their carrier rates or delivery zones. It was their packaging strategy eating into profits through dimensional weight charges. Here's what we discovered during our initial audit: → 67% of their shipments were being charged based on dimensional weight, not actual weight → Their standard boxes left 40% empty space on average → Custom packaging was costing 3x more than optimized alternatives We implemented a three-phase packaging optimization strategy: Phase 1: Right-sized their box inventory from 12 different sizes to 6 strategic dimensions that minimized wasted space while maintaining brand integrity through custom printing. Phase 2: Introduced flexible packaging solutions for soft goods, reducing dimensional weight by up to 60% for apparel items. Phase 3: Streamlined operations with automated packaging selection based on product dimensions and carrier requirements. The results after 6 months: → 34% reduction in total shipping costs → 28% improvement in packaging efficiency → Zero compromise on brand presentation → Enhanced customer unboxing experience This wasn't just about cutting costs. It was about optimizing the entire supply chain to work smarter, not harder. State-of-the-art facilities and strategic locations matter, but without proper packaging optimization, you're leaving money on the table with every shipment. What's your biggest packaging challenge right now?

Reducing Delivery Time in Quick Commerce: A Data-Driven Approach at Qcommerce Background In the quick commerce industry, speed is a critical success factor. Customers expect deliveries in under 30 minutes, and any delay impacts both customer satisfaction and business performance. Company was facing increasing complaints about late deliveries. The assumption was that a shortage of riders was causing the delays. However, a deeper analysis revealed multiple underlying inefficiencies. Identifying the Problem To find the root cause, we analyzed data across multiple factors: Delivery times by location, order time, and restaurant type Rider movement and route efficiency Restaurant order preparation times Traffic patterns in high-density areas Using SQL, we extracted delivery logs and segmented the data into meaningful insights. Python and Google Maps API helped map rider movement and identify delays in different locations. A Power BI dashboard provided real-time visibility into key metrics. Key Findings 1. Traffic congestion was a major factor – Certain routes had significantly higher travel times during peak hours. 2. Inefficient rider dispatching – Riders were often assigned from distant locations instead of from optimal pickup points. 3. Inconsistent restaurant prep times – Some restaurants frequently took longer than expected to prepare orders. Implementing the Solution Based on these insights, the team implemented three key improvements: Optimized Rider Allocation – Instead of assigning the nearest available rider, we introduced a zone-based dispatch system to ensure that riders were positioned closer to high-order areas. Peak-Hour Route Optimization – A Python-based dynamic routing model suggested faster routes in real time, reducing travel delays. Restaurant Coordination & Performance Benchmarking – SQL-based analytics helped track restaurant prep times, allowing for targeted improvement strategies. Results & Business Impact 15% reduction in average delivery time 20% improvement in on-time deliveries Higher customer retention and order frequency due to improved service reliability Key Takeaways 1. Data should challenge assumptions – The initial belief was that rider shortages were the issue, but analytics revealed multiple inefficiencies. 2. Granular segmentation matters – Breaking data into smaller categories (e.g., delivery zones, peak vs. off-peak hours) helps identify precise problems. 3. Real-time decision-making is crucial – Dynamic routing and performance tracking led to measurable improvements. Quick commerce businesses thrive on speed and efficiency. Leveraging data analytics can transform operational processes and enhance customer experience. How does your organization optimize last-mile delivery?

Hello #datafam I'm glad to share my latest project, I built a comprehensive analytics report for a third-party logistics (3PL) company to help them track performance, optimize delivery processes, and identify inefficiencies. The report provides actionable insights across compliance, delivery operations, and order management—key areas that impact customer satisfaction and operational efficiency. Key Features of the Report 1. Compliance Breakdown: The Compliance Dashboard highlights critical metrics that determine whether the ordering company or the delivery partner causes delays. Key sections include: 🟢 Checkout to Delivery Time Compliance – Measures adherence to SLA agreements, with a percentage breakdown of on-time vs. delayed deliveries. 🟢 On-Time Delivery Compliance tracks delivery punctuality, helping identify trends in delays. 🟢 Checkout to Assignment Compliance monitors how quickly orders are assigned to drivers, ensuring efficiency in the initial stages. 🟢 Pickup & Pack Compliance evaluates warehouse performance in preparing orders for dispatch. By isolating where delays occur (e.g., during order assignment, pickup, or last-mile delivery), 3PL companies can hold the right stakeholders accountable—whether it’s the retailer, warehouse, or courier service. 2. Delivery Performance: The Delivery Dashboard provides granular insights into driver performance, including: 🟢 Total Trips vs. Deliveries Completed measures productivity. 🟢 Online Delivery Rate tracks digital vs. manual delivery confirmations. 🟢 Distance Covered helps optimize route planning. By analyzing driver metrics, 3PL companies can reward high-performing couriers, retrain underperforming ones, and optimize delivery routes to reduce costs and improve speed. 3. Order Report: The Order Report gives a detailed breakdown of each item’s status, including: 🟢 Delivery Status (Pending, Shipped, Delivered) 🟢 Product Category & Quantity helps in inventory forecasting. 🟢 Customer & Courier Details ensures accountability at every stage. Retailers and logistics managers can quickly identify stuck orders, predict delays, and proactively communicate with customers, enhancing transparency and trust. How This Report Will Help 3PL Companies 🔶 Reduces Delivery Delays pinpointing bottlenecks in real time. 🔶 Improves SLA Compliance tracking which partners (retailers or couriers) are causing delays. 🔶 Optimizes Workforce Efficiency by identifying top-performing drivers and warehouse staff. 🔶 Enhances Customer Experience with real-time order tracking and proactive issue resolution. Explore the live report here: https://lnkd.in/deB-x7S5 Data-driven logistics is the future. With the right analytics, 3PL companies can minimize costs, maximize efficiency, and keep customers happy. #DataAnalytics #BusinessIntelligence #Logistics #3PL #SupplyChain #BI #GoBolt

When optimizing delivery operations across industries, from ride-sharing to logistics, there’s a sweet spot. Too efficient, you risk customer satisfaction. Too conservative, operations are inefficient. Here’s how to find it: 1. Define your operational boundaries or SLAs a) Order batching Whether you're running a ride-hailing service, food delivery platform or delivering parcels, order batching is crucial. First, you need to figure out the parameters that make the most sense for your business: Response time (e.g., 20 seconds for ride-hailing ride confirmation) Ride time (e.g., 25 min ride becoming 30 min for shared rides) Load capacity limits (e.g., max 50kg per driver) Customer service expectations (e.g., takeout delivery within 30 minutes) Figuring out API performance expectations helps you balance efficiency and good service. Warning: Beware of solutions that show fantastic efficiency metrics and huge cost reductions, but lead to unhappy customers or drivers. e.g. loading a driver with 100kg or 1hr delivery times for pizzas. b) Cost savings Calculating the right customer price point needs to be weighed against time and customer/driver preferences.   Example 1: A shared ride can take 25 minutes at a 20% discount. But if the ride takes 35 minutes, a rider won’t accept those conditions, even with a discount. Example 2: A premium rider might never want to ride share. Example 3: You might want to reward loyal, experienced drivers with 10% better orders/rides, or ensuring they’re assigned customers in familiar neighbourhoods. Surprisingly, a lot of companies haven’t figured out these boundaries, meaning there are often huge blind spots when it comes to the customer experience or employee satisfaction. 2. Know what you want to optimize and what tradeoffs are okay to make. Understand your business conditions and your boundary constraints and determine what you’re really optimizing for. e.g. How fast you assign a driver vs. optimization potential e.g. Cost reduction vs. service quality You might want to assign a driver instantly, but not batch riders together. On the flip side, maximizing deliveries or number of riders per car might require 5-10 seconds more time to batch ride requests and lower costs, but if the rider gets a 10% discount, it’s worth the wait. 3. Use a flexible system and can handle all your constraints Each business is different. Delivering pizza is different from delivering ice cream. Some companies might be focused on rapid growth, with others on reducing costs or maximizing on-time deliveries for premium customers. Choosing a flexible system lets you adapt according to changing needs and priorities. The system should be: - Configurable so you can run different simulations and test different tradeoff scenarios  - Stable so you can run your business-critical operations reliably - Easy to integrate your constraint and tradeoff parameters within your existing system

We improved our last-mile efficiency by 40% with a strategy Amazon used to make $4.1 billion in a quarter. As logistics companies race to deliver faster, they're often bleeding money where it hurts most, which is the last mile (the final leg of a delivery from the warehouse to the customer's doorstep). This final stretch from warehouse to doorstep makes up to 53% of total shipping costs. At SHIPZIP, we took a counterintuitive approach. Instead of chasing speed, we obsessively tracked one number: 👉 Cost Per Shipment (CPS) It is the total expense of getting a package from our warehouse to the customer's doorstep. This is how the industry giants are focusing on this metric: 📍 Amazon They pivoted from speed obsession to neighborhood batching, dramatically cutting delivery costs. This strategic shift boosted their North America operating income to $6.5 billion in Q4 2023, a staggering $6.7 billion increase year-over-year, yielding a 6.1% operating margin. Their focus on cost-efficiency over pure speed transformed their balance sheet. 📍 Flipkart They slashed CPS by strategically placing distribution centers closer to customers. Through their logistics arm, Ekart, they now handle 10 million monthly shipments across 3,800+ pin codes in India. This hub placement strategy simultaneously reduced rental costs and improved delivery predictability. 📍 Delhivery They implemented AI-driven route optimization that minimizes both distance and time while maximizing deliveries per trip. Their smart algorithms evaluate traffic patterns, package dimensions, and delivery windows in real-time. These technologies have significantly reduced fuel consumption and operational costs while keeping deliveries on schedule. Here's how we cut our cost per shipment: → We analyzed our Tier 1 delivery routes and found they prioritized speed over cost-efficiency. So we regrouped deliveries by neighborhood and reduced crosstown trips. This helped us to optimize CPS and cut fuel costs by 22%. → We found that smaller vans, though carrying fewer packages, could weave through traffic more easily, allowing our drivers to make more deliveries in less time. → Most importantly, we found that compromising slightly on delivery windows dramatically improved profits. Rushing a single package to meet a tight deadline often costs 3X more than batching it with others. Interestingly, after we optimized for cost per shipment, our customers noticed the change. It was not because we told them, but because deliveries became more predictable and reliable, with fewer missed attempts and damaged packages. What's your biggest frustration with last-mile delivery services? #LastMileOptimization #LogisticsStrategy #CostPerShipment

Why settle for watching delays when you can prevent them with AI? Most teams track deliveries. Few optimize them. Here’s the difference: - Monitoring tools show you what’s happening. - Optimization platforms shape what happens next. Tracking delays feels reactive; you’re stuck watching problems unfold. AI-driven optimization flips the script. It predicts challenges before they occur and adjusts routes dynamically to keep deliveries on track. Imagine this: - A system that reroutes drivers in real-time to avoid traffic jams. - Algorithms that forecast delays and automatically recalibrate delivery schedules. - Insights that reduce costs, improve efficiency, and ensure on-time performance at scale. Logistics teams spend less time firefighting and more time strategizing. This leads to: - 99% on-time delivery rates. - 28% reduction in fleet costs. - Scalability without sacrificing performance. Why settle for the grind of tracking when you can embrace the power of optimization? If you’re ready to move from reactive to proactive, let’s talk.