Your problem has a name. Find it. Read who solved it.
OR's living SMB directory — find your operational problem by name, read academic case studies. We don't sell, we don't recommend providers, we keep a directory.
What is OptDir?
An open directory of operational problems. We are not a vendor, not a consultancy.
A reference compiled from academic case studies and SMB experience. You read, compare, and decide what to try yourself.
1. Name your problem
The pain in your shop floor, warehouse or service desk has a name in the academic literature. Find it first.
2. Read the stories
What companies with the same problem tried, what they got, what they regretted — distilled from academic case studies.
3. Make your own call
Start with your own data, learn the categories, then choose a vendor — the call stays yours. No selling here.
Featured problems
SMB-sized, high-leverage examples.
How Much of Each Product Should I Make for the Most Profit?
You run a 5-50-worker shop making 3-15 products. The same raw material, the same machines and the same workers are shared across products. Each week sales says 'we can sell 200 more of this', production says 'but that machine is full', accounting says 'product A has the highest margin, push that one'. Each product has a different margin, a different machine-hour consumption, a different raw-material usage and a different demand ceiling. Decide by gut and you usually load the product with the highest sale price — yet the product that **eats the least bottleneck-machine hour** can be more profitable. Take this call by intuition and 10-25% of the monthly margin you could earn from the same capacity stays on the table.
A New Part Order Arrives — How Do I Get the Optimal Operation Sequence + Machine Choice + Setup?
If you are an SMB CNC manufacturer, tooling shop or engineering workshop producing 50-500 different parts, every new order puts the same decision in front of you: you take the customer's CAD model and have to work out on which machine, in which order, with which tool and fixture, and in how many setups the part will be made. If you simply write down the first feasible sequence that comes to mind, setup time grows 3-5x and parts that miss tolerance are reworked; finding the right sequence means comparing several alternative routings for the same part. When the decision lives only in one engineer's head, the similar-part memory walks out of the door when that engineer leaves, and routings of older parts are not refreshed when a new machine is bought. This page is for production-engineering teams who want to make the operation sequence and the machine assignment written and comparable as the bridge from design to make.
Backward from the Customer Order — Which Raw Material, When, and How Much Should I Order?
For mid-size manufacturing SMBs running 50-500 end items and 200-2,000 raw materials and sub-components (automotive Tier-2, white-goods component shops, furniture and assembly, machinery). With each product's parts list (BOM) and each part's procurement or production lead time on file, the question is: a customer orders 200 units of product X with a fixed delivery week — which raw materials in what quantity must be ordered which week, and which sub-parts must enter assembly when? Material requirements planning (academic name MRP) answers this question backwards from the delivery date and is the core logic underneath every ERP. Done on paper or by 'my supplier always takes 3 weeks' rules of thumb, you either run out and stop the line, or burn cash on excess stock.
Dozens of SKUs from the Same Supplier — At What Frequency Do I Order Each So Trucks and Stock Cost Are Minimum Together?
This page is for an SMB wholesaler, importer or manufacturer pulling 50-500 SKUs from the same supplier — typically across 3-15 main suppliers. The weekly question is the same: from this supplier, which products should ship today and which can wait until next week? If every product triggers its own order, the same supplier ends up sending three trucks and three customs filings a week; one truck, one customs filing and one setup cost can cover all of it if the groups are right. Manual planning falls apart past 50 SKUs: some weeks a half-empty truck, others three back-to-back orders — fixed costs come back as 5-15% of unit product cost.
Fixed Budget, Many Candidate Projects — Which Subset Should I Pick So That Total Return Is Maximised?
A mid-size holding or SMB investment committee faces 50-200 candidate projects every year (factory capacity, new line, warehouse, IT modernisation, digital transformation) against a fixed annual budget (50-500M TRY): which subset gets picked so the budget is respected and total return (NPV) is maximised? The intuitive 'sort by NPV / investment ratio, take from the top' rule misses small but high-return projects that fit the last 5-10% of the budget — empirically it deviates from the optimum by 5-15%, and with multi-dimensional constraints (budget + labour + machine-hours) the gap widens to 10-25%. The same structure recurs in monthly marketing campaign selection, capacity-bounded cargo loading, and supplier subset selection. The widespread committee belief 'no mathematical optimum exists, we just pick by judgement' is wrong — an optimal solution for 200-1000 candidates is delivered in minutes.
From One Node to Another — How Do I Compute the Shortest Path on a Weighted Graph?
For SMBs that need to compute the fastest or shortest route between two points: 10-50-vehicle field-service teams (plumbing, electrical, appliance repair), urban courier/parcel operations, or dispatch centres coordinating emergency response. Every day brings hundreds of 'how do I get from A to B fastest right now' questions; the answer shifts with traffic, road closures and vehicle type. A wrong route costs the technician one or two jobs missed for the day, the courier a late delivery, and the firm a customer. Manual or by-eye routing typically leaves 20-60 wasted minutes per vehicle per day on the table compared with a network-aware route calculation.
Recently added
New problems, stories and hard lessons appear here as published.
How Much of Each Product Should I Make for the Most Profit?
You run a 5-50-worker shop making 3-15 products. The same raw material, the same machines and the same workers are shared across products. Each week sales says 'we can sell 200 more of this', production says 'but that machine is full', accounting says 'product A has the highest margin, push that one'. Each product has a different margin, a different machine-hour consumption, a different raw-material usage and a different demand ceiling. Decide by gut and you usually load the product with the highest sale price — yet the product that **eats the least bottleneck-machine hour** can be more profitable. Take this call by intuition and 10-25% of the monthly margin you could earn from the same capacity stays on the table.
A New Part Order Arrives — How Do I Get the Optimal Operation Sequence + Machine Choice + Setup?
If you are an SMB CNC manufacturer, tooling shop or engineering workshop producing 50-500 different parts, every new order puts the same decision in front of you: you take the customer's CAD model and have to work out on which machine, in which order, with which tool and fixture, and in how many setups the part will be made. If you simply write down the first feasible sequence that comes to mind, setup time grows 3-5x and parts that miss tolerance are reworked; finding the right sequence means comparing several alternative routings for the same part. When the decision lives only in one engineer's head, the similar-part memory walks out of the door when that engineer leaves, and routings of older parts are not refreshed when a new machine is bought. This page is for production-engineering teams who want to make the operation sequence and the machine assignment written and comparable as the bridge from design to make.
Backward from the Customer Order — Which Raw Material, When, and How Much Should I Order?
For mid-size manufacturing SMBs running 50-500 end items and 200-2,000 raw materials and sub-components (automotive Tier-2, white-goods component shops, furniture and assembly, machinery). With each product's parts list (BOM) and each part's procurement or production lead time on file, the question is: a customer orders 200 units of product X with a fixed delivery week — which raw materials in what quantity must be ordered which week, and which sub-parts must enter assembly when? Material requirements planning (academic name MRP) answers this question backwards from the delivery date and is the core logic underneath every ERP. Done on paper or by 'my supplier always takes 3 weeks' rules of thumb, you either run out and stop the line, or burn cash on excess stock.
Dozens of SKUs from the Same Supplier — At What Frequency Do I Order Each So Trucks and Stock Cost Are Minimum Together?
This page is for an SMB wholesaler, importer or manufacturer pulling 50-500 SKUs from the same supplier — typically across 3-15 main suppliers. The weekly question is the same: from this supplier, which products should ship today and which can wait until next week? If every product triggers its own order, the same supplier ends up sending three trucks and three customs filings a week; one truck, one customs filing and one setup cost can cover all of it if the groups are right. Manual planning falls apart past 50 SKUs: some weeks a half-empty truck, others three back-to-back orders — fixed costs come back as 5-15% of unit product cost.
Fixed Budget, Many Candidate Projects — Which Subset Should I Pick So That Total Return Is Maximised?
A mid-size holding or SMB investment committee faces 50-200 candidate projects every year (factory capacity, new line, warehouse, IT modernisation, digital transformation) against a fixed annual budget (50-500M TRY): which subset gets picked so the budget is respected and total return (NPV) is maximised? The intuitive 'sort by NPV / investment ratio, take from the top' rule misses small but high-return projects that fit the last 5-10% of the budget — empirically it deviates from the optimum by 5-15%, and with multi-dimensional constraints (budget + labour + machine-hours) the gap widens to 10-25%. The same structure recurs in monthly marketing campaign selection, capacity-bounded cargo loading, and supplier subset selection. The widespread committee belief 'no mathematical optimum exists, we just pick by judgement' is wrong — an optimal solution for 200-1000 candidates is delivered in minutes.
From One Node to Another — How Do I Compute the Shortest Path on a Weighted Graph?
For SMBs that need to compute the fastest or shortest route between two points: 10-50-vehicle field-service teams (plumbing, electrical, appliance repair), urban courier/parcel operations, or dispatch centres coordinating emergency response. Every day brings hundreds of 'how do I get from A to B fastest right now' questions; the answer shifts with traffic, road closures and vehicle type. A wrong route costs the technician one or two jobs missed for the day, the courier a late delivery, and the firm a customer. Manual or by-eye routing typically leaves 20-60 wasted minutes per vehicle per day on the table compared with a network-aware route calculation.
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