What Is AI in Packaging Logistics? A Smart Guide

What Is AI in Packaging Logistics? A Surprising Starting Point

what is ai in packaging logistics? I’ve heard that question in warehouse offices in Dallas, during supplier calls from Shenzhen, and once at a folding-carton plant in Milwaukee where the production manager had just spent 47 minutes matching cartons to orders by hand. I remember standing near a row of RSCs and watching a stack of brown corrugated boxes get remeasured three times because nobody trusted the original spec sheet, which, honestly, felt like watching a fire drill in slow motion. The short answer: it is software that learns from shipment and packaging data, then helps teams make better choices about packing, routing, inventory, labeling, and delivery timing, often with an initial pilot running for 3 to 6 weeks before broader rollout.

The longer answer is more interesting. what is ai in packaging logistics really means using pattern recognition to reduce the guesswork that eats up hours every week. A team may be choosing between a 9" x 6" x 4" mailer made from 350gsm C1S artboard, a 12" x 10" x 8" carton, and a double-wall corrugated shipper with BC flute. AI can compare product dimensions, transit damage history, carrier rates, and dimensional weight charges in seconds. Humans can do that too, but not at scale, and not with 3,000 SKUs and 14 shipping zones on the table. I have seen people try, and by hour two the spreadsheet starts looking like it had a fight with a stapler.

Honestly, I think this matters now because packaging operations are under pressure from three directions at once: tighter margins, faster delivery expectations, and SKU sprawl. One cosmetics client I worked with in New Jersey had 1,240 active SKUs and 38 packaging configurations, including tuck-end cartons, mailer boxes, and kraft corrugated shippers. Their team was spending nearly two hours a day just deciding which carton family to use for mixed-order bundles. what is ai in packaging logistics in that setting? It is a decision-support layer that trims repetitive work and reduces the odds of a bad packing choice.

It also touches the parts of operations people notice immediately: carton selection, damage prevention, late-shipment reduction, and even package branding. A smarter packing recommendation can protect a bottle, reduce void fill, and still preserve the look of branded packaging. That combination matters in retail packaging, e-commerce, and subscription boxes where the unboxing experience is part of the product itself. I’ve watched marketing teams fall in love with a beautiful sleeve design, then discover it adds twelve cents and a curse word to the pack line (not always a literal curse word, but close). In one Chicago project, that twelve-cent increase on 50,000 monthly orders added $6,000 to packaging spend before freight was even counted.

“We thought we had a shipping problem. Turned out we had a carton-selection problem, a labeling problem, and a data problem.” That was a director of operations in Columbus, Ohio after a 6-week audit. He was right. what is ai in packaging logistics starts with better decisions, but it only works if the underlying data isn’t a mess.

I’m going to keep this practical. No hype. No sci-fi. Just the mechanics, costs, tradeoffs, and the places where AI helps, plus the places where it can absolutely mislead you if you trust it blindly. If a system cannot show a carton recommendation, a transit-risk score, and a per-order savings estimate in dollars and cents, it is usually just a dashboard wearing a lab coat.

How AI in Packaging Logistics Works Behind the Scenes

what is ai in packaging logistics from a technical standpoint? It is usually a mix of machine learning, optimization rules, and forecasting models that ingest historical and live data. The system studies shipment records, packaging dimensions, transit times, carrier performance, return reasons, and even weather patterns in some cases. If you’ve ever watched a planner juggle freight tables, carton libraries, and inventory constraints, you already understand the problem AI is trying to solve. The software just does it without needing coffee, which is frankly rude. In a typical deployment, model updates happen nightly, while exception scoring may run in near real time for orders under 2 minutes old.

Here’s the basic data stack. First comes order history: what shipped, when it shipped, where it went, and whether it arrived on time. Then packaging data: carton dimensions, material specs, void fill usage, weight limits, and product fragility. Then logistics data: shipping rates, fuel surcharges, service levels, return rates, and carrier performance. what is ai in packaging logistics if not a way to let those datasets talk to each other instead of living in five spreadsheets and one harried manager’s memory? A clean setup often includes CSV imports from a WMS in Louisville, API pulls from a parcel platform in Atlanta, and a packaging spec library maintained by procurement in Toronto.

Machine learning sounds fancy, but in plain English it just means the system finds patterns from past shipments and improves its recommendations over time. If the model learns that a 14" x 10" x 6" carton consistently creates 18% more dimensional weight cost than a 12" x 10" x 6" carton for a certain SKU cluster, it starts favoring the smaller option. If it sees that fragile items packed with 2" of void fill have a 3.7% lower damage rate than those with 1", it can recommend the safer setup. what is ai in packaging logistics is often that simple, even if the software dashboard looks intimidating. In one pilot I reviewed in Phoenix, changing the recommendation engine lowered average parcel weight by 0.4 pounds and cut DIM charges by $0.27 per shipment across 18,000 monthly orders.

In the workflow, AI can sit at several points:

• Forecasting demand so you know how many cartons, labels, and inserts to stock for the next 30 to 90 days.
• Cartonization so the system recommends the best box for each order profile, including exact internal dimensions.
• Carrier selection so it assigns the most cost-effective service that still meets the delivery promise, such as 2-day air versus zone-skipping ground.
• Delay prediction so teams can intervene before a shipment misses a cutoff, particularly during peak weeks in November and December.
• Returns analysis so packaging failures can be traced back to the exact pack-out pattern and station ID.

Let me give you a simple example. A DTC skincare brand ships a 3-item order: one glass serum bottle, one 4-oz moisturizer, and one microfiber pouch. The system checks item dimensions, fragility flags, and the destination ZIP code. It sees that a 10" x 8" x 4" mailer with molded pulp inserts keeps the package under 2 pounds, while a bigger carton pushes it into a higher rate tier. It also notices that the glass bottle has a 1.8% breakage rate in one carton family and 0.4% in another. what is ai in packaging logistics here is a recommendation engine that weighs both cost and protection, then suggests the lower-risk setup, often within 1 to 2 seconds at the packing station.

There’s also a big distinction between rule-based automation and AI-driven decision-making. Rule-based systems follow fixed instructions: “If weight is under 2 pounds, use carton A.” That works until an unusual order comes in. AI can adapt by looking at multiple variables together: product shape, damage history, zone, carrier delay risk, and seasonality. In my experience, rule-based logic is faster to launch. AI is better at uncovering the weird exceptions that quietly cost money, especially in operations handling 500 to 5,000 orders per day.

At one corrugated converter I visited near Atlanta, the pack line supervisor told me they had 19 carton sizes for what should have been 11. “Nobody wants to be the person who chose the wrong box,” she said. That’s exactly where what is ai in packaging logistics starts paying attention: not just to the average shipment, but to the cases everyone is afraid to standardize.

What Is AI in Packaging Logistics and Why Does It Matter for Modern Operations?

what is ai in packaging logistics matters because packaging is no longer a simple box-and-label exercise. It sits at the intersection of fulfillment speed, freight spend, product protection, and customer experience. A well-tuned system can reduce unnecessary material usage, keep shipping promises more accurate, and help teams Choose the Right shipper before mistakes become returns. In many operations, that means fewer reworks, lower chargebacks, and less time spent arguing over which carton should have gone out the door in the first place.

That practical value shows up in more than one place. If a system can guide cartonization, forecast packaging demand, and improve carrier selection, then it is not just automating tasks; it is shaping the economics of the order. what is ai in packaging logistics is also a useful answer for teams trying to balance sustainable packaging goals with cost control, because better dimensional fit often means less corrugated, less void fill, and fewer unnecessary shipments in oversized cartons.

For brands shipping fragile, premium, or heavily customized items, the stakes are even higher. A small change in packaging design can affect breakage rates, unboxing quality, and freight classifications all at once. That is why many teams pair AI with packaging engineering, rather than treating it as a standalone shipping tool. If the carton library is poorly structured, the model can only make educated guesses; if the packaging data is clean, the system can make recommendations that feel unusually human in their practicality.

Key Factors That Shape Results: Data, Accuracy, and Integration

If you ask me what is ai in packaging logistics really dependent on, I’d say data quality first, software second. Bad data produces confident nonsense. I’ve seen a warehouse file where the same SKU had three different dimensions: 8 x 6 x 4, 8.2 x 6 x 4.1, and 10 x 6 x 4 because someone measured the outer carton instead of the product. The model didn’t fail. The inputs did. That part always makes me wince, because you can almost hear the dollars leaking out through the gaps. A single quarter-inch error can change a carton selection and add 0.2 pounds to billable weight.

Accuracy matters because packaging decisions are physical, not abstract. A 1/4-inch error can change fit, damage risk, or dimensional weight. If an AI system thinks a bottle is 6.9 inches tall when it is really 7.4 inches with a pump, it may recommend a carton that looks fine on screen but crushes in transit. what is ai in packaging logistics only works when dimensions, weights, and material specs are consistent across your system of record. That means the spec sheet, the WMS, and the carrier rating engine all need to agree on whether a box is measured on the inside or outside, which sounds basic until someone in the plant uses a tape measure from a different drawer.

Integration is the next hurdle. A useful deployment usually needs your ERP, WMS, OMS, carrier system, and packaging database to share data cleanly. Otherwise the model sees one version of the truth and the warehouse sees another. That mismatch is expensive. One beverage client I advised in Indianapolis lost nearly $18,000 in a quarter because carton data in the WMS was updated, but the carrier rating engine still pulled old dimensions. The AI recommendation was decent. The integration was not. The worst part? Everybody pointed at everybody else, which is apparently a beloved corporate sport.

AI also evaluates packaging variables that teams sometimes overlook:

• Product fragility and orientation requirements, such as upright-only glass or temperature-sensitive goods.
• Void fill needs for shock absorption, often measured in inches or grams per pack.
• Dimensional weight impacts by carrier and zone, especially for parcels over 1,728 cubic inches.
• Material compatibility for heat, humidity, or oily products, including kraft liners and poly mailers.
• Sustainability targets such as reduced corrugated usage or lower plastic content, tracked in pounds or percentage reduction.

Those variables connect directly to operational KPIs. If you’re trying to judge what is ai in packaging logistics doing for you, track these numbers before and after:

• Damage rate per 1,000 shipments.
• Cost per shipment, including materials and freight.
• Pick/pack time per order.
• On-time delivery rate by carrier and service tier.
• Packaging waste per order, measured by grams or cubic inches.

Now the cost question, because it always comes up. Software fees vary a lot. A smaller pilot might cost $1,500 to $4,000 per month, while a more integrated platform can run $25,000 to $80,000 for implementation plus ongoing support. Add data cleanup, which is rarely budgeted honestly, and you may spend another $5,000 to $15,000 if your master data is messy. what is ai in packaging logistics worth? It depends on how much waste you are removing. A 4% reduction in freight spend on a $1.2 million annual shipping budget is real money, and so is a $0.15 per unit savings on 5,000 pieces when the same carton runs every month.

ROI usually comes from four places: fewer returns, better box utilization, lower freight costs, and less manual labor. I’ve seen teams save on postage alone by reducing dimensional weight charges by 6% to 11% after carton optimization. In another project, a fulfillment center shaved 22 seconds off average pack time by removing three unnecessary box choices. That sounds small until you multiply it by 8,000 orders a day, or by a 12-15 business day production schedule when new box tooling is involved.

If you are also redesigning custom printed boxes or a broader product packaging line, keep the creative and operational teams talking to each other. Beautiful retail packaging that ships poorly is still a cost problem. I’ve sat in meetings where marketing wanted a taller insert for presentation, while logistics wanted a flatter shipper to save 38 cents per order. AI doesn’t solve the politics, but it makes the tradeoff visible. Sometimes that visibility is half the battle, even if nobody in the room wants to admit it. A folding carton that costs $0.42 more per unit can still win if it prevents one in every 250 shipments from being repacked.

For teams buying materials or reworking structure, it helps to keep one eye on industry standards too. If you need shipping-test validation, look at the International Safe Transit Association’s test methods at ista.org. For sustainability metrics and waste reduction guidance, the U.S. EPA has useful packaging and materials resources at epa.gov. That doesn’t answer what is ai in packaging logistics by itself, but it keeps the conversation grounded in measurable performance rather than vendor slogans.

Step-by-Step: How to Introduce AI in Packaging Logistics

The best way to approach what is ai in packaging logistics is not to buy software first. Start by auditing the current process. Map how orders move from OMS to warehouse to shipping. Count how many manual decisions are made per shipment. In one distribution center I visited in New Jersey, the team made 11 separate packaging decisions for a single order wave, and four of them were based on tribal knowledge, not written rules. That is the sort of thing that looks harmless until the month-end report arrives and ruins everyone’s lunch. A proper audit usually takes 5 to 7 business days if the site has clean records and fewer than 2,500 daily orders.

Step one is the audit. Look for bottlenecks, repeated exceptions, and expensive errors. Are you overspending on cartons? Are fragile products breaking in a few specific lanes? Are packers using too much void fill because nobody trusts the carton library? That audit should produce a list of the top three problems. what is ai in packaging logistics works best when it has a narrow, ugly problem to solve, such as reducing breakage on 16-oz glass jars in zone 7 or cutting excessive dunnage on subscription kits shipped from Columbus, Ohio.

Step two is data cleanup. Standardize SKU dimensions, weight, carton library entries, and shipping performance records. If one system uses centimeters and another uses inches, fix that before anyone touches a model. If return reasons are entered as “damaged,” “broken,” and “arrived cracked,” normalize them into one field. This is not glamorous work, but it matters more than demo videos. I would rather spend an afternoon cleaning dimension fields than spend three months pretending the model is “learning” from junk. A clean carton spec should include inside length, width, height, board grade, flute profile, and finish, such as 32ECT single-wall kraft or 200# test double-wall.

Step three is choosing one high-impact use case. I usually suggest one of these:

1. Cartonization for box-size optimization.
2. Demand forecasting for packaging procurement.
3. Carrier selection for freight and parcel cost control.
4. Delay prediction for service-level protection.

Step four is the pilot. Keep it small enough to measure, usually one warehouse or a limited SKU set. A pilot with 200 SKUs is easier to control than a full rollout with 2,000. Set your baseline now: damage rate, pack time, postage per order, and exception rate. If the AI doesn’t beat the baseline after a realistic trial, the issue may be the model, the data, or the use case itself. what is ai in packaging logistics is not magic. It is a controlled experiment, and a good pilot often shows results within 3 to 4 weeks after proof approval.

Step five is training. Warehouse staff need to know when to trust the recommendation and when to override it. One supervisor in a Chicago facility told me the best systems are the ones workers can question. I agree. If the software tells you to pack a glass jar in a slim mailer with no inserts, a human should be able to say no without breaking the process. Otherwise you are just replacing judgment with obedient nonsense, and I’ve seen enough obedient nonsense to last a lifetime.

Step six is the review cycle. Weekly works for fast-moving operations; monthly may be enough for lower-volume businesses. Track the KPIs, compare AI recommendations against human choices, and ask where exceptions keep appearing. The point is to improve the model over time. That’s part of what is ai in packaging logistics: it learns, but only if someone feeds it the right feedback. A site in Monterrey, Mexico, for example, reviewed 87 exception orders in one month and used those cases to retrain the carton library before peak season.

When I help clients outline a pilot, I often tell them to tie it back to packaging design, not just shipment rules. If the system keeps recommending oversized mailers, the fix may be packaging structure, not software settings. In other cases, a small change in insert design can eliminate a whole class of packing exceptions. That is where Custom Packaging Products becomes part of the conversation, because the material choice and the logistics model should not live in separate silos. If a carton can be redesigned from 400gsm SBS to 350gsm C1S artboard without increasing crush risk, the savings can show up immediately in both freight and material spend.

Common Mistakes When Using AI in Packaging Logistics

The most common mistake is trusting bad data and expecting clean output. I’ve seen teams feed AI a carton database with duplicate SKUs, missing weights, and inconsistent unit conversions, then act surprised when the recommendations looked random. If you want a blunt answer to what is ai in packaging logistics gone wrong, that’s it: polished interface, broken inputs. It is like asking a calculator to solve a problem after somebody ripped out half the numbers. One site in Ohio had 14 carton records for the same 12" x 10" x 8" box because the naming convention changed twice in one year.

Another error is trying to automate everything at once. I understand the temptation. Once leadership hears “AI,” they want forecasting, cartonization, routing, labor planning, and returns all at the same time. That usually slows implementation and confuses the warehouse. Start with one process. Prove value there. Then expand. what is ai in packaging logistics is easier to sell internally when the first pilot saves money in a visible way, like cutting repacks by 19% at one facility in North Carolina.

Packaging constraints get ignored too often. Crush resistance matters. Product orientation matters. Sustainability targets matter. If you are using recycled-content corrugated or paper-based void fill, the model needs to respect the physical behavior of those materials. A lighter box is not always a better box. Sometimes it needs a stronger flute, a different score line, or a revised insert structure to meet ISTA transit-test expectations. A 32ECT carton and a 44ECT carton may look similar on a screen, but the performance difference in humid Houston can be dramatic.

Warehouse staff are another missed asset. The people on the floor see problems software misses: a carton that collapses during humid summer weeks, a label that sticks badly on one liner, or a SKU that is always repacked because the dimensions in the system are off by half an inch. If you ignore their feedback, you lose the practical intelligence that makes what is ai in packaging logistics usable instead of merely impressive. A packer in Charlotte can often tell you in 30 seconds what a dashboard misses in 30 days.

Exception handling is where many projects stumble. Fragile shipments, oversized goods, seasonal spikes, and international orders cannot always be handled by the same model that works for everyday domestic parcels. One client shipping holiday ornaments needed separate logic for November and December because breakage rates doubled when the mix shifted. AI can handle exceptions, but only if you tell it what exception means. A 24-inch wreath shipper bound for Denver is not the same as a 6-ounce ornament set headed to Miami.

Finally, some teams misunderstand AI as a replacement for logistics judgment. That is not how I would position it. It is a decision-support tool. A very smart one, yes. But still a tool. The best use of what is ai in packaging logistics is not to remove human expertise. It is to free experienced people from repetitive choices so they can focus on exceptions, supplier negotiations, packaging design, and service recovery. In a facility with 60 pack stations, that can mean the difference between another stressed-out shift and a manageable one.

Expert Tips to Get Better Results from AI in Packaging Logistics

My first tip: use AI to support packaging design choices, not just shipping decisions. Those two areas are deeply connected. If your product packaging is structurally inefficient, no routing model will fix that cost on its own. I’ve seen a brand save more by changing insert geometry than by negotiating a slightly lower parcel rate. That’s why what is ai in packaging logistics should sit alongside packaging engineering, not outside it. A small insert change in a 250,000-unit annual run can save thousands of dollars before freight even enters the conversation.

Second, test packaging scenarios with real shipment data before you switch materials or box sizes. Don’t guess. Run 500 or 1,000 historical orders through the model and compare the damage rate, cubic volume, and freight cost. If the AI suggests a smaller carton, ask whether the item still passes the drop profile you care about. A 12% materials saving is irrelevant if returns rise by 4%. I like to see trials that compare at least three pack configurations, such as a poly mailer, a single-wall corrugated carton, and a molded-pulp insert system.

Third, connect AI insights to sustainability goals. This is where teams can reduce void fill, trim excess corrugated, and lower freight emissions by tightening package dimensions. I’m cautious here, though. Sustainability cannot be a vague promise. It needs a measurable target, like reducing corrugated usage by 8% or cutting plastic void fill by 2,000 pounds a quarter. what is ai in packaging logistics can help, but it should report the metrics plainly. If your carbon accounting team wants grams of paper saved per order, the model should be able to show that number without a scavenger hunt.

Fourth, set guardrails. The system should respect brand standards, product protection rules, and service-level commitments. If a luxury skincare box must preserve a specific unboxing experience, the AI should not recommend a plain shipper that saves 14 cents but destroys the presentation. Package branding matters in retail packaging, subscription, and gifting. The right answer is not always the cheapest box. In one Toronto project, a rigid setup with a $0.58 inner tray outperformed a cheaper wrap because the return rate dropped from 2.1% to 0.6%.

Fifth, monitor exception shipments closely. That’s where the model learns the fastest. If a certain SKU family keeps failing in zone 8 or on second-day air, retrain the rules or update the input fields. I’ve seen one company reduce exception-related repacks by 31% after reviewing only 73 problem shipments. That kind of number is why what is ai in packaging logistics deserves ongoing attention after launch. A weekly exception review in a 3PL facility in Atlanta can surface more value than a quarterly executive meeting ever will.

Sixth, compare AI outputs against human decisions. You’ll learn two things at once: where the model is smarter than the team, and where the team knows something the model does not. Sometimes the AI is right because it spotted a pattern nobody had time to see. Sometimes the planner is right because the system missed a supplier delay or a holiday peak. That comparison builds trust. If the AI recommends a carton that saves $0.19 but increases line rework, the floor team should be able to show why.

One more practical observation from a supplier negotiation in the Midwest: vendors love to sell AI as if it replaces operational discipline. It doesn’t. If you haven’t standardised carton libraries, defined measurement rules, and documented pack exceptions, the software will merely digitise the chaos. what is ai in packaging logistics works best where the basics are already under control. A standardized spec sheet with board grade, flute type, and inner dimensions is worth more than a glossy demo.

What to Do Next: A Practical 30-Day Action Plan

If you’re ready to act, start with a simple 30-day plan. Day 1 through 5: list the top three packaging logistics problems you want to solve. Be specific. “Reduce damage” is too broad. “Cut breakage on glass items shipped in zone 5 by 20%” is better. “Lower freight” is vague. “Reduce DIM weight charges on orders over 18" long” is useful. That’s the level of clarity what is ai in packaging logistics needs to be effective. A target tied to $0.12 per order or 1.5% breakage is far more actionable than a slogan.

Day 6 through 10: gather the minimum dataset for a pilot. You need product dimensions, packaging specifications, shipping rates, delivery performance, and damage history. If you have box family data, carton tests, or ISTA results, include those too. If not, note what you don’t have. Missing data is still data. If your team can only find 18 months of order history but not the packaging spec archive, that gap should be documented before the first model is built.

Day 11 through 15: request demos or trials from vendors. Ask specific questions. How do they handle exceptions? What integrations are native versus custom? What is the pricing structure: per shipment, per user, per warehouse, or fixed subscription? How often does the model retrain? If a vendor dodges those questions, that tells you something. what is ai in packaging logistics should come with transparent implementation details, not fog. A good answer might include a setup timeline of 12-15 business days from proof approval to pilot start.

Day 16 through 20: assign one internal owner. One person. Not a committee. That owner should coordinate warehouse, operations, IT, and procurement input. In my experience, cross-functional projects fail when everyone assumes someone else is measuring the baseline. Accountability matters more than software brand names. If the packaging engineer is in Richmond and the fulfillment lead is in Phoenix, one project owner should still be able to reconcile both sides of the workflow.

Day 21 through 25: set baseline metrics. Record current damage rate, pack time, average postage, repack frequency, and exception volume. If possible, capture a two-week history. You need a before-and-after picture, not a vague feeling that “things seem better.” what is ai in packaging logistics is measurable, or it isn’t useful. A clear baseline might show 3.2% damage on one SKU family, 41 seconds average pack time, and $4.86 postage per order before the pilot starts.

Day 26 through 30: review the pilot results. Decide whether to expand, refine, or switch use cases. If the cartonization pilot saves $0.24 per order across 12,000 monthly orders, that’s $2,880 a month before labor and return savings. If the model barely improves over current practice, don’t force the rollout. Move to a different use case or clean the data further. If a new box style needs a custom die and the tooling lead time is 12-15 business days, factor that into the timeline before you promise savings to leadership.

And keep one final link between packaging and logistics in view: your packaging suppliers can help more than you think. If you’re redesigning inserts, modifying sleeve structures, or considering branded packaging upgrades, bring logistics data into those conversations early. That’s where smarter material choices and better shipping outcomes begin to converge. If you’re ready to evaluate product formats, Custom Packaging Products is the kind of internal resource that should sit beside the AI pilot, not after it. A supplier in Guangdong, for instance, may quote a rigid insert at $0.38 per unit for 10,000 pieces, while a nearby converter in Ohio may offer a 9-business-day turnaround on a simpler corrugated structure.

From where I sit, what is ai in packaging logistics is less about replacing people and more about giving them cleaner decisions. The warehouses that win are usually the ones that pair software with discipline, honest data, and a willingness to change a carton when the numbers justify it. I’ve seen that happen in facilities from Toronto to Dallas, and the pattern is always the same: clarity, measurement, then improvement.

what is ai in packaging logistics is not a buzz phrase if you treat it like an operational method. It can cut errors, improve planning, reduce waste, and speed up shipping, but only when the data is clean, the integration is real, and the team is willing to test, measure, and adjust. That’s the practical version I trust after years around pack lines, supplier tables, and the occasional stubborn carton library. If your current packaging line uses 350gsm C1S artboard, 32ECT corrugate, or a mixed pack of poly mailers and rigid mailers, AI can help most when each material is cataloged with exact specs and a clear Cost Per Unit.