--- title: "Store reorder points and supplier history so AI can plan inventory orders" description: "Keep reorder points, supplier lead times, seasonal patterns, and quality history in files, so AI reorder advice uses your numbers." canonical: "https://scalewithsearch.com/articles/ai-memory-for-inventory-management" date: "2026-01-28" modified: "2026-09-25" --- ## Site navigation - [Scale With Search](https://scalewithsearch.com/) - Real estate - Real estate - [Real estate](https://scalewithsearch.com/for/real-estate) - Work - Start here - [Send your brief](https://scalewithsearch.com/work#send-your-brief) - [Prepare your six-question brief](https://scalewithsearch.com/work#prepare-your-six-question-brief) - Build - [Site build, content library with SEO, signal desk](https://scalewithsearch.com/work) - For your business - Trades and home services - [Auto body and collision shops](https://scalewithsearch.com/for/auto-body-and-collision-shops) - [Foundation and home repair contractors](https://scalewithsearch.com/for/foundation-and-home-repair) - 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You ask an AI assistant which products need a reorder. It returns a textbook formula for safety stock and lead time. So you explain your numbers. Widget A reorders at 150 units, Widget B at 75, Widget C at 200. Supplier X ships in 5 days, Supplier Y in 14, and Supplier Z needs 21 days for international orders. The next week you ask about another product, and the assistant has none of it. You restate the reorder points, the lead times, and the seasonal pattern again. Every inventory review starts from zero. Inventory memory is a small set of files that hold those numbers and the history behind them. The assistant reads the files before each review. All products, suppliers, and figures in this article are a fictional example. ## Know what the chat cannot keep Each SKU has a reorder point, a preferred supplier, and a typical order quantity. Each supplier has a lead time, a minimum order, a price tier, and a record of reliability. Seasonal products follow a yearly cycle. Fast movers need different rules than slow movers. Much of the useful knowledge sits in no system at all: - Widget A sells at a steady rate all year. - Widget B spikes in the fourth quarter. - Widget C sells with Widget D, so a stockout of one cuts sales of both. - Supplier X is reliable but costs the most. - Supplier Y costs less and has had occasional quality problems on some products. - Supplier Z costs the least but has the longest lead time. A chat session can hold these facts for one conversation. When the session ends, the facts go with it. ## Split the records by how often they change Put stable knowledge in Markdown files. Pull fast-changing numbers from the system that owns them. | Record | Where it lives | What it holds | |---|---|---| | `sku-register.md` | Markdown file | Product code, description, reorder point, preferred supplier, typical order quantity | | `suppliers/.md` | One Markdown file per supplier | Contact, lead time, minimum order, relative price, on-time record, quality incidents | | `products/.md` | One Markdown file per important product | Seasonal pattern, paired products, past stockouts and their cost, quality issues | | `ordering-rules.md` | Markdown file | How to rank orders, combine orders, and set safety days | | Current stock | Inventory or point-of-sale system | Units on hand today, exported as a CSV at the start of each review | Current stock changes every day. Do not retype it into a Markdown file, because the copy goes stale by the next sale. Export it at review time and hand the export to the assistant with the files. [Why plain text is the durable layer for AI memory](/articles/plain-text-ai-memory) explains this split between an explanation file and a governed record. ## Rank the reorders from documented points It is Monday, and you need to know which products are near their reorder points. The register lists 347 active products. Each reorder point comes from weekly sales and supplier lead time. Widget A sells 40 units a week from Supplier X, with a 5-day lead time; its reorder point is 150. Widget B sells 15 units a week from Supplier Y, with a 14-day lead time; its reorder point is 75. You ask: "Which products need reordering?" The assistant compares the stock export with the register: | Product | Stock | Reorder point | Status | Action | |---|---:|---:|---|---| | Widget B | 68 | 75 | 7 below | Order now; 14-day lead time | | Widget D | 195 | 200 | 5 below | Order this week | | Widget A | 165 | 150 | 15 above | Watch; order if sales hold at the current rate | | Widget C | 412 | 200 | Well above | No action | The list is specific because the reorder points exist in a file. Without the register, the assistant can only restate the formula. ## Choose a supplier from recorded performance Widget B needs an order, and three suppliers can fill it. Supplier Y is the usual source, with a 14-day lead time and reliable quality on this product. Supplier X costs about 12% more per unit and ships in 5 days. Supplier Z costs about 12% less per unit, needs 21 days, and has an uneven quality record. The supplier files hold the evidence. For Widget B, Supplier Y delivered the last six orders on time with no quality issues. Supplier Z delivered its last order 4 days late, and the order before that had a 2% defect rate. You ask: "Which supplier should I use for the Widget B reorder?" The assistant works from the files: 1. Stock is 68 units, and sales are 15 a week, so stock runs out in about 4.5 weeks. 2. Supplier Y's 14-day lead time leaves a safe margin. 3. Supplier X's 5-day lead time is not needed, and it costs more. 4. Supplier Z's 21-day lead time is risky at this stock level, and its recent delay adds risk. 5. Recommendation: Supplier Y, 200 units, the typical order size. Two hundred units cover about 13 weeks of sales and put stock back above the reorder point. Each step cites a recorded fact, so you can check the reasoning line by line. ## Plan seasonal stock from last year's pattern Plan the fourth quarter in mid-September. Widget B's product file records last year's pattern: October sales ran 40% above the baseline, November 30% above, and December 80% above. January returned to normal. The file also records a failure. Last year, Widget B ran out in early December because the order was too small. The stockout lasted 8 days, and the file records the estimated lost sales. You ask: "How much Widget B should I order for Q4?" The assistant applies the pattern to the 15-unit weekly baseline: - October: 21 units a week for 4 weeks, 84 units. - November: 20 units a week for 4 weeks, 80 units. - December: 27 units a week for 5 weeks, 135 units. - Fourth-quarter demand: about 300 units against 68 in stock. It recommends two orders of 150 units from Supplier Y. Place the first now; it arrives in early October and covers October and the first half of November. Place the second in mid-November; it arrives in early December, before the peak. About 40 units remain at the start of January, below the reorder point of 75. The two weeks before the first arrival use about 30 units. A normal reorder in the first week of January restarts the regular cycle. The staged plan avoids a December stockout and avoids a large stock of cash-tied inventory in October. ## Account for products that sell together Widget C and Widget D sell as a pair. About 70% of Widget C sales include Widget D. When Widget D ran out last spring, Widget C sales fell by about 25%, even with Widget C in stock. Customers buy the two for one use. The relationship is in both product files, because someone noticed it over months of sales. Now Widget C has 412 units, well above its reorder point. Widget D has 195 units, just under its point of 200. A plain reorder rule says: order Widget D, leave Widget C alone. You ask: "Evaluate inventory for Widgets C and D." The assistant reads both files and finds the pairing. A Widget D stockout would slow Widget C and leave you with excess Widget C. It recommends a Widget D order now, a little above the normal quantity. It also flags Widget C: if the Widget D order is late, expect slower Widget C sales and a later Widget C reorder date. ## Record quality incidents where the next order sees them The last Supplier Z order of Widget F had an 8% defect rate. Packages arrived damaged, and several units had cosmetic defects. You issued partial refunds and lost margin on the order. Record the incident in two places: Supplier Z's file and Widget F's product file. Include the order date, the defect description, the refunds and margin lost, and the resolution. Three months later, Widget F needs a reorder, and Supplier Z still has the lowest unit price. You ask: "Should I order Widget F from Supplier Z?" The assistant finds the incident. On a typical 100-unit order, Supplier Z's saving over Supplier Y is less than half of what the last defect cost. One repeat of the defect erases more than two orders' worth of saving. The recommendation is Supplier Y, unless Supplier Z has a documented corrective action. Without the incident record, the assistant sees only the lower price. [How corrections become part of the system](/articles/corrections-become-system-memory) applies the same idea to any repeated error. ## Sequence orders by lead time and days of cover You need orders for three products from three suppliers. `ordering-rules.md` states the logic: - Rank orders by slack: days of cover minus lead time. - Place orders with the least slack first. - Combine orders to one supplier to meet minimum quantities or cut freight. - Keep a stated number of safety days before stock runs out. You ask: "Generate an ordering schedule for Widgets A, B, and E." The assistant computes the slack for each: | Product | Supplier | Lead time | Days of cover | Slack | Order | |---|---|---:|---:|---:|---| | Widget E, 300 units | Supplier Z | 21 days | 19 days | -2 days | Today. A 2-day gap is likely; ask about faster freight or cover from another supplier | | Widget B, 150 units | Supplier Y | 14 days | 16 days | 2 days | Today or tomorrow | | Widget A, 200 units | Supplier X | 5 days | 11 days | 6 days | By Thursday, which keeps 2 safety days | The schedule avoids a stockout where it can, shows where it cannot, and does not tie up cash in early orders. Every number comes from a file or the stock export, so you can check each one. ## Set up the files and the review routine The setup has three parts: 1. Claude Code, which runs in your terminal and reads files in a project folder. 2. A folder, for example an Obsidian vault, with the register, supplier files, product files, and ordering rules. 3. A `CLAUDE.md` file that tells the assistant where each record lives and which record wins when two disagree. The [AI context manifest template](/articles/ai-context-manifest-template) gives a format for that third file. No integration with inventory software is required. The files sync through your normal cloud storage. The memory persists across review cycles. Review stock on Monday and close the session. On Thursday, open a new session and ask about supplier options. The assistant reads the same files, because the knowledge is in the folder, not in the chat history. Keep the files current as part of the routine. After each order, record the actual delivery date. After each defect, add the incident. After each season, update the pattern with the real numbers. ## Keep the purchase decision with a person The assistant ranks, compares, and drafts. It does not place orders. A purchase commits cash and binds you to a supplier, so a named person approves each order before it goes out. [Who approves what an AI agent sends](/articles/who-approves-what-an-ai-agent-sends) covers how to set that rule. If the stock export is missing or older than the review date, the assistant stops and says so. It does not rank reorders from the last export it saw. ## Related: AI memory - [Document your business processes so an AI can follow them without a briefing](/articles/how-to-document-business-processes-for-ai) - [Reorganize an existing AI vault so the assistant finds the right context](/articles/how-to-organize-ai-knowledge-vault) - [Move business context out of custom instructions and into files](/articles/why-custom-instructions-fail) ---- ```text .|########||. .|########||. .|########||. |##||. .||##|. |##||. .||##|. |##||. .||##|. |#|. .|#|. |#|. .|#|. |#|. .|#|. |#| |#| |#| |#| |#| |#| .#| |#. .#| |#. .#| |#. .#. .#| .#. .#| .#. .#| |#. .#| |#. .#| |#. .#| |# ...... #| |# ...... #| |# ...... #| .# ||#########| #| .# ||#########| #| .# ||#########| #| .#. |######||######|. .#. .#. |######||######|. .#. .#. |######||######|. .#. #. .##|###|##|#|######| .# #. .##|###|##|#|######| .# #. .##|###|##|#|######| .# || |##|#||||||||||||#||#| || || |##|#||||||||||||#||#| || || |##|#||||||||||||#||#| || # |#||||||||||||||||||||#| #. # |#||||||||||||||||||||#| #. # |#||||||||||||||||||||#| #. || |#||||||||||||||||||||||#| || || |#||||||||||||||||||||||#| || || |#||||||||||||||||||||||#| || # .#||||||||||||||||||||||||#| # # .#||||||||||||||||||||||||#| # # .#||||||||||||||||||||||||#| # || ....|||#||||||##|#|||#|#||##||||....|. || || ....|||#||||||##|#|||#|#||##||||....|. || || ....|||#||||||##|#|||#|#||##||||....|. || #. . ....|# ....#||| ||| .#||| ....#. .# #. . ....|# ....#||| ||| .#||| ....#. .# #. . ....|# ....#||| ||| .#||| ....#. .# # . ||||#| .#####|| . .#| .#||| ||||# #. # . ||||#| .#####|| . .#| .#||| ||||# #. # . ||||#| .#####|| . .#| .#||| ||||# #. .| ||||| #. |#|||||. || #. |#||. .||||| || .| ||||| #. |#|||||. || #. |#||. .||||| || .| ||||| #. |#|||||. || #. |#||. .||||| || || |.... #. ....|#. |. ...|. ....|| || || |.... #. ....|#. |. ...|. ....|| || || |.... #. ....|#. |. ...|. ....|| || #. .||||||##||||||##||####|||||||#||||||#| .# #. .||||||##||||||##||####|||||||#||||||#| .# #. .||||||##||||||##||####|||||||#||||||#| .# # .||####|###########################|. # # .||####|###########################|. # # .||####|###########################|. # # .#||||||.#.|| # |. ..# #| #|||||#| # # .#||||||.#.|| # |. ..# #| #|||||#| # # .#||||||.#.|| # |. ..# #| #|||||#| # # .#|||||| .. |# ## |#| ...#|#|||#| # # .#|||||| .. |# ## |#| ...#|#|||#| # # .#|||||| .. |# ## |#| ...#|#|||#| # # .#|||#|# .# .#| #| ##|.#..#|||||#| # # .#|||#|# .# .#| #| ##|.#..#|||||#| # # .#|||#|# .# .#| #| ##|.#..#|||||#| # # .#||||||############|######|||||#| # # .#||||||############|######|||||#| # # .#||||||############|######|||||#| # # |...||#||||||#||||#|#||||||#|||||#||| |.| # # |...||#||||||#||||#|#||||||#|||||#||| |.| # # |...||#||||||#||||#|#||||||#|||||#||| |.| # #. .. ||||# .|||##|. |#| .|| || .|||#. #| # .# #. .. ||||# .|||##|. |#| .|| || .|||#. #| # .# #. .. ||||# .|||##|. |#| .|| || .|||#. #| # .# || | ...|| ...##| | #| .|. |. ##### .. .| || || | ...|| ...##| | #| .|. |. ##### .. .| || || | ...|| ...##| | #| .|. |. ##### .. .| || || ||||| || ||||#| . |. |. |#. ||||| |#| || || || ||||| || ||||#| . |. |. |#. ||||| |#| || || || ||||| || ||||#| . |. |. |#. ||||| |#| || || .# |.....#|....|#.||||.|||##.|#|....||.#||.#. #. .# |.....#|....|#.||||.|||##.|#|....||.#||.#. #. .# |.....#|....|#.||||.|||##.|#|....||.#||.#. #. #.|||||||#############################| ||| .# #.|||||||#############################| ||| .# #.|||||||#############################| ||| .# || ##||#||#||||||#||#||#||#||##. || || ##||#||#||||||#||#||#||#||##. || || ##||#||#||||||#||#||#||#||##. || # .#|||||||||#||#|||||||||||#| # # .#|||||||||#||#|||||||||||#| # # .#|||||||||#||#|||||||||||#| # || |#||||||||||||||||||||||#| || || |#||||||||||||||||||||||#| || || |#||||||||||||||||||||||#| || .# |#||||||||||||||||||||#| #. .# |#||||||||||||||||||||#| #. .# |#||||||||||||||||||||#| #. || |#||||||||||||||||||#| || || |#||||||||||||||||||#| || || |#||||||||||||||||||#| || #. .######|#|#########| .# #. .######|#|#########| .# #. .######|#|#########| .# .#. |#####||||#####| .#. .#. |#####||||#####| .#. .#. |#####||||#####| .#. |# ||########|| #. |# ||########|| #. |# ||########|| #. |# ...... #| |# ...... #| |# ...... #| |#. .#| |#. .#| |#. .#| |#. .#. |#. .#. |#. .#. .#| |#. .#| |#. .#| |#. |#| |#| |#| |#| |#| |#| .|#|. .|#|. .|#|. .|#|. .|#|. .|#|. |##||. .||##| |##||. .||##| |##||. .||##| .||########||. .||########||. .||########||. 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