# Infor M3 AI for Food and Beverage: Traceability, Yield and Shelf Life > Source: https://netray.co/erp-ai/infor-m3-food-beverage-ai > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: AI for ERP ## Short answer AI for Infor M3 in food and beverage gives quality, planning and plant teams plain-language access to lot genealogy, remaining shelf life, yield variance and allergen data held in M3, with the query shown. It runs privately next to M3, so a recall trace or recipe cost question is answered in minutes without sending formulations to an outside model. ## Kind industry ## Erp Family infor ## Erp Products - Infor M3 - Infor CloudSuite Food & Beverage ## Industries - Food and Beverage - Process Manufacturing ## Region Global ## Persona Plant Manager ## Eyebrow Infor M3 + food and beverage ## Intro Food and beverage plants face a mix of constraints that most discrete industries do not. Raw materials are perishable and variable, recipes use ingredients in ratios that drift with yield, products have a shelf life that governs what can ship, and regulators expect to see a full forward and backward trace on short notice. Infor M3 covers this through lot-controlled items, attribute-based grading, formula and manufacturing order structures, and warehouse functions that track location and best-before dates. The depth of data is good. The difficulty is that answers to practical questions, such as which customers received product containing a given supplier lot, sit across manufacturing orders, material issues, deliveries and customer orders. Quality managers do these traces under pressure, often with the plant in the middle of a production day. Planners juggle short-dated stock against customer minimum shelf-life requirements. Finance wants to know why standard cost and actual diverge on a particular recipe. Each is a join across M3 tables and programs that few people can run unaided. This page describes where a private AI layer on M3 helps food and beverage teams, which M3 objects it uses, where Infor's own programs and reports are enough, and what to ask about safety of formulation data before connecting any model. ## Pain Points ### Recall traces take too long **Detail:** Tracing a supplier lot forward to customers and a customer complaint back to ingredients involves several programs and exports. Mock recalls reveal how long this takes in practice, and regulators expect speed. ### Shelf life is managed after the fact **Detail:** Short-dated stock is found by browsing lots. Customer minimum remaining shelf-life rules are applied by the warehouse team from memory, and rejected deliveries follow. ### Yield and cost variance are hard to explain **Detail:** Actual material consumption against formula varies with raw material quality and process loss. Seeing the pattern by recipe, line and shift needs custom reporting. ### Catch weight complicates every report **Detail:** Items sold by count but costed by weight need dual units of measure. Standard reports often mishandle them, so analysts correct the figures by hand. ### Allergen and label data live apart **Detail:** Allergen declarations, nutrition data and specifications sit in documents or separate systems, and changeovers rely on checklists rather than data. ## Use Cases ### Forward and backward lot trace **Description:** Quality enters a supplier lot or a finished lot. The agent returns the full genealogy: manufacturing orders it fed, finished lots, warehouses, deliveries and customers, with quantities and dates. **Erp Objects:** Lot master MILOMA, balance MITBAL and location MITLOC, manufacturing order header MPDHED and materials MPDMAT, delivery and customer order lines OOLINE **Outcome:** Trace answers in minutes rather than hours, which matters most in a mock recall or actual event; completeness depends on lot discipline at issue and receipt. ### Short-dated stock and customer shelf-life rules **Description:** Planners ask which lots will fall below a customer's minimum remaining shelf life within a number of days and which orders could use them instead. The agent suggests allocations for human approval. **Erp Objects:** Lot best-before and expiry dates, customer order lines, customer shelf-life terms, warehouse balances **Outcome:** Fewer write-offs and rejected deliveries; the saving depends on how much stock is near expiry. ### Recipe yield variance analysis **Description:** The agent compares planned and actual material consumption by recipe, line and period, flags the ingredients driving variance and links to supplier lots involved. **Erp Objects:** Manufacturing order materials, product structure and formula, material issue transactions, lot attributes **Outcome:** Yield problems are tied to causes such as a supplier lot or a line, which supports targeted fixes. ### Catch weight aware reporting **Description:** Users ask for inventory, sales and margin on catch-weight items and the agent applies the correct alternate and basic units so results are consistent with finance. **Erp Objects:** Item master MITMAS unit of measure definitions, catch weight transaction quantities, sales invoice lines **Outcome:** Analysts stop correcting exports; numbers match the ledger. ### Allergen and changeover checks **Description:** The agent lists allergens declared for each item through specification documents and shows production sequences that violate the plant's allergen sequence rules, for planner review. **Erp Objects:** Item attributes, production schedule, specification documents **Outcome:** Sequencing problems are caught at scheduling rather than on the floor. ### Raw material price and supply watch **Description:** Buyers ask how commodity ingredient prices moved against contracts and which recipes are most exposed, with supplier lead times and open orders. **Erp Objects:** Purchase order lines PPS200 data, supplier agreements, item cost, recipes **Outcome:** Margin exposure is visible before price lists go out. ### Audit pack preparation **Description:** For a GFSI audit the agent assembles lot records, supplier approvals, cleaning and quality records referenced in M3 into a checklist with gaps for the quality team to resolve. **Erp Objects:** Quality inspection data, supplier master, lot genealogy, attached documents **Outcome:** Audit preparation time falls; evidence stays under the quality team's control. ## Architecture **Overview:** A private model is deployed beside M3 and reads through the M3 API (MI programs), ION and, for heavy genealogy joins, a read-only replica of the M3 database. A semantic layer defines lots, orders, items and units of measure in business terms. Specifications, certificates and audit documents are indexed privately. Outputs always show the query and sources, and any write-back is drafted for approval. **Layers:** #### M3 access **Detail:** MI programs and ION API for operational reads; read-only replica for lot genealogy and consumption joins. #### Semantic layer **Detail:** Business definitions for lots, shelf life, catch weight units, formulas and allergens, so the model uses the same logic as quality and finance. #### Document retrieval **Detail:** Specifications, supplier certificates and audit records indexed in a private vector store, linked to item and supplier keys. #### Private model serving **Detail:** Open-weight models served locally with vLLM or Ollama on plant or private cloud GPUs. #### Governance **Detail:** M3 role and facility scoping, full query logging and human approval for actions such as lot allocation. ## Deployment Options ### Plant or data center on-prem **Fit:** Companies protecting proprietary recipes **Detail:** Model and indexes inside the corporate network beside M3; no external model calls. ### Private cloud **Fit:** Multi-plant producers with central IT **Detail:** Dedicated GPU capacity in a private tenancy connected to the M3 environment. ### Hybrid edge **Fit:** Plants with limited connectivity **Detail:** Local inference at plants for trace and shelf-life questions, with replicated data from the central M3 instance. ## Compliance ### Lot genealogy queries over M3 data support the key data element and critical tracking event records the rule expects. The company defines which foods and events are covered; the AI helps retrieve and check records. **Framework:** FSMA Section 204 traceability rule (FDA) ### One step back, one step forward traceability is supported by genealogy queries; responsibility for the traceability system stays with the food business operator. **Framework:** EU General Food Law (Regulation 178/2002) traceability ### Evidence compilation and gap lists support audit preparation, with the quality team controlling records and corrective actions. **Framework:** GFSI schemes (BRCGS, SQF, FSSC 22000) ### The tool can surface declared allergens by item and sequence conflicts. Label content and declaration decisions remain with regulatory staff. **Framework:** Allergen labelling rules (FALCPA, EU 1169/2011) ## Integration Notes - Confirm lot control and best-before capture are enabled on the items in scope; traces are only as complete as receipts and issues. - Use MI programs for transactional reads and a replica for genealogy joins over MILOMA, MPDMAT and OOLINE to protect M3 performance. - Define catch weight and alternate unit logic once in the semantic layer and reuse it across all questions. - Link specification and allergen documents to M3 items via item number and supplier so retrieval cites the correct revision. - Scope by M3 company, division and facility to match user authority; test with users from different plants. - Keep any lot allocation or quarantine action as a drafted transaction approved in M3 by warehouse or quality. ## Product Fit ### ERPray **Why:** Natural-language traces, shelf-life and yield questions over live M3 data, read-only with the query shown. ### DataRay **Why:** Chat across specifications, certificates and audit documents alongside M3 data on-premises. ### Custom build **Why:** Allocation recommenders and audit pack agents built around the plant's own rules. ## Engagement ### Discovery **Duration:** 2 to 3 weeks **Deliverables:** - Trace, shelf-life and yield question inventory - Lot control and unit of measure review - Document source map - Pilot scope ### Foundation **Duration:** 3 to 5 weeks **Deliverables:** - MI and ION access - Read-only replica - Semantic layer for lots and formulas - Private model environment ### Pilot with quality and planning **Duration:** 4 to 6 weeks **Deliverables:** - Lot trace assistant - Shelf-life watchlist - Mock recall test with timing - Pilot readout ### Extend **Duration:** Ongoing **Deliverables:** - Yield variance analysis - Audit pack support - Monitoring plan - Internal runbook ## Buyer Questions - How complete is lot capture at receipt and material issue today, and how will the AI expose gaps? - Can we run a mock recall and time the result against our current process? - How are catch weight units handled in every answer? - Where do formulations and specifications live, and could they leave our network? - How are facility and company authorizations enforced? - What actions can the AI take in M3, and who approves them? - What happens after an M3 upgrade or customization change? - How is accuracy measured on our own historical trace requests? ## Faq ### Does AI make M3 traceability FSMA 204 compliant? **Answer:** No tool makes a company compliant by itself. The rule requires specific records at critical tracking events and a way to produce them quickly. AI on M3 helps retrieve, check and assemble those records from lot genealogy data. You still define covered foods, capture the right data elements and own the program. ### How fast can a lot trace be? **Answer:** Once genealogy is queryable through a replica and a semantic layer, a trace usually returns in seconds to minutes, compared with hours of manual export work. The real determinant is data completeness. A mock recall in the pilot gives you a measured time and a list of data gaps. ### Is Infor's own reporting enough? **Answer:** For stable, scheduled reports, yes. M3 programs and Infor reporting tools handle many standard traces. AI is useful when questions vary, when a non-expert needs the answer, or when M3 data must be combined with specification documents. If your needs are narrow, a good report may be the cheaper answer. ### Can it handle catch weight items? **Answer:** Yes, provided the unit definitions are modeled once in the semantic layer. Catch weight is a common source of wrong numbers in generic BI. We validate results against finance on a sample before users see them, and show which unit each figure uses. ### Will our recipes be exposed to an outside model? **Answer:** Not in an on-prem or private deployment. Models run on your hardware or in your private tenancy, and prompts, answers and indexes stay there. This matters because formulas and supplier terms are competitive information. We also recommend restricting formulation access by role. ### Can it suggest lot allocations? **Answer:** It can propose allocations that respect customer shelf-life rules and first-expired-first-out logic, with the reasoning shown. A planner or warehouse lead approves before anything is posted in M3. We keep write actions behind approval because allocation errors have real cost. ### How long does it take to get started? **Answer:** A pilot focused on lot trace and shelf life typically takes 8 to 12 weeks from kickoff for one plant. Time depends on data access, replica setup and how consistent lot and unit of measure practices are. We scope after reviewing your item and lot data. ## Search terms infor m3 food and beverage, infor m3 lot traceability, infor m3 shelf life, infor m3 catch weight, fsma 204 erp traceability, food manufacturing erp ai, infor m3 recall, infor cloudsuite food and beverage, infor m3 recipe yield, private ai food manufacturer, infor m3 manufacturing order --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json