Hubbry Logo
DIFOTDIFOTMain
Open search
DIFOT
Community hub
DIFOT
logo
7 pages, 0 posts
0 subscribers
Be the first to start a discussion here.
Be the first to start a discussion here.
DIFOT
DIFOT
DIFOT
View on Wikipedia
from Wikipedia

DIFOT (delivery in full, on time) or OTIF (on-time and in-full [delivery]) is a measurement of logistics or delivery performance within a supply chain. Usually expressed as a percentage,[1] it measures whether the supply chain was able to deliver:

  • the expected product (reference and quality)
  • in the quantity ordered by the customer
  • at the place agreed by the customer
  • at the time expected by the customer (in many cases, with a tolerance defined in conjunction with the customer).

Function

[edit]

OTIF measures how often the customer gets what they want at the time they want it. Some consider it superior to other delivery performance indicators, such as shipped-on-time (SOT) and on-time performance (OTP), because it looks at deliveries from the point of view of the customer.

This key performance indicator (KPI) has the advantage of measuring the performance of the whole logistic organization in meeting customer service expectations. To reach a high OTIF level, all the functions of the supply chain (among which orders taking, procurement, suppliers, warehouses, transport ...) must work at their best level.

Calculation

[edit]

Generally OTIF is calculated by taking into account the number of deliveries:

OTIF ( % ) = number of deliveries OTIF ÷ total number of deliveries * 100

But it can also, according to organizations, be calculated according to the number of orders or the number of the order lines.

Some organizations calculate OTIF by the percentage of the total order quantity that has been on-time. This goes against the principle of OTIF as the in-full component of OTIF has not been met.

Requirements for the OTIF measurement are :

  • have a delivery date (even hour for some organizations) stated on the customer order or specified by the customer
  • measure the date or the hour of delivery and archive it in the system
  • maintain record of the reasons why an order was not OTIF.

If the orders are split at the customer request, then each delivery line is considered.

Companies which have set up a measure of the OTIF are unanimous in recognising its value.[citation needed] They quote among other positive aspects: the increase in operating profit due to the reduction of operating expenses (in relation with the non-quality reduction, better inventory control, better customer orders taking, higher reliability in storage and transport ...) and the increase of sales (due to a better product availability for sales).

Typically, leading practice in the [UK] retail sector identified by researchers Janet Godsell and Remko van Hoek would demand an OTIF in excess of 97 per cent measured at a stock keeping unit (SKU) level.[2] However Godsell's research also found that instances where an OTIF target was met when measured against a target of the promised delivery date stated by the supplier to the customer (the "promise date"), would often fail to meet the same performance measure when the delivery date requested by the customer (the "request date") was used as the target.[3]

The OTIF notion was extended to DIFOTAI (delivery in full, on time, and accurately invoiced), which also takes into account the quality of the invoicing. [citation needed]

References

[edit]
Revisions and contributorsEdit on WikipediaRead on Wikipedia

DIFOT

View on Grokipedia
from Grokipedia
DIFOT, an acronym for Delivery In Full On Time, is a key performance indicator (KPI) in that measures the percentage of customer orders delivered completely, with the correct quantity and products, and by the requested delivery date. Also known as OTIF (On Time In Full), it evaluates the efficiency of and fulfillment processes from the customer's perspective, focusing on both timeliness and completeness rather than just shipment initiation. The DIFOT metric is typically calculated using the formula: (Number of orders delivered in full and / Total number of orders) × 100, though variations may apply it to line items or shipments depending on the organization's tracking method. For example, if 450 out of 500 orders meet both criteria, the DIFOT rate is 90%. Accurate computation requires reliable data on delivery records, often supported by to track factors like levels, supplier , and transportation delays. A high DIFOT score, ideally above 90-95%, signals strong reliability, enhanced , and loyalty, as it ensures customers receive exactly what they ordered without delays that could disrupt their operations. Conversely, low scores highlight issues such as delays, shortages, or human errors, prompting businesses to refine processes like supplier agreements and . In and distribution sectors, DIFOT is particularly valuable for assessing supplier and driving continuous improvement initiatives.

Overview

Definition

DIFOT stands for Delivery In Full On Time. It serves as a key in , measuring the percentage of customer orders that are fulfilled completely—all items in the specified quantity—and delivered within the agreed-upon timeframe, defined as the customer-specified delivery date. This metric emphasizes both completeness and timeliness to assess overall delivery reliability. DIFOT applies specifically to outbound deliveries from suppliers to customers within and broader operations. DIFOT gained widespread prominence in the 2010s, particularly through adoption by major retailers like to evaluate supplier performance. DIFOT contributes to enhancing by promoting dependable .

Importance

DIFOT serves as a critical key performance indicator (KPI) in supply chain management by directly influencing customer satisfaction. High DIFOT rates ensure that orders are fulfilled completely and promptly, which minimizes customer complaints, fosters loyalty, and encourages repeat business through enhanced trust in the supplier's reliability. For instance, businesses achieving consistent DIFOT performance often see reduced returns due to delivery errors, further solidifying customer relationships. From an operational perspective, DIFOT highlights the reliability of core processes such as and inventory management, enabling companies to streamline workflows and allocate resources more effectively. By tracking DIFOT, organizations can identify bottlenecks that lead to inefficiencies, ultimately improving overall and reducing associated costs like overtime or expedited shipping. This metric's role in promoting ties it to broader performance, where sustained high scores contribute to seamless end-to-end execution. DIFOT provides a competitive edge by serving as a benchmark in supplier contracts and industry standards, often with penalties imposed for subpar performance. For example, many agreements include financial repercussions for failing to meet DIFOT thresholds, incentivizing suppliers to prioritize accuracy and timeliness. In retail sectors, targets typically aim for 95% or higher, allowing top performers to differentiate themselves and secure more business. Beyond these benefits, strong DIFOT performance drives growth through increased and positive referrals, while cutting costs from penalties, rework, and excess . The metric's prominence has grown since the , fueled by expansion that demands faster and more accurate deliveries to meet consumer expectations for same-day or next-day service.

Measurement

Calculation

The DIFOT metric is calculated as a representing the proportion of orders that meet both the "in full" and "" criteria relative to the total orders processed over a defined period, such as a month or quarter. The primary formula is: DIFOT (%)=(Number of orders delivered in full and on timeTotal number of orders)×100\text{DIFOT (\%)} = \left( \frac{\text{Number of orders delivered in full and on time}}{\text{Total number of orders}} \right) \times 100 This formula derives from standard supply chain performance measurement practices, where the numerator counts only those orders satisfying both conditions simultaneously, while the denominator includes all relevant orders in the evaluation period. To apply the formula step-by-step, first define "in full" as the complete fulfillment of 100% of the ordered line items and quantities without shortages or substitutions, based on the original customer order specifications. Next, define "on time" as the delivery arrival at or before the customer-specified due date, often verified against confirmed receipt timestamps. Qualifying orders are then tallied over the chosen timeframe, excluding non-applicable cases such as canceled orders prior to processing. For partial deliveries, only those orders achieving full completion count toward the numerator; incomplete orders, even if timely, do not qualify. Granularity may shift to the line-item level in complex scenarios, where DIFOT assesses each line independently before aggregating to the order level. Accurate computation relies on integrated data from (ERP) systems, which track order details, fulfillment status, delivery confirmations, and timestamps for due dates and actual arrivals. These systems provide the raw inputs needed to classify orders as qualifying or non-qualifying without manual intervention. For illustration, consider a scenario with 100 total orders in a month: if 85 are delivered with all line items and quantities intact by their due dates, the DIFOT score is (85 / 100) × 100 = 85%. This example highlights how the metric emphasizes perfect over partial successes.

Components

The DIFOT metric comprises two interdependent components: "In Full" and "On Time," both of which must be met for a delivery to qualify as successful. These elements ensure that customer expectations for completeness and punctuality are fully addressed in supply chain operations. The "In Full" component requires that the entire order quantity be delivered without shortages, substitutions, or missing items, encompassing all specified stock-keeping units (SKUs) as per the original order. This is typically measured at the order level, where partial fulfillment of any line item results in the entire order failing the criterion, though some contexts assess at the line-item level for granularity. For instance, if an order includes multiple SKUs and one is under-delivered, the delivery does not count as "In Full" regardless of the accuracy of other items. The "" component evaluates whether the delivery arrives by or before the customer-agreed date, often considering the transit time from the point of dispatch rather than initial order placement. Grace periods are generally excluded unless explicitly defined in the agreement, emphasizing strict adherence to the committed schedule. Measurement may incorporate industry-specific tolerances, such as allowing delivery up to one day early in consumer goods sectors to account for variability, while late arrivals strictly disqualify the delivery. Due to their interdependence, a delivery must satisfy both components simultaneously to contribute positively to the overall DIFOT percentage; for example, a complete order arriving late or a punctual but incomplete shipment results in non-qualification. Nuances in measurement often rely on documentation, such as records, to verify quantities, SKUs, and arrival times against agreed terms.

Influencing Factors

Internal Factors

Internal factors refer to the controllable elements within an organization's operations that directly influence Delivery In Full On Time (DIFOT) performance, encompassing processes from production to fulfillment. These factors are critical because they determine the reliability of internal execution, where inefficiencies can lead to missed delivery commitments despite favorable external conditions. Effective management of these elements enhances overall reliability by minimizing self-induced disruptions. Inventory management significantly impacts DIFOT through issues like stockouts and overstock, which prevent complete . Stockouts occur when exceeds available stock, resulting in incomplete deliveries and lower DIFOT scores, while overstock ties up capital without guaranteeing availability due to poor allocation. Inaccurate exacerbates these problems; typical error rates in forecasting range from 20% to 50%, leading to mismatched levels that directly reduce the "in full" component of DIFOT. Order processing delays further erode DIFOT by introducing bottlenecks in the fulfillment pipeline, such as inefficient picking, packing, or internal approvals. These delays often stem from warehouse layout inefficiencies, staffing shortages, or human errors in , which can extend processing times and cause orders to miss on-time targets. For instance, slow picking or confirmation processes can reduce overall delivery timeliness, as each step in order handling contributes to the total . Production scheduling plays a pivotal role in DIFOT, particularly in manufacturing environments, where bottlenecks and mismatches delay order completion. Bottlenecks arise when production lines cannot handle scheduled volumes, leading to postponed fulfillment and on-time failures, while poor capacity alignment fails to match output with demand peaks. These internal scheduling issues can cascade through the , amplifying delays in delivery. Quality control shortcomings indirectly affect DIFOT by generating defects that necessitate returns, reworks, or partial deliveries, undermining the "in full" and aspects of orders. Defects reduce the effective delivery quantity, as faulty items fail to meet customer specifications, and rework diverts resources from timely production. In analyzed cases, issues have been shown to lower delivery on metrics to around 90%, pulling down overall DIFOT . In retail sectors, internal factors like seasonal demand spikes can overwhelm capacity, such as insufficient staffing or throughput during peak periods, leading to rushed and higher rates that compromise DIFOT. These spikes highlight the need for robust internal to avoid fulfillment breakdowns.

External Factors

External factors influencing Delivery In Full On Time (DIFOT) encompass uncontrollable elements outside a company's direct , often stemming from upstream dependencies and broader environmental conditions. Supplier reliability plays a , as delays or incomplete inbound shipments from vendors can cascade into outbound fulfillment disruptions, reducing the ability to meet customer order timelines and quantities. For instance, issues tied to unreliable suppliers directly lower DIFOT scores by creating shortages that prevent full and timely deliveries. Transportation disruptions further exacerbate DIFOT challenges through events like weather events, labor strikes, or carrier capacity limitations, which delay shipments and compromise on-time delivery rates. The 2021 Suez Canal blockage exemplifies this, halting 432 vessels and delaying cargo valued at $92.7 billion, which led to widespread bottlenecks and reduced global delivery performance. More recently, as of 2024-2025, attacks in the by Houthi militants have forced rerouting around , causing delays of up to 10-14 days and increased costs, further impacting DIFOT metrics worldwide. Customer-related issues, such as last-minute order changes or inaccurate delivery addresses, also contribute to failed deliveries, accounting for a portion of external delays that hinder DIFOT achievement. Regulatory and market factors add layers of unpredictability, with customs clearance delays in or economic fluctuations increasing costs and timelines. Trade regulations and geopolitical events can impose sudden barriers, directly impacting the timeliness of cross-border shipments and overall DIFOT metrics. In , peak holiday periods intensify these pressures, as third-party carriers face capacity constraints from surging volumes—often spiking by 300% or more—leading to widespread delivery delays during high-demand seasons.

Improvement Strategies

Best Practices

Improving is a foundational for enhancing DIFOT, as accurate predictions of customer needs help align and production schedules to minimize shortages or overstock that lead to delayed or incomplete deliveries. Organizations can achieve this by employing statistical models such as time-series analysis or algorithms, through better integration of historical , market trends, and promotional inputs. Supplier collaboration strengthens inbound reliability, directly supporting on-time outbound fulfillment under DIFOT. Implementing vendor scorecards that evaluate suppliers on metrics like delivery timeliness and order completeness fosters and enables joint planning sessions to synchronize schedules and resolve bottlenecks proactively. Process standardization through lean principles streamlines by eliminating non-value-adding activities, thereby reducing cycle times from order receipt to shipment. Key tactics include mapping workflows to identify waste, such as excess handling or waiting, and training staff in error-proofing techniques like to prevent incomplete orders. Ongoing performance monitoring ensures sustained DIFOT gains by identifying patterns in failures through regular audits and root cause analysis. Tools like Pareto charts help prioritize the vital few causes—such as specific supplier delays or internal processing errors—accounting for the majority of low-DIFOT incidents, allowing targeted interventions. For instance, a manufacturer in the printing industry formed cross-functional teams comprising , production, and personnel to address DIFOT shortfalls, resulting in a 98% achievement rate by streamlining communication and resolving recurring issues collaboratively. can support these practices by providing data visibility, but success hinges on disciplined process execution.

Technologies

Enterprise Resource Planning (ERP) systems like and , when integrated with Warehouse Management Systems (WMS), support DIFOT optimization by enabling real-time inventory visibility and order tracking across the . These integrations synchronize on availability, status, and operations, allowing for proactive adjustments to prevent delays in delivery. For instance, 's real-time monitoring features track production schedules and order execution to identify potential disruptions early. Similarly, 's inventory transaction capabilities provide instantaneous updates on material movements, enhancing accuracy in order processing. Business intelligence tools, such as Tableau, facilitate DIFOT visualization through interactive dashboards and to analyze performance trends. These platforms process historical delivery data to forecast risks like stockouts or transit , enabling data-driven decisions for improvement. By integrating with systems, tools like Tableau support time-series forecasting models that predict DIFOT impacts from variables such as demand fluctuations. Automation technologies play a key role in DIFOT enhancement, with (RFID) ensuring accurate picking and inventory handling in warehouses. RFID systems automate the scanning and verification of items during order assembly, reducing and speeding up fulfillment processes. Complementing this, AI-driven route optimization within Transportation Management Systems (TMS) dynamically adjusts delivery paths based on real-time factors like traffic and vehicle capacity. These AI algorithms minimize transit times and ensure on-time arrivals by leveraging on historical and live data. Emerging technologies, including (IoT) for shipment monitoring and for supplier transparency, have seen increased adoption in supply chains since 2020. IoT sensors embedded in shipments provide continuous tracking of location, temperature, and status, alerting managers to anomalies that could affect delivery timelines. complements this by creating tamper-proof ledgers of supplier interactions, fostering greater transparency and accountability in the upstream to support reliable on-time deliveries. The implementation of these technologies typically boosts DIFOT performance by 5-20% through enhanced data accuracy and reduced operational variances. For example, real-time tracking and integrations have achieved DIFOT increases of up to 7% above industry benchmarks, while RFID-enabled optimizations have delivered gains of 15-23% in specific case studies.

OTIF

OTIF, or On Time In Full, is a supply chain performance metric that measures the percentage of orders delivered both and complete in quantity, serving as a key indicator of fulfillment reliability. It is frequently used interchangeably with DIFOT (Delivery In Full ), though subtle variations exist in application. In some contexts, particularly within rigorous quality-focused industries, OTIF extends "in full" to encompass defect-free delivery as an implicit or explicit third criterion, ensuring not only quantity but also product integrity without damage or discrepancies. A primary distinction lies in how "in full" is assessed: OTIF often evaluates completeness at the line-item or unit level, meaning partial shortages in any order line can disqualify the entire shipment, whereas some implementations of similar metrics may assess at the order level. This granularity makes OTIF broader in certain scenarios, aligning with concepts of that incorporate additional elements like accurate documentation. For instance, an order arriving on but missing even one item from a multi-line purchase would fail OTIF criteria, mirroring the strictness seen in related delivery metrics and underscoring its role in within supply chains. OTIF gained significant traction in the 2010s, propelled by major retailers like Walmart, which imposed stringent supplier mandates requiring high compliance rates to avoid penalties, thereby standardizing its use across global supply networks. It is particularly prevalent in the automotive sector, where just-in-time manufacturing demands precise timing and completeness, and in consumer goods, where retailers enforce it to optimize inventory turnover. Typical contract targets range from 90% to 99%, with benchmarks like 95-98% common in fast-moving consumer goods to balance efficiency and penalty avoidance. Like other core KPIs, OTIF highlights the shared emphasis on timely and complete delivery to drive overall supply chain performance.

Fill Rate

Fill rate is a key performance indicator in that measures the percentage of customer orders fulfilled completely from available , focusing solely on the quantity of items shipped without regard to delivery timing. This metric emphasizes order completeness, helping businesses assess their ability to meet demand without stockouts or backorders. It is distinct from time-based metrics, as it ignores whether shipments arrive on schedule. There are two primary types of fill rate: line fill rate, which calculates the percentage of individual order lines (specific items within an order) that are shipped in full, and order fill rate, which measures the percentage of entire orders shipped completely. Line fill rate is often used for granular analysis in complex orders, while order fill rate provides a broader view of overall fulfillment success. The standard formula for unit fill rate, a common variant, is calculated as: Fill Rate (%)=(Total units shippedTotal units ordered)×100\text{Fill Rate (\%)} = \left( \frac{\text{Total units shipped}}{\text{Total units ordered}} \right) \times 100 This approach distinguishes fill rate from DIFOT by excluding any on-time delivery requirement, concentrating purely on availability. In applications, fill rate is particularly valuable for assessment in high-volume sectors such as groceries, where maintaining for perishable directly impacts shelf availability and . For instance, grocery retailers use it to evaluate supplier performance in delivering complete orders, ensuring minimal disruptions in fast-moving consumer . Industry targets typically aim for 95% or higher to support and operational efficiency. A key limitation of fill rate is that it does not account for delivery delays, potentially overlooking issues in the broader process; therefore, it is often paired with DIFOT to provide a more holistic evaluation of performance. The "in full" component of DIFOT directly aligns with fill rate principles, serving as its quantity-focused counterpart.

References

  1. https://www.6sigma.us/difot/difot-a-key-performance-indicator-for-business-success/
  2. https://www.phocassoftware.com/resources/blog/improve-your-delivery-in-full-on-time-rates-with-business-intelligence
  3. https://www.unleashedsoftware.com/blog/difot-metric-supplier-performance/
  4. https://soar.wichita.edu/server/api/core/bitstreams/06709f86-cbf9-46c0-bf04-c8fce7ce6b38/content
  5. https://docs.infor.com/csi/9.01.x/en-us/csbiolh/lsm1454143864778.html
  6. https://www.mecalux.com/blog/otif
  7. https://www.apollocouriers.com/delivered-in-full-on-time-difot-why-it-matters/
  8. https://www.transvirtual.com/blog/importance-of-difot-reporting/
  9. https://featured.com/questions/outbound-logistics-efficiency-kpi
  10. https://www.bartier.com.au/insights/articles/how-to-make-a-supply-agreement-effective-through-clear-kpis-and-slas
  11. https://kaizen.com/insights/otif-supply-chain-indicator/
  12. https://redstagfulfillment.com/2010s-ecommerce-growth-decade/
  13. https://mark-h-meng.github.io/publications/attachments/meng2018blockchain.pdf
  14. https://www.theseus.fi/bitstream/handle/10024/348527/Improving%20the%20Requested%20On-Time%20Delivery%20by%20Analyzing%20the%20Data.pdf?sequence=2&isAllowed=y
  15. https://www.irjweb.com/Study%20of%20Supply%20Chain%20Performance%20with%20DIFOT%20as%20a%20Key%20Performance%20Indicator.pdf
  16. https://www.transvirtual.com/blog/what-is-difot/
  17. https://www.traceconsultants.com.au/thinking/optimise-difot-performance-strategies-for-supply-chain-success-with-trace-consultants
  18. https://www.mckinsey.com/capabilities/operations/our-insights/defining-on-time-in-full-in-the-consumer-sector
  19. https://conexiom.com/glossary/what-is-difot
  20. https://www.ketteq.com/blog/the-hidden-costs-of-forecast-inaccuracy-and-how-to-fix-them
  21. https://www.dtdcaustralia.com.au/difot/
  22. https://www.traceconsultants.com.au/thinking/enhancing-business-performance-through-difot-a-strategic-imperative-for-ceos-and-cfos
  23. https://goldratt.co.uk/high-otif-ddp-difot-whatever-your-measure-is-heres-how-to-deliver-on-time-2/
  24. https://hedyla.com/en/logistics-seasonality/
  25. https://conexiom.com/blog/difot-and-otif-what-they-are-and-how-to-improve-them
  26. https://www.sciencedirect.com/science/article/abs/pii/S0964569123003939
  27. https://unctad.org/news/suez-and-panama-canal-disruptions-threaten-global-trade-and-development
  28. https://www.upperinc.com/blog/on-time-delivery-kpi/
  29. https://redstagfulfillment.com/on-time-and-in-full-otif/
  30. https://www.phocassoftware.com/resources/blog/inventory-management-metrics-to-handle-demand-volatility
  31. https://flexlogistics.com/news/holiday-shipping-challenges-and-how-3pls-handle-them
  32. https://www.symphonyai.com/resources/blog/retail-cpg/a-seven-step-action-plan-to-improve-demand-forecast-accuracy-and-grow-sales/
  33. https://www.slimstock.com/blog/demand-forecasting/
  34. https://www.supplychainbrain.com/blogs/1-think-tank/post/39274-improving-vendor-management-with-supplier-scorecards
  35. https://www.ivalua.com/blog/vendor-scorecard/
  36. https://www.dataparc.com/blog/using-pareto-charts-for-quality-control/
  37. https://asq.org/quality-resources/pareto
  38. https://www.transportworks.com/printing-industry-case-study
  39. https://help.sap.com/docs/sap-digital-manufacturing/resource-orchestration/real-time-monitoring-view
  40. https://docs.oracle.com/en/cloud/saas/supply-chain-and-manufacturing/24b/faosm/Inventory--Inventory-Transactions-Real-Time-SA-31.html
  41. https://precisebusiness.com/erp-improves-difot-ratio/
  42. https://blog.fusiongraph.com/tracking-inventory-in-real-time-in-sap-s4-hana
  43. https://docs.oracle.com/en/cloud/saas/supply-chain-and-manufacturing/25d/faosm/Inventory-Inventory-Transfer-Order-Real-Time-SA-472.html
  44. https://www.tableau.com/analytics/what-is-predictive-analytics
  45. https://help.tableau.com/current/pro/desktop/en-us/predictions_overview.htm
  46. https://www.tableau.com/data-insights/dashboard-showcase
  47. https://www.sciencedirect.com/science/article/pii/S2773067025000196
  48. https://lowrysolutions.com/blog/rfids-potential-in-shaping-the-future-of-supply-chain-management/
  49. https://www.omniful.ai/blog/tms-route-optimization-algorithms
  50. https://pando.ai/blogs/ai-in-logistics-intelligent-route-optimization
  51. https://jmsr-online.com/article/the-role-of-iot-in-real-time-supply-chain-visibility-and-risk-management-390/
  52. https://www.sciencedirect.com/science/article/pii/S2772503025000192
  53. https://magnascientiapub.com/journals/msarr/sites/default/files/MSARR-2024-0081.pdf
  54. https://onlinelibrary.wiley.com/doi/10.1155/int/7304193
  55. https://www.xcelpros.com/on-time-delivery-in-operations-part-2/
  56. https://pmc.ncbi.nlm.nih.gov/articles/PMC12304313/
  57. https://www.plaio.com/articles/otif-in-supply-chain/
  58. https://addvise.net/en/how-do-i-calculate-otif-delivery-reliability/
  59. https://www.supplychaindive.com/news/walmart-on-time-in-full-87-suppliers/550083/
  60. https://www.sciencedirect.com/science/article/abs/pii/S1366554524003831
  61. https://www.metrichq.org/supply-chain/on-time-in-full/
  62. https://flow.space/blog/fill-rate/
  63. https://www.mecalux.com/blog/fill-rate
  64. https://dclcorp.com/blog/fulfillment/fill-rate/
  65. https://www.unleashedsoftware.com/blog/otif/
  66. https://www.benchmarkingsuccess.com/understanding-fill-rate-kpis-line-fill-rate/
  67. https://www.inflowinventory.com/blog/fill-rate-definition/
  68. https://quickbooks.intuit.com/r/midsize-business/fill-rate/
  69. https://www.netsuite.com/portal/resource/articles/erp/supply-chain-kpis-metrics.shtml
  70. https://www.timforrest.com/top-kpis-for-supermarkets/
  71. https://solink.com/resources/industry-insights/grocery-store-kpis/
  72. https://www.spscommerce.com/resources/guide-to-retail-supply-chain-metrics/
Add your contribution
Related Hubs
User Avatar
No comments yet.