Data interface

Lanza needs to interface with your ERP system to get data such as master data, demand data and supply data and to update stock parameters such as reorder points and reorder quantities.

Lanza technical

Import of ERP data

During the implementation we create a data-mapping document that specifies for each data field required by Lanza the source ERP data and required transformations. Based on the data mapping we specify the required ERP data.

Our integration layer is flexible. The ERP data is delivered to Lanza via a secure data transfer solution such as SFTP, Azure Blob Storage, SharePoint Online, a web API or any other solution as requested by the client.

Export of stock parameters

An important function of the Lanza is to calculate optimal stock parameters such as reorder points and order quantities. These parameters are exported to a plain text or Excel file. The client can download this file via a secure web portal, or this file can be loaded into the ERP system using the data transfer solution. Alternatively, we can use an API to push parameters to the ERP system.

Network optimization

When your inventory network consists of multiple inventory points, the entire network can be optimized using multi-echelon optimization (MEO). As the inventory level at an inventory point influences the expected availability of other inventory points, an integral optimization is required.

Echelons

In inventory networks, echelons are different distribution levels, representing a link in the supply chain network. Inventory points in a certain echelon typically receive supply from an upstream echelon and fulfill demand for a downstream echelon.

In Lanza, this is modelled by differentiating between local and central warehouses. Local warehouses are network end-points that directly serve customer orders, while central warehouses are all intra-network warehouses that serve network replenishment orders.

 

Multi-echelon optimization (MEO)

The algorithm used for MEO is based on consists of four steps:

  1. Calculate forecasts for local warehouses,
  2. Calculate lead time for central warehouses,
  3. Create scenarios for possible central inventory levels and corresponding required local inventory levels to satisfy availability targets,
  4. Choose scenario that minimizes total network inventory.

The calculations are based on a ‘waiting time’ principle, where the lead time for local warehouses is based on the (expected) waiting time for the central warehouse.

Combining direct and downstream demand

In some, more complex networks, an inventory point can serve as a supplier for downstream echelons but also serve direct customer demand. In this case, Lanza combines direct and downstream demand for this inventory point, making sure that customer availability targets are achieved and total inventory value is optimized.

Supply management

Managing supply is essential in spare parts management. Lanza supports this process in several ways. Amongst others by using realistic lead times, giving insight into historical supply, and by optimizing your purchasing efficiency.

Lead times

Realistic lead times are essential to accurately determine inventory levels. In Lanza, there are three ways to determine what lead time to use in calculations:

  • Actual lead time
    Historical average lead time and lead time deviation, when reliable*.
  • Default lead time
    Lead time from the lead time field in the ERP system.
  • Manual lead time
    A manually entered lead time and lead time deviation.

 * when reliable: with the setting ‘Actual’, the historical lead times are only used when they are considered reliable. This depends on the lead time deviation compared to the average lead time, and the number of deliveries. These parameters can be set in the global settings. If the lead times for a certain part do not comply with these conditions, the default lead time is used.

Additional lead times

On top of the lead time from the supplier, it is important to consider any internal processing time that is necessary to acquire new parts. In Lanza, these are split up into internal processing time (i.e. the time to promote a purchase requisition into a purchase order and the time to receive goods in the warehouse) and review period (i.e. the interval that is used to replenish, e.g. once a week)

These additional lead times can be configured per segment and on a part level.

Repairables and repair lead times

For repairable parts, Lanza also accounts for the difference between new-buy purchase and repair lead times. For more information, see Repairables.

 

Order quantities

Correct order quantities are important to optimize your purchasing efficiency. For this purpose, Lanza calculates optimal order quantities. This is done with three methods:

  • Economic Order Quantity
    Order quantity that minimizes the long term holding and order costs. To determine the holding and order costs, the cost per order and holding cost % are configurable per segment and on a part level. Note that it is possible to limit the order quantity to a maximum of expected demand over a certain period, e.g. 2 years as in the example below.
  • Interval
    Order quantity that orders the required amount to satisfy demand in a certain period of time, or ‘interval’. The length (in days) of the interval is configurable per segment and on part level. Often the interval is differentiated based on the segment (larger intervals for cheap parts and smaller intervals for expensive parts).
  • Manual
    Here a planner can enter an order quantity manually.

Minimum and rounding order quantities

Lanza is able to account for supply restrictions imposed by suppliers or other factors. If the data is available, a planner can choose to account for or ignore the MOQ/MOD restrictions. This can be done on segment level or part level.

A minimum order quantity (MOQ) dictates that the order quantity should be at least the value of the MOQ. If activated, Lanza compares the calculated order quantity with the MOQ. When the calculated order quantity is lower, the MOQ is used as order quantity. Otherwise, the calculated order quantity is used. The MOQ is often used to account for supplier restrictions.

A rounding order quantity (MOD) dictates that the order quantity should be a multiple of the MOD value. If activated, Lanza rounds the calculated order quantity to the closest, or optimal, multiple of the MOD. The MOD is often used to account for packaging sizes. For example, there are 100 screws in a container and purchasing is done per container. When the calculated order quantity gives an outcome of 107 pieces, the MOD makes it 100 pieces.

Demand forecasting

Lanza offers three planning methods: reactive, proactive, and mixed. For each planning method, different forecasting algorithms are available. These settings can be adjusted on a segment level and part level.

Configurable settings include:

  • Planning method
  • Forecast method
  • Smoothing factors (if applicable)
  • Demand stream
  • Initalisation period

 

Reactive planning

This planning method is suitable for forecasting based on historical demand. Lanza offers specialized forecasting algorithms aimed at fast moving and slow moving parts.

Fast movers

Lanza offers multiple forecasting algorithms for fast moving parts. Where applicable, smoothing factors can be adjusted.

  • Moving Average (MA), applicable for parts with steady demand
  • Single Exponential Smoothing (SES), applicable for parts with fluctuating demand
  • Double Exponential Smoothing (DES), applicable for parts with trending demand
  • Triple Exponential Smoothing (TES) or Holt-Winters, applicable for parts with seasonal demand
  • Optimal, option to let Lanza choose the optimal method based on the lowest forecast error

 

Slow movers

Lanza offers specialized forecasting algorithms for slow moving parts. Because of the lack of data, regular forecasting methods often do not provide accurate results. For more information on forecasting slow movers, see Slow mover management.

  • Croston, applicable for (cheap) slow movers with intermittent demand.
  • Typical Demand Quantity (TDQ), applicable for (expensive) slow movers with lumpy and intermittent demand.

 

Proactive planning

This planning method is suitable for forecasting based on future expected demand. Future expected demand is typically gathered from planned maintenance schedules, sales forecasts, production planning, or other sources of future demand.

Within future expected demand, Lanza differentiates between different demand streams, such as fixed reservations (amount and timing of demand is known beforehand, e.g. for preventive maintenance) and projected demand (amount and timing of demand is uncertain, e.g. for inspection based maintenance). It is possible for Lanza to assist in creating future expected demand based on reliability information. For more information, see reliability-based planning.

Using proactive planning, the reorder point is calculated based on the expected demand of a specific future planning period. This planning period can be configured by the user by adjusting the planning window. Using default settings, the planning window is the period between t0 + lead time ↔ t0 + lead time + lead time.

 

Mixed planning

This planning method is suitable for forecasting based on a combination of historical and future expected demand.

Within historical demand, Lanza differentiates between different demand streams, such as planned and unplanned demand. In an ideal world you apply a reactive forecast (based on historical demand) on your historical unplanned demand, and you combine this with future reservations for your planned demand. This requires the distinction between planned and unplanned demand in the available data.

 

Reliability-based planning

A specialized forecasting method, developed for the MRO world, uses reliability information.

In this approach, Lanza forecasts demand based on:

  • Asset utilization
    Accounting for running hours, travelled kilometers/mileage, on-offs, cycles, landings, etc.
  • Failure behavior
    Accounting for Mean Time Between Failure (MTBF) or Mean Time Between Unscheduled Removal (MTBUR).
  • Installed base
    Accounting for the installed base for a part on asset and asset type-level.

This requires a proper set up of the Bill Of Material. If this is available the behaviour of an asset installed base can be used to guide the demand forecasts on part level.

Inventory models

The basis for each planning concept is an inventory model. Lanza has three inventory models: Fast, Slow, Non.

Fast

For fast movers, Lanza uses an (R,s,Q)-inventory model, calculating the reorder point (s) and order quantity (Q). Inventory is reviewed every (review) period (R). When the inventory drops below the reorder point, a replenishment is triggered to replenish the inventory with the order quantity (Q).

This model

  • is applicable for fast moving parts
  • assumes a normal distribution of demand
  • is the default model for the planning concepts: ‘Wholesale’, ‘Lean’, ‘Proactive’
Fast model inventory development

 

Slow

For slow movers, Lanza uses an (S-1,S)-model, calculating a base stock level. When inventory drops below the base stock level (S), a replenishment is triggered to replenish the inventory back to the base stock level.

This model

  • is applicable for slow moving parts
  • assumes a (compound) Poisson distribution of demand
  • is the default model for the planning concept: ‘Just in case’
Slow model inventory development

 

Non

For non-movers, Lanza uses no inventory model, as there is no demand. Here the required stock level is by definition zero, e.g. for non-movers or obsolete parts.

This model

  • is applicable for non-moving parts
  • is the default model for planning concept: ‘Non stock’

Classification

Lanza lets you classify your parts in the Classification app. Based on the available data, Lanza classifies your parts into assortments and segments.

Assortments

Lanza is able to categorize your parts portfolio into so called assortments. Assortments are groups of parts that can be used for reporting and for optimization purposes. Each assortment contains an optimization matrix.

Strategy_Classification_Assortments

Segments

This matrix is used to classify the parts in each assortment over different segments. These segments are determined by the 2 dimensions of the matrix. These dimensions are technically customizable, but we always advise to use Price and Demand Frequency.

Each dimension (in the image above: Price and Demand frequency) has a few thresholds. These thresholds can be altered by a user. With the right thresholds an accurate division of the parts over the matrix can be achieved.

Note: it is possible to configure extra rows or columns if needed. For example when you want to separate extreme slow movers from regular slow movers.

Based on this classification you can differentiate settings across the matrix.

Planning health check

One of the additional services offered by Lanza is the planning health check. For companies that use Lanza without any additional support, it is strongly recommended to perform a yearly planning health check.

Why?

You are, or will be, using Lanza and want to assure that you get the maximum value from the software. Together, we make sure that:

  • Lanza is used in the intended way
  • Lanza is set-up correctly to achieve your business targets
  • Lanza utilizes smart business rules to automate recurring processes and checks.

What?

The result of the planning health check is a scorecard on six focus areas, including proposals and recommendations for improvement. The focus areas are:

  • Alignment with business targets
    Do the settings comply with your business targets, are expected results achieved, and does Lanza have the right scope for these targets?
  • Utilization and processes
    How is Lanza used, do you follow the correct processes, are there additional efficiencies to be found?
  • Classification and planning concepts
    Is your classification still up-to-date and are planning concepts configured according to best-practices?
  • Relevance check
    Are custom additions to Lanza still relevant and up-to-date?
  • Manual changes
    What patterns are distinguishable in manual changes, are these changes sustainable, can these changes be automated by using business rules?
  • Adoption of functionalities and best practices
    Are you using the latest new functionalities and following Lanza best practices?

Contact Lanza support if you want to learn more.

ISO27001

At Lanza we are committed to safeguarding your information and ensuring the highest standards of security. To help keep your data safe we use an ISO 27001 certified Information Security Management System (ISMS). The benefits of an ISO 27001 certified ISMS include:

Enhanced information security

ISO 27001 establishes a systematic approach to managing sensitive information, ensuring that data is protected against threats like cyberattacks, data breaches, and unauthorized access.

Legal and regulatory compliance

The ISMS helps us meet regulatory requirements, such as GDPR, or local data protection laws, ensuring adherence to privacy standards.

Risk management and mitigation

The ISMS provides a structured process for identifying, assessing, and managing security risks, ensuring that threats are continuously monitored and mitigated to protect valuable assets.

Operational efficiency

Implementing an ISMS involves defining clear processes and responsibilities, improving efficiency and ensuring that everyone in the organization is aware of their role in maintaining security.

Continuous improvement

ISO 27001 promotes a cycle of continuous monitoring, review, and improvement of security practices, ensuring we stay ahead of emerging threats.

User Management

User identification

For a seamless user experience, we offer single-sing-on using Microsoft Entra ID. By using an Entra ID Enterprise Application you are in control. Users and permission to access the application can be managed in your own Entra ID tenant. Alternatively, we can create and manage users in our Entra ID tenant.

For organizations not using Entra ID we can configure direct federation between our Entra ID tenant and identity provider that supports the SAML 2.0 or WS-Fed protocol.

Role based access control

Each user is assigned one or more roles by a Lanza admin. A user’s role determines the user’s permissions. For example, the viewer role can view parts but is not able to change any part settings and a user with the dashboard role can only view the KPI dashboard and reports.

Lists

The Reports app offers the possibility to export standard and custom reports directly from Lanza. 

 

Stock parameter export

The most important export is the inventory parameter export. This is the file that contains all stocking parameters, as calculated by Lanza. This file contains all information required to update the stocking parameters in your ERP-system. This is fully configurable based on your requirements.

Typically, it contains fields such as the part number, part location, MRP-type, lot size method, reorder point, safety stock, order quantity, MIN, MAX.

Custom exports

Next to exporting stocking parameters, there is the possibility to export custom files or reports, based on the data and calculations of Lanza. Please contact your contact person if you are interested in creating a custom file.

Examples of custom exports are:

  • Forecast export
    Used to export the forecast per month, per part. For example to share internally, or to support collaboration with suppliers.
  • Lead time export
    Used to identify parts where realized lead times do not comply with contractual lead times. Logic can be built into the list to propose actions, such as adjust lead time, contact supplier, or do nothing.
  • Non-mover export
    Used as input to analyze non-movers. Logic can be built into the list to propose actions based on the available data in Lanza, such as date of last transaction, introduction date, and storage location.

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