[email protected] 2006 palisade user conferencenovember 14 th,...

www.mettler-consulting.com [email protected]

2006 Palisade User Conference November 14th, 2006

Inventory Optimization of Seasonal Products

with Palisade Evolver and @Risk

Don MettlerMettler Consulting, Inc.

11-14-2006



Introduction

• Problem – how to you reduce inventory given manufacturing capacity constraints and dynamic seasonal product demands while not sacrificing industry leading customer service standards

• Solution– A genetic model that analyzes thousands of variables and

incorporates the factor of uncertainty into appropriate variables– Delivery the solution with as little interruption as possible to

existing process • Integrate with existing systems – Cognos, ERP, Sales Processing• Work seamlessly with the ordering process• Access via the Internet to allow remote users to enter mission

critical data



Capacity vs. Demand - The Problem



Goals

• Produce the just the amount of product to meet demand while keeping inventory and associated costs to a minimum– Work against corporate culture that tends to over

build in order to insure against customer stock outs

• A seamless process that can be plugged into existing systems– Minimize new operating procedures for plant

personnel and sales reps.

• A series of reports and outputs that can measure the effectiveness of the model



Model Environment

• The new model will need to incorporate the following requirements– Optimize inventory over the coming 52 weeks– Highly volatile product demand– Plant capacity and product inputs– Distributed user set



Highly Volatile Product Demand

• Product supports the agriculture industry– Lettuce, tomatoes, carrots, etc…

• High variability in the output due to weather and other natural factors

• Historic data has not been tracked• Sales personnel often do not communicate

back to the plant as effectively as they could on the product demand in order to ensure optimal plant planning



Fixed Production Capacity

• Each product line is produced using multiple machines

• Machines are shared between multiple product lines

• Capacity for each product line is the capacity the machine with the lowest capacity – “the weakest link”

• Administrators can increase capacity by adding work hours – very costly



Delivery of Solution

• Easy to use product forecast entry for the sales force with corresponding management reporting to enforce entry of forecast

• The solution must be integrated into the ordering process and only require limited input from the customer services reps for each order

• Data from all plants must rolled up (Cognos) for management reporting

• Intuitive interface for plant administrators to manage their model efficiently



The Solution

• Model based on genetic modeling that evaluates all factors within a given plant and includes the factor of uncertainty around demand variables– Variable inputs– Fixed inputs– Time periods– Outputs and measures



Variable Inputs

• Demand data– Entered on a monthly basis by the customer sales rep

per product line– Sales rep can associate a confidence level with each

product line– This data is the uncertainty of the model

• Cost data– Materials, labor, freight

• Alternative manufacturing options– Internal farm out, external farm out, or turn down



Fixed Inputs

• Plant machines– Multiple machines are required for each

product– Machines are grouped into product lines

where each machine can belong to multiple lines

– Costs are associated with each machine for the optimization process

– Capacity can be lowered or raised by adding work shifts – generally not done very often



Time Period

• Model is run on a rolling 52 week period– Variable demand is captured in months (12)– Costs and capacity are also captured in

months (12)– Each of the 52 weeks is evaluated separately– Costs and inventory from previous weeks

carry forward



Excel Prototype

• Excel was used as a model development platform since many none technical people can easily use and understand

• Easier than prototyping using a development language such as VB, C++, or Java

• Easier to demonstrate and explain to none technical people

• Results from the excel model were validated within the core team.

• Used Palisade RiskOptimizer product for the prototype



Risk Optimizer

• Once the excel model was completed we introduced variability to the model in the form of Risk Optimizer

• @Risk was used with the sales forecast data to introduce uncertainty into the model

• Optimization was used to manipulate the quantities manufactured and to implement production constraints

• The total cost of inventory was minimized and the optimal manufacturing levels stored in the database for reference as order are placed



Production Solution

• Final model was implemented in 6 plants with the possibility of more (2 east, 4 west)

• Solution must provide a recommendation to CSRs during their ordering process, with as little interference as possible

• Users and administrators interact with the model through a web interface accessible from any computer with Internet access



52 Week Optimization

• 52 individual weeks are optimized– Demand for each week– Capacity for each week– Inventory and costs for each week is carried

forward

• Future orders are match against the appropriate week for optimal results



Running Schedule

• Model runs nightly for 8 hours• Results are stored in the database

– Results are stored for each week and product type

• Previous results are stored to be used as the starting point for the next nights run.

• Weekend runs go for 16 hours to ensure optimal solutions and correct for any weekly changes

• Refreshes are possible at the administrators discretion



Application of Results

• Results of the optimization process are stored on the Database

• Each order looks up the corresponding week and product type to find the settings– The stored results will know if the plant should be

building up a given product line or ramping down

• Resulting settings are run through a linear decisioning process to determine quantities to manufacture, ship from inventory, farm out, or turn down



Order Processing

• Every order is analyzed during input – Attributes of the order that are reviewed

• Product and product line• Quantity• Due date• Customer ranking (A, B, C)

– Model returns a recommendation• Pull from inventory• Build and ship• Build and pre-build (how much to build for inventory)• Farm out internally/externally• Turn down



Sales Demand Collection• The Web based Demand Forecast is configured based

on the customers assigned to the Sales Rep. The customers are maintainable by the system administrator

• A confidence percent value lets sales reps indicate how accurate they feel their sales forecasts are– This confidence percent feeds the @Risk distribution

• The actual forecast amount is the medium while the Confidence Percent is used to determine the high and low amounts.



Administrative Tools

• Allows for a plant administrator to maintain and modify the model– Add and remove machines– Add and remove product types– Add and remove product lines– Associate machines and product types to product

lines– Set machine capacity levels– Clear previous optimization settings– Set running time length



Results

• Gradual lowering of inventory levels– Lower inventory costs– Lower manufacturing costs– Lower scrape and freight costs

• Improved customer service– Better informed customer service reps.– Less chance of stock as a result of better demand

forecasts• Development time – total of 10 months

– 2 months for the prototype– 5 months for the web model– 3 months for system integration and product rollout

[email protected] 2006 palisade user conferencenovember 14 th,...

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