Controlling Multi-Echelon Inventory Systems with Waiting Time Fill Rate Constraints

Abstract

The purpose of this project was to investigate if, how, and in what ways service
criterias based on customer waiting times may be used for analytical multi-echelon
inventory optimization by the company Syncron. The project also aimed to generate
some insights in what impact different parameters may have on achieving the target
customer order waiting time fill rate.

This was in part achieved through a literature overview of service measures and
existing analytical models. Three service measures are described. Waiting time fill
rate was selected for further investigation as it is quite intuitive for practitioners, less complex and easier to implement. The optimization model that was used was an adaptation of the model by Berling and Marklund (2013). The model was modified to handle allowable waiting times shorter than retailers’ transportation time using a result called ”lead time shift”. The case when the allowable waiting times are equal to or longer than the transportation time are not covered in this project, but it is noted that these situations may be translated into customer segments with direct upstream demand at the central warehouse. Three test series using data from two case companies were analyzed. The first series was a numerical study of parameter effects, the second used a larger number of items and the third represents a potential real case scenario for Syncron.

The results show that the retailer’s customer arrival rate and the central warehouse’s lead time are highly impactful on the model’s general ability to reach targets. The order quantity at the central warehouse may have a large impact if it is large enough that the reorder point becomes negative. In such cases, adding the constraint that the warehouse’s reorder point should be equal to or larger than -1 greatly improved performance.

The inclusion of an allowable waiting time slightly reduced the model’s performance. The negative effect of allowable waiting time was slightly larger for retailers with larger customer arrival rates. The inclusion of an allowable waiting time of 5 days in the third test series lowered the achieved fill rate by 1.12 percentages on average and lowered the average total inventory by 10.97% when compared to no allowable waiting time.

Popular Abstract

For many manufacturers of industrial equipment and machines it is becoming more and
more common to offer your products as a service rather than a single physical product. A key part of the service offering is guaranteeing a high uptime of machinery through availability of critical spare parts. Ensuring high global availability of spare parts often leads to the deployment of complex distribution networks consisting of multiple levels of warehouses and dealers as well as millions of dollars tied up in spare parts across the globe. What if a tradeoff could be made with customers to lower the amount of needed inventory?

A possible trade-off dimension is lead time. More specifically, the contract between the manufacturer and customer could state that the agreed upon availability should be maintained within a couple of days from announcing their order, a so called allowable waiting time. As an example, a target could be that 95% of demand should be satisfied within 2 days. In most cases demand could still be satisfied immediately, but it would incentivise customers to announce their orders ahead of time when possible. Early announcement of orders can make sense in situations when the part will be used for planned maintenance or when the customer can perform other maintenance while replacing the part.

However, in order to actually use a service measure that includes an allowable waiting time a couple of problems must first be solved: (1) How can the resulting waiting time service of the manufacturer’s network be estimated? (2) How can (near-)optimal reorder points be found efficiently for the system such that a target waiting time service is fulfilled? (3) In what cases can the optimization model be expected to reach the target?

It was found that the waiting time service can be estimated using a result called “lead time shift”. This means that an allowable waiting time can be included in an analytical model by simply modifying the lead time by subtracting the allowable waiting time.

The lead time shift is a quite general result meaning that it can be used in basically any optimization algorithm. For this project the chosen model was the induced backorder cost optimization model by Berling and Marklund of LTH.

The performance of the optimization model was analyzed through simulation of a number of test series based on data from Syncron’s customers. The results showed that dealers’ customer arrival rate and parameters that affect the variability of the central warehouse’s performance had a high impact on the model’s ability to reach the target service. In cases where the variability was caused by negative warehouse reorder points, requiring the warehouse’s reorder point to be equal to or greater than -1 greatly improved performance. The inclusion of an allowable waiting time slightly reduced the model’s performance. This negative effect was slightly larger for dealers with larger customer arrival rates.

For a series of different products the inclusion of an allowable waiting time of 5 days with the same availability target lowered the achieved availability by 1.1 percentages, but simultaneously lowered the required inventory by 11%.

• author: Brieditis, Ludwig and Agardh, Jacob
• supervisor: Johan Marklund
• organization: Production Management
• course: MIOM05 20191
• year: 2019
• type: M1 - University Diploma
• subject: Technology and Engineering
• keywords: Multi-Echelon, Inventory Control, OWMR, Service Measurements, Waiting Time Fill Rate, Lead Time Shift, Compound Poisson Demand, (R, Q)-policy
• language: English
• id: 8994350
• date added to LUP: 2019-10-15 14:35:36
• date last changed: 2019-10-15 14:35:36