Las capacitaciones y las limitaciones del aprendizaje

Due to the highly fluctuating physical inventory levels and complex processes at the warehouses, activities and capacity problems cannot easily be analyzed. Therefore, we need a software program that is able to simulate the actual processes in the warehouses and measure the size and frequency of the problems. A suitable simulation program is ‘Plant Simulation’, which is licensed by the University of Twente. Discrete event simulation, like the ‘Plant Simulation program’, is one of the most commonly used techniques for analyzing and understanding the dynamics of manufacturing systems. It is a highly flexible tool which enables the user to evaluate different alternatives of system configurations and operating strategies to support decision making in the manufacturing context (Negahban & Smith, 2014).

By using detailed warehouse data from the Vezet system as an input channel for the model, we can simulate the events in both warehouses per minute. As stated in chapter 3, it is rather hard to assume accuracy from the distribution functions due to the limited amount of data points of total production volumes. That is why we start with another strategy, to make a model which represents the warehouse operations at the EA and EAA warehouse. We provided the model with real data points from previous months, after which we are able to draw conclusions about the operational level of the warehouses of Dailycool and Expedition. Because of the difference in storing rules, Dailycool used class-based storage while Expedition uses dedicated storage, we created two separate models.

4.1.1. Input of the data model

Besides the differences in allocation rules between Dailycool and Expedition, both models have the same basis, which is explained in this section. The basis of the model exists of input parameters, methods and attributions to simulate processes and output parameters.

Production input

When an item is produced at the production site, it is registered in the system and immediately sent to the warehouse. The transport always takes place on a load carrier, a pallet or a rolly. In the table below, we show two examples of events that are used as input for the data model. An event must be read as follows: event number ‘35’ consists of 75 crates with freshly cut onions, stacked in crates of 11 centimeters, and happens on the first day of the simulation period at 8:03 hours. At that exact time the crates enter the warehouse and are sent to a location in storage zone 2. The second example occurs at the second day at 13:35 hours. The average number of events per day is X.

Production event

Time Description Number of

crates

Zone CBL

35 1:08:03:00.000 AH_uien_250gr 75 2 11

2569 2:13:35:00.000 AH_zuurkool_400gr 50 4 17

Table 4-1: Examples of input data for production

For the data model, we used production input of the Vezet system from November 2014 – February 2015.

Order picking input

The system of Vezet also registers when an employee has performed a pick action. After a pick action, the product is immediately loaded into the truck and does not require a storage location in the warehouse anymore. The pick information is almost identical to the production data, but has one major difference, which is the lack of the storage zone column. The zone of a product is not interesting anymore, because the order picker must pick the oldest product, no matter its storage location. Although, a product has a fixed storage zone, it may happen that this zone is fully occupied and the product is located at another zone. So, the data model looks for the oldest production date and picks that product. This can also been seen in the table. The oldest ‘fruit salade’ is produced on day 23, and will be picked firstly. The ‘fruit salade’ produced today (day 24) is pick 30 minutes later, when product with an older production date is distributed to Albert Heijn.

Pick event Time Description Production day Number of crates CBL

109 24:10:24:00.000 AH_fruit_salade_250gr 23 120 11

143 24:10:58:00.000 AH_fruit_salade_250gr 24 60 11

Table 4-2: Examples of input data for order picking Capacity

To model the processes in the warehouses, we added the capacity per location to the model. The capacity per location is dependent of the size of the crates, as explained in section 3.2. The model has to check in which crates the product is stacked and then use the corresponding capacity. For example, a product stacked in CBL 17 cm crates, can be put in a location that has a capacity of 300 crates of this type, while its capacity with CBL 11 cm is 450 crates.

Start inventory

Before we could run the model with detailed production and pick data, we had to know the number of crates in inventory at the start of the period. From the graphs presented in chapter 3, we concluded that the level of inventory at the start of the day is an important factor in the warehouse processes. Therefore, we used the ‘TELLST’ from the Vezet system to calculate the real inventory level per product. This leads to a detailed inventory list that can be used to create the start position of the model.

4.1.2. Description of the model Event Controller

The event controller manages and synchronizes the points in time that are required for the model (e.g., minutes, hours, days).

A method is used to program the desired steps to be taken by the model. For example, in our model a method is used to assign a product to a specific location.

Generator

This device is used to activate a method, given pre-specified intervals. We use a generator to check the inventory levels every 30 minutes.

Table

A table can be used to store data in rows and columns. A table can contain input as well as output data, and can easily be saved as an Excel-file.

Variables

This can be used to measure general settings, for instance the day number. In our model the days of the week are indicated with a number to be able to measure the differences between the days. Zones

‘Plant Simulation’ uses frames to create an additional layer within the model. In our simulation model, a frame is called a zone and is similar to the real lay-out of the warehouses. Within a zone we can find storage locations and crates.

Moveable unit

A moveable unit represents a load carrier (either a pallet or a rolly), which contains a number of crates. Other characteristics of the crates are also saved at the moveable unit, like product description, production date and the preferred storage zone.

Location

A location is used to stack crates and has a pre-specified capacity (depending on the size of the crates). The number and capacity of the locations, used in the model, match the warehouse situation of December 2014.

4.1.3. Output of the data model

This data model is designed to measure the performances of the two warehouses. The three most important performances for our analysis are: physical inventory level, volume utilization and location utilization of the warehouses.

Physical inventory level

The physical inventory level represents the total number of crates in inventory on a certain moment of time. When using real data as input for the model, we can compare the physical inventory level of the data model with the inventory levels we calculated in section 3.1. When using the same input range, the same inventory levels must be measured. So, this provides us with a method to verify the model.

Volume utilization

The volume utilization measures the number of crates present in the warehouse divided by the total capacity of the warehouse. As stated in section 3.2, the capacity of the warehouse is fluctuating due to the different dimensions of the crates. When measuring the capacity, we exactly know the capacity of the occupied locations, because we know the crate type at the location. However, for the free locations we do not know the capacity yet, because we do not know which product is to be expected at that location. Therefore, we have to estimate the capacity of the empty locations. To estimate the capacity of a free location, we take the average and most frequent encountered crate size: CLB 7. The volume utilization is measured every 30 minutes and stored in a table.

Location utilization

The location utilization is used to analyze the locations of the warehouse. We divide the number of occupied locations by the total number of locations. The total number of locations is a fixed unit, so the most important part is to investigate how many locations are occupied. To this end, we implemented the allocation rules of the warehouse and measured, in the data model, how many locations are occupied.

4.1.4. General structure of the data model

Both models consist of a production facility, a receiving area and storage zones (see figure 5-1 for the simulation model of Dailycool). At the input section, products are created according to the table ‘production data’ that contains production data from the past months. We use a movable unit to represent a pallet or rolly containing a number of crates. A movable unit (yellow products in the model, figure 4-2), moves from production to the receiving area where the products receive its characteristics, like number of crates on a rolly, product name and production time. Next, the products are transported to a predefined storage zone. Every action is determined and trigged by generators. By subtracting the number of crates picked from the number of crates produced, we know the marginal difference in physical inventory position. Combining this with the inventory position at the beginning of the simulation, we can calculate the inventory position at every point of time.

To illustrate the actions within a storage zone, we present storage zone 7 in figure 4-2. This is the Expedition warehouse, in which every SKU has its dedicated storage location. After a pallet is made in figure 4.1, it is sent to a storage zone. Within the storage zone, the product is immediately sent to its location (represented in figure 4-2 by a square with little dots). Each location has its own capacity and registers the amount of crates present at the location. When a product is required for distribution, it needs to be picked. The command for picking actions is given in figure 4.1, after which we see that the required crates (the yellow units) will move from the storage location to ‘the order picker location’ in the middle of the picture. He will prepare the crates for distribution and loads them into a truck.

Figure 4-2: Lay-out of storage zone 7