Estudio del funcionamiento del Marmotor Barrufet

The study was conducted using discrete-event, deterministic simulation. Deterministic means that the results of the simulation runs follow directly from the chosen parameter settings and the data inserted into the model. Multiple runs using the same parameter settings and the same input data, thus, produce exactly the same results, as the model does not have any stochastic features.

*_{Results from this study have previously been presented in Småros J., Lehtonen, J-M., Appelqvist, P.,} Holmström, J. (2003), “The impact of increasing visibility on production and inventory control efficiency”, International Journal of Physical Distribution & Logistics Management, Vol. 33, Iss. 4, pp. 336-354.

Due to the deterministic nature of the model, the validation technique of conducting several replications of the simulation runs was not applicable. However, replication logic was still present to a degree. Since simulation runs were carried out for all combinations of twenty-one products, four levels of VMI adoption, as well as three different planning frequencies, a total of 252 simulation runs were performed, which means that several runs for each product and parameter value were conducted.

To make the simulation model more realistic, it was designed to mirror the supply chain of a consumer goods manufacturer, CandyCo, involved in VMI. Actual sell-through data from CandyCo’s VMI implementation as well as actual product information was used in the simulations. This means that a certain level of randomness was included in the simulations, as the sales data were not drawn from any pre-defined distribution but originated in a real-life case. The use of CandyCo’s data in the study was motivated by CandyCo’s interest in the research. The company currently has a VMI agreement with one of its largest customers, but also serves several non-VMI customers. The VMI implementation has already proven valuable both to CandyCo and to the distributor participating in the arrangement. VMI has, among other things, reduced the distributor’s workload and improved communication between the companies. Still, CandyCo believes that additional benefits could be attained if the sell-through data available through VMI could be used more effectively in its production and inventory control.

In simulation studies, a typical approach used for verifying that the model works as it is supposed to is to compare the results that it produces with data on the real-life system. However, in this study, the aim was not to accurately recreate CandyCo’s supply chain. Instead, the aim was to create a fairly realistic, yet very much simplified model that could provide more general results. No attempt was made to include aspects, such as realistic forecasting or production scheduling, in the model. Therefore, rather than validating the model by making sure that it accurately reflects the real-life supply chain, the simulation model and results were reviewed in co-operation with representatives of CandyCo who validated the general logic of the model and the results it produced. In addition, manual checks using spreadsheet software were carried out to verify the results for a sample of products and parameter settings.

Simulation model and data

The supply chain model used in the simulations consists of one manufacturer serving three distributors, which in turn serve several retail outlets. Although Figure 3 illustrates a situation where one of the distributors is involved in VMI and the other two employ traditional orders, all levels of VMI adoption– zero, one, two or three VMI distributors - are examined.

Distributor 1 (VMI) Distributor 2 (non-VMI) Distributor 3 (non-VMI) Retail outlets Retail outlets Retail outlets Manufacturer Orders Deliveries Orders Deliveries Orders Deliveries Inven tory le vel Reple nishm ents Orders Deliveries Orde rs Deli_ver ies

Production Finished goods

inventory

Production loading when the manufacturer has access to distributor sell-through data

Production loading when orders are used

Figure 3. Supply chain model used in Study 1.

In the model, the retail outlets order products from the distributors daily. Actual distributor sell-through data from CandyCo’s VMI implementation was used to model the retail outlets’ orders. From a sixty-week period of daily sell-through data, ranging from March 2001 to May 2002, mature products with reasonably level demand and belonging to one of CandyCo’s most important product categories were selected. Seasonal products and products introduced or discontinued during the observed period were discarded. The selected sample included products with different demand volumes, the best selling product having almost twenty times the sales of the least selling product (measured in units). In addition, there were differences in demand variability; the relative standard deviation of the products’ daily sell-through data varied from 65% to 213%, and the relative standard deviation of the weekly sell-through data varied from 17% to 80%. However, based on a graphical examination, sales for almost all products appeared to be roughly normally distributed, although some of the products did have heavy right-hand tails. There were also differences in the products’ replenishment frequencies. The replenishment frequencies varied between 1,5 replenishments per week on average and 4,5 replenishments per week (i.e. almost daily replenishment) on average.

To model multiple distributors, the original sell-through data series was divided into three twenty-week sections, which were then used as the demand for the three distributors in the simulation model. The resulting demand pattern is reasonably realistic as the VMI distributor stands for approximately one third of the case manufacturer’s total sales and as the ordering patterns of the different retail outlets can be assumed to be quite similar.

In the model, the distributors fulfill the retail outlets’ orders as they are received. The distributors’ inventories are managed using a re-order point control mechanism. For non- VMI distributors this means that they place orders when a product’s inventory level has dropped below a specified re-order point. For VMI distributors, the manufacturer monitors the distributors’ inventories and ships replenishments when the re-order points have been reached. The orders are multiples of the distribution batch size for each of the products and the amount ordered is the number of batches needed to bring the inventory level back above the re-order point.

For both VMI and non-VMI customers, the re-order points were determined using an iterative, manual process. First, an initial value was given to the re-order point based on standard safety stock calculations assuming normally distributed forecast errors (Vollmann et al., 1997): ) 25 , 1 ( MAD Z d ROP= + ⋅ , (3)

where d = product’s demand during the replenishment lead time,

Z = value from a table of standard normal distribution probabilities corresponding to the desired service level (here 99%),

MAD = mean absolute deviation of demand.

Then, to ensure that no stock-outs occur, iterative simulation runs in which the products’ individual re-order points were gradually increased were conducted until re-order point levels guaranteeing a 100% service level were found. This approach clearly results in unrealistically high re-order points and inventory levels. However, the inventory levels have no impact on the results of this study as long as no stock-outs occur. The relevant parameters are, instead, the product-specific replenishment quantities.

In reality, the inventory control parameters would be regularly updated based on forecast information. However, since only products with reasonably stable demand were selected for examination, both parameter updates and forecasts could be omitted from the model. In addition, in the model, the re-order points and inventory control logic of the distributors are unaffected by their status as VMI or non-VMI distributors, although, in reality, the inventory control parameters in VMI would be determined by the manufacturer and the VMI distributor jointly.

Since identical re-order point logic is used for controlling the inventories of both non- VMI and VMI distributors, the only difference between these two types of distributors from the model’s point of view lies in how the manufacturer’s production is loaded, i.e. what demand information drives production decisions. For production scheduling the manufacturer uses the period batch control for standard products presented by Burbidge

(1994). When orders are employed, the production load for one period consists of the orders received from the distributors during the previous period. In the case of VMI, the manufacturer’s production load for one period is determined based on the distributor’s deliveries to the retail outlets, i.e. sell-through data, during the previous period. When serving a combination of non-VMI and VMI distributors, the manufacturer’s production load is formed by adding together orders from non-VMI distributors and sell-through data from VMI distributors.

The manufacturer ships products to the distributors on a daily basis according to orders or determined replenishment needs. In the simulation runs, a weekly, bi-weekly or monthly planning and production cycle is employed. Manufacturing is not capacity constrained. In the beginning of each simulation run, the manufacturer’s safety stock is initialized to be high enough so that all orders and replenishment requirements can be fulfilled. Again, iterative simulation runs are used to identify this level of safety stock. The exact amount of inventory does not impact on the results as long as no stock-outs occur. Variables

The study looks at the impact of three variables on the bullwhip experienced by the manufacturer. Firstly, the impact of the level of VMI adoption among the distributors on the manufacturer’s production is examined. This is done by increasing the number of VMI distributors one by one and by measuring the impact on the manufacturer’s production load variability. Secondly, twenty-one products are examined to establish how a product’s replenishment frequency as determined by its average demand in relation to its minimum replenishment batch size affects the value of information sharing. Thirdly, three different production planning cycle lengths – one week, two weeks and four weeks – and their impact on the value of increased demand visibility are examined. In order to study all combinations of variables, a total of 252 simulation runs were conducted (see Figure 4).

VMI adoption among distributors 4 situations: no VMI customers, 1/3 VMI customers, 2/3 VMI customers, and all customers involved in VMI Product’s replenishment frequency 21 products with replenishment frequencies ranging from 1,5 to 4,5 replenishments per week on average Manufacturer’s production planning cycle 3 planning cycle lengths:

one week, two weeks, and four weeks

x x = 252 simulation

runs

Figure 4. Simulation runs conducted in Study 1.

The dependent variable, bullwhip, is measured as the relative or absolute standard deviation of the manufacturer’s production load. Bullwhip is defined by Fransoo and Wouters (2000) as:

in out

c c

bullwhip= , (1)

where cout and cin represent the relative standard deviation of demand measured upstream

in the supply chain and downstream in the supply chain, respectively. The relative standard deviation c is defined as the standard deviation (σ) of demand (D) divided with the mean demand (µ) for a specified time interval [t, t+T]:

)) , ( ( )) , ( ( T t t D T t t D c + + = µ σ . (2)

In this study, c_in is the relative standard deviation of demand at the distributors, i.e. the orders placed by the retail outlets, and cout is the relative standard deviation of demand at

the manufacturer, i.e. the orders placed by non-VMI distributors and the sell-through data of the VMI distributors. Since the manufacturer’s production is unconstrained and loaded directly with the demand, the relative standard deviation of the production load is equal to cout. In addition, since the same retail outlet demand data is used in all simulation runs, cin

does not change between runs and does not need to be discussed when presenting the results. Therefore, from here on, the standard deviation of the production load, either relative or absolute, will be used to express the different factors’ impact on the manufacturer’s production and inventory control efficiency. Reducing production load standard deviation is key to achieving lower inventories, improving delivery accuracy and improving capacity utilization.