After the data was collected through focus group interviews, time studies, and inventory statistics, the data were analysed to conduct a VSM and further, complete the aim with this study which focused on non-value added activities, cause to waste, and improvements (Figure 9).
Figure 9. Overview of inputs, main analysis methods, and output within this study.
The model above describes the analysis of data within this study consisting of three main stages: VSM, calculating lead times, and perform an ABC cross-analysis. The analysis led to the identification of non-value-added activities, causes to waste, and classification of products in terms of importance.
4.4.1 Value stream mapping and lead time
Value-stream mapping was conducted to identify certain steps and activities that occur within the process of picking orders. The activities were divided into value-adding activities, non- value-adding activities which are necessary and non-value-adding activities that are not necessary. To create a value-stream map and an overview of the flow, time-studies and focus group interviews were conducted. Both to achieve a comprehensive view of the process as a whole and the required time it takes from retrieving the pick lists until the order has been fully prepared for loading. As a result of the time study, lead times were calculated as previously been described in section 4.3.2. By performing focus group interviews, time studies, and inventory statistics, causes to waste were identified as to why non-value-added activities occur. In addition, it was found to be interesting to look deeper into the properties of the packages. With the help of the inventory software and gathered statistics, packages were filtered according to different attributes. The main attribute that was investigated was the number of times packages been moved. Packages that is been moved more than 15 times were compiled and further analysed regarding possible reasons as to why they been handled numerous times. Possible reasons were identified on the behalf of focus group interviews, the empirical
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background, and time-studies when truck operators were picking orders. To better visualize the underlying causes of why waste occurs, an Ishiwika diagram was created.
4.4.2 Performance measurements of the order picking process
To measure the performance of the order picking process, three main key performance indicators were used which have been developed and reviewed by Frazelle (2016) and Kusrini
et al. (2018). The indicators analysed were financial, productivity, and cycle time. Cycle time
was an important indicator to use since Kusrini et al. (2018) claimed it as the most important one to consider. To calculate the financial aspect, the cost per man-hour was set to 70 € including salary to truck operator, fuel, and maintenance cost. Equations used for calculating the performance indicators can be found in Appendix 2.
4.4.3 ABC analysis of product categories
An ABC cross-analysis was conducted to divide the product range according to their importance and reasoning about inventory layout and material management. Firstly, the products were divided into four categories: impregnated, planed, finger-jointed, and painted products. The ABC analysis was carried out for each category alone. Each product's demand during the period 1st July 2019-31st December 2019 was summarised in Excel-sheets. So was the number of times each product been ordered during the same period. The annual demand and order frequency for each product was then multiplied with each other according to equation 1. 𝐷𝐷𝑝𝑝× 𝑂𝑂𝑂𝑂𝑝𝑝 = 𝑊𝑊𝑊𝑊𝑝𝑝 (𝑚𝑚3) (1)
Where
Dp = Demand for product p
OFp = Order frequency for product p
WVp = Weighted volume expressed in cubic metre (m3)
The equation resulted in a weighted volume expressed in cubic metre. If the equation only was based on volume figures, the analysis had not accounted for order frequency. The order frequency was argued as important as it is presumed to correlate against the number of times each compartment is visited. Initially, the number of products within each category varied to a large extent, hence a selection of products was made after the weighted volume was calculated. The reason why a selection was done, was because several of the products are not produced against stock and are considered as special orders. Depending on the number of products it was within each category, the selection criteria varied (Table 6).
Table 6. Selection criterion and number of products before and after
Planed
products Impregnated products Finger-jointed products products Painted
Minimum weighted volume (m3) ≥ 3000 ≥ 600 ≥ 30 ≥ 15
The original number of products 195 93 54 33
Number of products after selection 38 21 41 24
The minimum weighted volume was determined by the number of original products and the proportion of special ones which most often are manufactured against customer order. Within category planed products, 38 was selected. 21 was selected within impregnated products and 41 respective 24 was selected within finger-jointed and painted products.
The next step was to express the weighted volume in percentage, witch calculation is expressed in equation 2.
23 𝑊𝑊𝑊𝑊𝑝𝑝
∑𝑛𝑛𝑝𝑝=1𝑊𝑊𝑊𝑊𝑝𝑝× 100 = 𝑊𝑊𝑊𝑊𝑝𝑝 (%) (2)
The weighted volume was divided into the total weighted volume and further multiplied with 100. Further, the cumulative weighted percentage was calculated. The cumulative weighted percentage acted as guidance to classify the products into A, B, and C. For a detailed view of the calculations and classification of products, see Appendix 3.
4.4.4 Improvements
By identifying non-value-added activities and causes of these wastes, including performing an ABC cross-analysis, improvements could further be discussed. A studied theory such as principles of LAVC acts as guidance towards improvements and by identifying causes, suggestions on how to make the material flow more efficient could be stated. The improvements include reduction of lead-time and costs if the non-value-added activities are eliminated and how to, at least, reduce the amount of waste by implementing a more efficient material management.