Modeling Lean and Agile Approaches: A Western Canadian Forest Company Case Study

Results

For the sake of simplicity, the averages of the optimization models’ economic outcomes were aggregated by: manufacturing environment, batch sizes, and demand variation inside the batch. Incomes for the lumber demand scenarios were similar; however, operation costs guided by manufacturing drivers determined different profit and performances (see Table A8, and Figure 2). A descriptive results analysis per ME and LDS follows.

Figure 2. Profits variation between MEs and lumber demand scenarios (LDSs).

Results for MEs

On average, the agile approach was the most profitable approach, followed by lean (3.9% below agile) and BC-SC (6.4% below agile). Regardless of the variation, for large batches, agile was still the most profitable, followed by lean (1.6% below agile) and BC-SC (6.3% below agile). For large batches with low variation, the agile and lean approaches obtained almost equal profits, followed by BC-SC (11.1% below agile and lean). For high variation, agile and lean profits were almost equal, followed closely by BC-SC (2.6% below lean and agile). Regardless of the variation, the differences were larger for small batches than for large batches, where the agile approach remained the most profitable, followed by the lean (7.1% below) and BC-SC (13.8% below) approaches. For small batches with low variation, agile still produced the highest profits, followed by the lean (6.1% below) approach and then the BC-SC (12.1% below) approach. For high variation, the differences were larger, with agile being the most profitable, followed by lean (8.0% below) and then BC-SC (15.5% below).

The agile approach led to lower harvest levels, followed by the lean and BC-SC approaches. The timber volume supplied to the sawmill followed the same trend. As a consequence, procurement and manufacturing costs followed the same order, and because the lumber demand data were the same in each LDS, incomes were similar for the agile, lean, and BC-SC approaches, although the BC-SC and lean approaches had higher costs. Over and under-capacity costs were not significant due to the low allowable capacity deviation between periods.

In our formulations, overproduction was paid for with lower prices, while underproduction was penalized by charging a cost for the underproduction, which was determined by multiplying the volume of underproduction by a fraction of the lumber price. This mechanism was efficient at controlling production, and brought flexibility that was translated to a customer service emphasis in all of the approaches. The agile approach had the smallest magnitude of lumber over and underproduction, with the exception of large batches with low variation. Lumber production was mostly below lumber demand. The lean approach produced large volumes over and under the demand for most of the small and large batches. The BC-SC approach produced large over-demand volumes for most of the small and large batches, with the exception of large batches with low variation.

In terms of order fulfillment, the agile approach led to production levels being as close as possible to demand, with 89%, 99%, 99%, and 98% of demand met by production for large batches with low and high variation and small batches with low and high variation, respectively. For the lean approach, order fulfillment was less stable, with 91% and 92% of demand met by production for large batches with low and high variation, respectively, and 107% and 111% of demand met by production for small batches with low and high variation, respectively. The BC-SC approach produced over the demand most of the time, with 96% and 108% of demand met by production for large batches with low and high variation, respectively, and 114% and 115% of demand met by production for small batches with low and high variation, respectively. Consequently, BC-SC was the worst at controlling lumber production. The flow time indicates the degree of responsiveness; thus, on average, the agile approach showed 10.4 periods; the BC-SC approach showed 11.7 periods, and the lean approach showed 12.2 periods of flow time. These results agree with Narasimhan et al. [26], who found higher delivery speeds and reliability for the agile approach compared with the lean approach (Figure 3, Table A8 and Table A9 in the Appendix B).