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

Discussion

The industrial relevance of lean, agile, and hybrid manufacturing systems is clear, but there is a need to unpack these ambiguous terms, if integration of these conflicting paradigms is to be logically developed to meet specific business needs. An analysis and discussion of tradeoffs based on variation, inventory, and capacity follows.

Our results can be compared with Goldsby et al. with respect to the economic performance of MEs, although they used DES and we used MIP to model the SC. Nevertheless, both analyses determined the operational costs. The lowest costs were produced by the agile approach, followed by the lean approach (5% over) and the BC-SC approach (14% over). However, Goldsby et al. showed only a small difference between the lean and agile costs (e.g., only 2%) and a hybrid with 15% higher costs. Although the differences in percentages between our costs were larger, both results are close in terms of magnitudes and trends. Furthermore, we found that the operational costs agree with the results of Narasimhan et al., in which lean costs were the lowest, closely followed by agile costs. Goldsby et al. analyzed the concept of the "order-to-ship time" as a customer service parameter. Also, Qrunfleh et al. explored the ability of lean and agile systems to increase responsiveness and waste elimination. They found that an agile SC strategy contributes significantly to building SC responsiveness. Contrarily, no significant relationship between a lean SC strategy and SC chain responsiveness was found. However, a lean SC strategy has a significant relationship with waste elimination. In our study, we did not explicitly model delivery time, although order fulfillment or over/under production attempts to capture this. They stated that lean exhibits three times lower order-to-ship times than the hybrid (i.e., BC-SC) approach and eight times slower order-to-ship times compared to the agile approach, without mentioning backorder numbers. We considered customer service parameters as order fulfillments, as did Babazadeh et al. Nevertheless, our results show that the lean approach produced double the number of backorders of agile, which agrees with the results of Narasimhan et al. and Enriquez et al., which showed higher delivery speeds and delivery reliability for the agile approach over the lean approach. These experimental results are in great tuning with manager perceptions, which strongly believes that lean SC strategy is mostly focused on waste elimination rather that responsiveness improvement; this is the capital emphasis of agile SC strategy.

The lean supply paradigms perform better with a low variation of products enabling flow, and reducing the need for buffer or protective inventory and capacity. However, with the growth in products innovation and demand uncertainty, supply chains need to strategically locate inventory and capacity to enable the flow of production. However, our results indicate that lean accumulates more inventories than agile. Also, Goldsby et al. found more inventory in agile than in hybrid, but we found the opposite. The differences in inventory can be related to our over and under-demand lumber production constraints, which cannot be stated in a DES model as explicitly as in an MIP model. There are also ship-to-order and order fulfillment conceptual differences between the studies. However, our results for the order fulfillment, overproduction, and underproduction of lumber and inventory are in agreement with the results of Hallgren et al., which showed that the lean approach had a significant impact on cost efficiency, while the agile approach did not. However, the agile approach had a higher delivery performance (e.g., order fulfillment).

In an effort to measure the impacts of demand variation and batch size, we tested the impact of lumber demand scenarios on MEs. Unfortunately, we found a few quantitative and theoretical research to compare with, so we can only benchmark our results with them. Our results show that the agile approach has the highest profit, closely followed by the lean and BC-SC approaches. Goldsby et al. showed that the agile and lean approaches were equally cost-efficient, while the hybrid approach was by far the most expensive (the most cost-efficient ME was assumed to be the one with highest profits). Enriquez et al. found that the agile system is more attractive when one of the demands is low and there is a high variation of demands, which is equivalent to small batches with high variation in our study. Our results showed that the agile and lean approaches continued to have higher profits than the BC-SC approach for large batches without considering the effect of demand variation. When lumber demand showed low variation, no change was observed between the agile and lean profits, while the profits of BC-SC decreased by half. These profits are comparable to those identified by Christopher et al. While the lean approach was expected to have higher profitability than the agile approach for this type of lumber demand, instead it showed the same costs as the agile approach. However, when lumber demand shows high variation, differences in profits were tiny, with the agile and BC-SC approach were leading profits, followed closely by the lean approach. These profits are in agreement with those of Christopher et al. The BC-SC approach had the highest profits for product demands characterized by high variation and high volumes.

For small batches, the agile approach had higher profits than the lean and BC-SC approaches, but with larger profit differences. Our results for high variation again agreed with those of Christopher et al., who stated that "agile profitably responds to high variety and low volumes". However, for low variation, my results did not show that the BC-SC approach had a higher profit as Christopher et al. suggested, probably because we did not explicitly work with mixed portfolios of demands, where the hybrid approach is supposed to perform better.

In compliance with the economic penalties used for over and under-demand and capacity usage deviations, the agile approach always showed lower costs than the lean approach, which was able to use more capacity and deviate farther from demand, while compromising costs. The BC-SC approach showed the highest costs because log demand was a forecasted average per period and was not exactly the required log demand. As a consequence, order fulfillment (%) and lumber production deviations were higher than they were in the lean and agile approaches, where timber was pulled directly from sawmills every period.

The models determined the expected result patterns with an objective function as a central driver plus constraints as second order drivers. Although Al-Aomar et al. claimed that lean objectives are in conflict and a multi-objective formulation is required, we showed that ME attributes, such as customer service, chase and level manufacturing strategies, can be modeled with MIP, as Babazadeh et al. did when modeling the design of an agile SC. Alternatively, if agile, lean, and hybrid approaches are required to be measured in the short term, their stochastic shop floor techniques could be modeled with DES and VSM, and multi-objective techniques.

Lumber production outsourcing, interchanges of lumber between sawmills, over/under capacity usage, and over/under demand features increase the ability of our formulations to represent MEs. Although over/underproduction capacity usage penalties did not play a large role in costs, because their related constraints were too tight, these constraints actively helped to model chase and level manufacturing strategies. However, over/under demand lumber production constraints played a large role in relaxing and controlling order fulfillment and in controlling lumber production, which is another central feature of MEs.