Modeling Lean and Agile Approaches: A Western Canadian Forest Company Case Study
Read this article. Both lean and agile principles were tested to determine if they improved performance in the coastal forest industry. While both agile and lean methodologies have obvious differences, in your analysis what are some similarities?
Introduction
A large number of companies are thinking to switch to lean and agile manufacturing systems. Supply chain managers strongly rely on their ability to reduce costs and waste, increase customer service, and provide a competitive advantage. Lean and agile manufacturing systems are multi-dimensional approaches that contain a variety of management practices. Resources efficiency and high performance are key stones of lean manufacturing, while the capabilities of addressing customer requirements are on the side of agile manufacturing.
Lean manufacturing can be described as developing a value stream to eliminate all waste including time, and ensure a level schedule. A manufacturing process working away from variation and uncertainty can be defined as a level schedule, which ensures high capacity utilization, thus leading to lower manufacturing costs. Consequently, lean manufacturing is a system mainly that is focused on increasing the efficiency of operations. Contrarily, agile manufacturing is a system that is capable of operating profitably in a competitive environment, where customer demands continuously and unpredictably change. Agility reacts quickly and effectively to changing customer needs in a volatile marketplace; it is able to handle variety and introduce new products. The ability to introduce highly customized products is a key component of agile systems, which is addressed with capabilities to change between products without significant investment. However, such flexibility increases manufacturing and transportation costs. Thus, a lean manufacturing approach is predominant in production systems that are focused on products that satisfy basic needs, where the products demand is stable with large life cycles. Contrarily, an agile manufacturing approach is predominant in production systems that advocate innovative products that satisfy sophisticated needs, where the products demand is almost unpredictable with short life cycles.
The adoption of emergent manufacturing systems principles should be subject to accurate economic analyses. However, there is no substantive economic evidence at the bottom of the economic forest company level to support their implementation. Hence, we reviewed the related literature to suggest a framework to address manufacturing system evaluation.
This type of systems evaluations have been performed using discrete event simulation (DES) models, measuring their advantages in different industries when adopting lean, agile, and hybrid principles, but only at the shop floor level of the factory. On the other hand, mixed-integer linear programming (MIP) models have been used to elucidate the ability of agile principles in order to improve the economic performance for supply chain network design. Strategic and tactical decisions such as opening facilities and their capacities, and selecting transportation modes were considered. Although the previous research is novel, it does not address the short-term aggregate planning problem where the ability of the manufacturing system must be measured.
Lean, agile, and hybrid manufacturing are philosophies that are easy to understand; however, their complexity appears during implementation. Goldsby et al. modeled a supply chain (SC) for lean, agile, and hybrid manufacturing to evaluate their benefits and tradeoffs. A DES model was used to simulate the SC. The results showed costs, lead times, and inventory tradeoff between manufacturing environments. However, there was no mention of how product demand patterns should be assessed in order to better represent the manufacturing philosophies. Another attempt to quantify lean manufacturing benefits was made by Al-Aomar et al. The manufacturing system was modeled with DES, and a tabu search approach (TS) was used to find the model parameters that optimized the lean measures (e.g., work in process (WIP) and lead times (LT)). As lean measures with one objective could be contradictory, a multi-objective cost function to rank solutions at each TS step was applied. Although this approach considered the effect of lean techniques on profits, it is not clear how this method balanced lean measures, as the author claimed.
Despite efforts to measure the benefits of manufacturing systems with models, most research has been conducted for only one facility, regardless of the effect on the SC, and without considering demand changes. Until now, researchers have been applying DES, value stream mapping (VSM), experimental design, and TS to find the setting that optimizes the balance between manufacturing measures. These approaches have been successful when modeling floor variables, given their stochastic nature. Unfortunately, these efforts fail to explicitly measure the impact of the manufacturing principles on economic performance. On the other hand, MIP have been used to optimize short-term production planning, where policies to manage inventory, production capacity, customer service, and lead times can be easily tested. Thus, the mid and short-term production planning problem represents a suitable scenario for testing lean, agile, and hybrid manufacturing environments.
The SC uses raw materials, which are resources with limited capacities to produce products that satisfy customer demand, optimizing the tradeoff between setup and inventory holding costs. The problem has been solved with MIP formulations. Furthermore, Billington et al. suggest that the problem begins when material requirement planning (MRP) systems assume no constraints for facilities; hence, any amount of production is presumed to be possible in each facility. However, lead times (setup and production time) can increase due to bottleneck operations, triggering unpredictable lead times. This problem has been called a capacity-constrained production scheduling problem.
Depending on the manufacturing system, different modeling approaches can be applied. A tradeoff analysis between setup and holding inventory costs should be conducted. Multiple products and capacity constraints increase the complexity of the problem. The MIP formulations usually focus on minimizing the setup and holding inventory costs, which are subject to material balance constraints, plus capacity and market constraints. However, in practice, the number of binary and continuous variables (i.e., production quantities by product and setup cost) makes the problem intractable with exact algorithms.
MRP calculates the requirements for all items, including raw materials, parts, components, and subassemblies. MRP determination is based on master planning scheduling, capacity requirement planning, and lot-sizing determinations. Lot-sizing decisions may be made based on previous requirements, and always consider materials, machines, and labor constraints. If there are capacity constraints, this problem could become a single or multiple-level capacitated lot-sizing problem.
In this context, the well-defined and rich literature on lean tools and methods is contradicted by a few documented quantitative implementations in which the value of lean manufacturing using DES and VSM was tested. A pull strategy to reduce WIP and LT in relation to a push strategy (i.e., large WIP and LT) was assessed, but there are no details on how the pull future state was developed.
Although there is evidence that lean manufacturing techniques (i.e., Just In Time (JIT), total preventive maintenance (TPM), and cellular manufacturing) improve performance in discrete manufacturing, evidence on continuous production is scarce. Aldulmalek et al. performed an analysis of a continuous manufacturing process based on DES, VSM, and historical data. They introduced buffers and scheduling around the bottleneck work station. Later, a DES was run and set up with two levels of TPM, two setup times, and push and hybrid pull manufacturing. Their results showed that pull hybrid manufacturing and TPM trigger significant lead-time reductions, as well as strong reductions in WIP.
Consequently, we sorted a hierarchically and translated lean, agile, and hybrid principles into planning drivers. As a matter of fact, the literature on manufacturing systems is extensive, yet few quantitative implementations can be found, and almost no publications are available for forest industry applications. Meanwhile, not all of the drivers that were mentioned in the literature can be translated and used in a mathematical formulation. We summarized the key drivers based on a large but not extensive literature review, as shown in Table 1 (detailed information can be found at Hallgren et al. Translation of the drivers was applied later on the model formulation of each manufacturing environment (ME).
Table 1. Attributes of lean, agile, and hybrid manufacturing supply chain (SC) drivers.
| Driver | Lean | Agile | Hybrid |
|---|---|---|---|
| SC strategy | Costs leadership, zero waste, flexibility and incremental improvements for existing product production. | Differentiation responsiveness, site of inventory capacity: responsiveness. Provides customized products with short lead times by reducing the costs of variety. | Mass customization by postponing product differentiation until final assembly. |
| Product attributes | Functional commodity: Highly predictable, long life cycles (e.g., a staple). | Innovative: uncertain demand, short life cycles (e.g., a customized laptop). | Mixed portfolio: functional and innovative components. Long–short life cycles (e.g., a car). |
| Volume-variety | Large volumes of low variety. | Small volumes of high variety. | Both. |
| Demand | Stable and predictable. | Unstable and unpredictable. | Both. |
| Manufacturing focus | Maintains high average utilization rate. Level strategy. | Deploys excess buffer capacity to ensure that raw materials are available to manufacture. Chase strategy. | Part chase strategy and part level strategy. |
| Back orders | Allowed but penalized. | Not allowed or highly penalized. | Both mixed. |
| Inventory policy | Generates high turnover and minimize inventory. | Deploys significant stocks of parts to tide over unpredictable market needs. | Minimizes functional component inventories. |
| Lead time focus | Shorten lead times as long as it does not increase cost. | Invests aggressively in ways to reduce lead times. | Lean at component level. However, at the product level, follows agile focus. |
| Decoupling Point (DP) | At warehouse site | At manufacturing site | At manufacturing site |