Monthly Archives: November 2016

Efficiency and Performance measurement: Application of DEA in Forest products Industry

by Gaurav Kakkar, kakkarg@vt.edu

Forest products industry is extremely complex by nature. With an aim to develop sustainably, this mature industry have to deal with demanding and wide variety of performance measures. Thus determination of efficiency in such situations is highly difficult. But with the ever growing completion, the companies to need to be efficient in order to compete and survive even with low net profit margins (Sporcic & Landekic, 2014). In such cases where the business work in such complex environment, independent efficiency measures might become isolated approach for business assessment. Rather comparison of performance between firms operating on a similar transformation process might be more useful. This is measure of relative performance (Salehirad & Sowlati, 2006). Relative efficiency is measured by the ratio of relative efficiency of weighted sum of outputs and weighted sum of inputs. The basic requirement for this computation is a set of predefined weights across all units. This becomes a difficulty while obtaining common set of weights. Even after selection of weights the units of parameters become an issue.

Data Envelopment Analysis (DEA) is one of the optimum tool handling such measurement problem. Developed by Charnes et al. (1978), the approach measures relative efficiencies of individual decision making units (DMU) by optimizing weighted output/input ratio. The transformation process is driven by the actions of DMU. While measuring efficiency, it can be multiple peer entities with same transformation process (for eg. Saw mills using similar techniques in the U.S.) or a single entity with different resource utilization over time. DEA is a non-parametric approach, i.e. the inputs, outputs related to the transformation process need not to have the same units of measurement (Triantis, 2012). In other words, for example labor hours, capital investment, overtime, number of machines, board feet of finished lumber, electricity consumption, CO2 emissions, waste generated etc. can be used in their original units while measuring the efficiency of the operation. This removes the need to convert all the inputs and associated outputs measures to a uniform unit. Another feature of DEA technique is weights optimization. Unlike other efficiency measurement techniques like regression analysis, there is no need to assign predefined weights to the parameters of the process. The approach sets up a frontier of efficient DMUs using relative efficiency measures (Triantis, 2012). There are different mathematical models to conduct DEA analysis but CCR (Charnes, Cooper and Rhodes) model (Charnes, Cooper, & Rhodes, 1978) and BCC (Banker, Charnes and Cooper) (Banker , Charnes, & Coooper, 1984) are most frequently used. According to Farrell (1957), technical efficiency represents the ability of a DMU to produce maximum output given a set of inputs and technology (output oriented) or, alternatively, to achieve maximum feasible reductions in input quantities while maintaining its current levels of outputs (input oriented).

DEA is specifically applicable in cases where there are no clear success parameters, and when same efficiency can be achieved using different resources combinations. Thus in such cases, measuring the degree of efficiency individual entities in relation to others acting in the similar conditions with same transformation process might be of more interest. It have been widely applied in different areas for measuring productivity and efficiency. It has also been used for making comparisons between organizations, companies, regions and countries. Application also extends in banking, agriculture, wood industry, management of renewable resources, schooling, etc. for evaluating business performance (Sporcic & Landekic, 2014). The organizations can thus learn from the best performing peers and adapt to move towards the efficiency frontier. The outputs of the analysis, depending upon the method used, also gives the excess of inputs or deficiency in outputs in comparison. That is the measure of technical efficiency. The similar analysis when coupled with the unit cost information can be used to draw allocative efficiency. These efficiency measurements and comparison with the frontiers can be used to develop strategy and benchmark performance goals and objectives.

The following section highlights few examples as possible application of this technique in efficiency measurement of different features of forest products industry.

  • Sporcic & Landekic (2014) applied this technique to measure productivity and efficiency of 48 forest management offices in Republic of Croatia. All the offices were managed by Croatian Forests Ltd. and were responsible to manage 80% of the national forest cover. The authors used most commonly used DEA models, CCR and BCC to evaluate relative efficiency. Table 1 lists the inputs and outputs used by the authors.

Table 1 List of Inputs & Outputs used by Sporcic & Landekic (2014)

Inputs Outputs
Land, (forest area in thousand hectares) Revenues, (yearly income in hundred-thousand croatian kunas)
Growing stock, (volume of forest stock in cubic meters per hectare) Timber production, (timber harvested in cubic meters per hectare)
Expenditures, (money spent in hundred-thousand croatian kunas) Investments in infrastructure, (forest roads built in kilometers)
Labor, (number of employees in persons) Biological renewal of forests, (area of conducted silvicultural and protection works in hectares)

The results included the global technical efficiency (using CCR model), local pure technical efficiency (using BCC model) and determine scale of the transformation process. The authors also calculated efficiency frontiers, number of efficient units, identify sources and values of inefficiencies and impact of structural characteristics of forest offices (number of employees, growing stocks and surface area) on their overall efficiency. The results showed that 31% of the DMUs were found efficient according to CCR model and 50% using BCC model.

  • Upadhyay, Shahi, Leitch, & Pulkki (2012) used DEA in analyzing 24 lumber mills in northwestern Ontario, Canada to measure relative technical efficiency from data over the period of 10 years (1999-2003 and 2004-2008) using the average values for each period. Table 3 lists set the 4 inputs and 1 output used in the study. The authors conducted two set of analysis with and without using energy as an input.

Table 2 List of Inputs & Outputs used by Upadhyay, Shahi, Leitch, & Pulkki (2012)

Input
Materials (Log volume) Labor (man-hours)
Energy

Hog-fuel

Electricity

Output
Lumber Volume

The results of DEA show that while some mills (DMUs) improved their performance over the two period with limited resources, others saw a decline in their performance. With considering energy as an input, more mills reported a negative change in the efficiency. One of the probable explaination by the authors for decrease in the efficiency is reduced production in the second period. Those mills failed to adjust their inputs (mainly labor) and were running with more than required resources.

  • Runsheng (1998) demonstrated the application of DEA to conduct production efficiency analysis on 65 mills producing unbleached linearboard sector in North America in 1994. Authors used 8 inputs and 1 output (Table 3) to measure the economies of scale, technical efficiency and allocative efficiency. The results showed that most of the analyzed DMUs were technically efficient but only few maintained allocative efficiency. The analysis also showed that most of the mills demonstrated constant returns to scale.

Table 3 List of Inputs & Outputs used by Runsheng (1998)

Inputs
Fiber (BDST/FST) Labor

Operating labor (MH/FST)

Salaried (MH/FST)

Chemicals (lb/FST) Materials (unit/FST)
Fuel (MCF/FST) Delivery (mile/FST)
Power (kWh/FST)
Outputs
Finished short ton production (Annual)
BDST= Bone-dry short ton, FST= Finished short ton, MCF=1000 Cubic feet, MH= manhour

Thorough just these three examples, it’s clear that Data Envelopment Analysis (DEA) is a powerful tool for relative efficiency and performance measurement. Though its application in forest products industry is fairly limited as of now as compared to other industries (Sporcic & Landekic, 2014) but it certainly has the potential to be useful tool for resource management and strategy design for the U.S. Forest Products Industry.

References

  • Banker , R., Charnes, A., & Coooper, W. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 1078-1092.
  • Charnes, A., Cooper, W., & Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units. European Journal of Operational Research, 429-444.
  • Farrell, M. (1957). The measurement of productivity efficiency. Jouranl of the Royal Statistical Society, 253-281.
  • Runsheng, Y. (1998). DEA: A new methodology for evaluating the performance of forest products producers. Forest Products Journal, 29-34.
  • Salehirad, N., & Sowlati, T. (2006). Productivity and efficiency assessment of the wood industry: a review with a focus on Canada. Forest Products Journal .
  • Sporcic, M., & Landekic, M. (2014). Nonparametric Model for Business Performance Evaluation in Forestry. In J. Awrejcewicz (Ed.), Computational and Numerical Simulations. InTech. doi:10.5772/57042
  • Triantis, K. (2012). Engineering Applications of DEA. In Handbook of Data Envelopment Analysis. Kluwer Publishers.
  • Upadhyay, T. P., Shahi, C., Leitch, M., & Pulkki, R. (2012). An application of data envelopment analysis to investigate the efficiency of lumber industry in northwestern Ontario, Canada. Journal of Forestry Research, 657-684.

 

 

Value Stream Mapping: Visualization of Operations

Gaurav Kakkar, email at kakkarg@vt.edu

A value stream of a product is all the actions associated with conversion of supplier filled raw materials to consumable finished product. It includes all the internal activities that must be performed within each tier of supply chain to make the final product (Rich, et al. 2006). Value Stream Mapping (VSM) is a portfolio of techniques to analyze this flow of material and information in a production system from door to door operations. The exercise includes mapping all the processes involving flow of information or materials in the company. It is suitable tool to facilitate decision makers, operators to visualize and analyze the operations to recognize waste and identify its causes. Thus it can be used to view and diagnose current status and develop strategies for future improvements making it an effective method for illustrating and redesigning the value streams. The method originates from the Toyota Production System (Ono 1988) and consists of two main phases: value stream analysis and value stream design. The first phase aims at visualizing the current value stream and the second aims at identifying the wastes.

There are three types of operations being undertaken at a facility (Monden 1993):

1. Non-value adding (NVA)
2. Necessary but non-vale adding (NNVA) and
3. Value Adding (VA)

NVA activities like waiting times, double handling etc. are pure wastes and involves unnecessary actions. NNVA, on the other hand are wasteful but are necessary to support the value adding operations and making of the product. It is impossible to eliminate them completely. The value adding operations, VA involves conversion of processing of raw materials or semi-finished products. VSM helps in categorizing the production operations into these categories and obtain a complete systemic view. Toyota Production System (Ono 1988) identified following 7 wastes which prevents a system a go leaner:

1. Overproduction
2. Waiting
3. Transport
4. Inappropriate processing
5. Unnecessary Inventory
6. Unnecessary motion
7. Defects

Hines and Rich (1997) and compiled seven tools of value stream mapping that can be used to target each of the above mentioned seven manufacturing wastes. Each of these tools have varying application in identifying the wastes and thus can be used in combination with each other. Below is the brief introduction of these tools.

1. Process Activity mapping: In this mapping methodology, a vital piece of raw material is followed in the entire production process. Those steps in conversion process that add value for which the consumer would be willing to pay are called Operations and rest non value adding are classified as waste. The map can be used to identify value adding fraction of total time that the material spends in the production system.

2. Supply Chain Responsiveness matrix: This mapping technique uses cumulative inventory in days at every stage of the production process and plots it against the time to plan and move from one stage to another. The resultant graph can be used to identify problems in material flow.

3. Product Variety funnel: This methodology is applicable to production systems involving standard raw materials and a variety of final product types. By tracking the production process, the management can identify the point up to which the manufacturing stays generic without differentiation. Maintaining a generic stock buffer till the point allows that allows rapid expansion and finish products to fulfill consumer orders.

4. Quality Filter mapping: This mapping technique highlights waste generated in each stage of production. This waste can be scrapped product, rework or service defects. Such a mapping can facilitate managers to focus on stages to make quality improvements and eliminate waste.

5. Forrester Effect mapping: This mapping highlights delays with scheduling and actual production with respect to demand fulfillment. It compares demand forecast, actual shipments, launch of production batches and ordering of raw materials in a line graph. A perfectly lean system would have a series of flat lines.

6. Decision Point Analysis: This is a great tool to identify ‘make to order’ points in a production system. It uses the total production times through the factory and the waiting time the customer would accept. It is mostly applicable where the aim is to reduce lead time for customers.

7. Overall Structure maps: This tool is different from the other mapping tools as it shows the number of suppliers for each stage of production, companies engaged in distribution channels of the firm and the value each one of them adds to the final product.

While none of these tools provides a sufficient and robust solution to all of the management problems, an optimal combination of them can be used to address the key issues. Table 1 shows the application and level of usefulness of these tools to eliminate each of the seven wastes and characterize the system.

Table 1. Value stream mapping tools and their application

Mapping tools
Waste/structure Process activity mapping Supply chain response matrix Production variety funnel Quality filter mapping Demand amplification mapping Decision point analysis Physical structure

Volume and value

Overproduction L M L M M
Waiting H H L M M
Transport H L
Inappropriate processing H M L
Unnecessary inventory M H M L H M L
Unnecessary motion H L
Defects L H
Overall Structure L L M L H M H
Notes: H= High correlation and usefulness

M= Medium correlation and usefulness

L= Low correlation and usefulness

Source: (Hines and Rich 1997)

The process targets lean, agile and pull controlled value chains with shorter lead times and reduced inventories (Rother 2003, Nash and Poling 2011). The research team at SIM is extending this knowledge to the secondary forest products industry in support their attempts to go lean.

References

  • Hines, Peter, and Nick Rich. 1997. “The seven value stream mapping tools.” International Journals of Operations and Production 46-64.
  • Monden, Y. 1993. Toyota Production System: An Inegrated Approach to Just-in-Time. 2nd. Cambridge, MA: Productivity Press.
  • Nash, Mark A, and Sheila R Poling. 2011. Mapping the Total Value Stream. CRC Press.
    Ono, T. Toyota. 1988. Production System: Beyond large-Scale Production . Productivity Press.
  • Rich, Nick, Nicola Bateman, Ann Esain, Lynn Massey, and Donna Samuel. 2006. Lean Evolution: Lessons from the Workplace. New York: Campridge University Press.
  • Rother, M. 2003. Learning to See: Value Stream Mapping to Add Value and Eliminate Muda. Lean Enterprise Institute.