Case Study 2.1 – Assessment of Supply chain efficiency by DEA

University of the Potomac

CBSC-520

Abstract

Conduct a study on the assessment paper of hardwood sawmills prepared using Data Envelopment Analysis (DEA) to identify supply chain inefficiencies when hardwood sawmills were under severe financial pressure. The New York state had about 175 hardwood sawmills and the study was conducted on 15 sawmills. The collected data samples first computed using DEA and then Pearson Correlation Analysis to compare sawmill assessment scores and relative assessment scores to draw correlation.

About Data Envelopment Analysis (DEA)

Data envelopment analysis (DEA) is a nonparametric method in operations research and economics for the estimation of production frontiers. It is used to empirically measure productive efficiency of decision making units (or DMUs). Although DEA has a strong link to production theory in economics, the tool is also used for benchmarking in operations management, where a set of measures is selected to benchmark the performance of manufacturing and service operations.

Data envelopment analysis (DEA) is a linear programming methodology to measure the efficiency of multiple decision-making units (DMUs) when the production process presents a structure of multiple inputs and outputs. Non-parametric approaches have the benefit of not assuming a particular functional form/shape for the frontier; however they do not provide a general relationship (equation) relating output and input. There are also parametric approaches which are used for the estimation of production frontiers.

Study and Observations

The study was performed on seven areas of supply chain management in sawmills, a questionnaire was developed for each area to analyze each category in supply chain.

Operational Planning: This section looked at the organizational structure, goals and objectives, sales, and operations planning and forecasting. The scores ranged from 54 to 92 percent, with an average of 77 +/- 10 (SD) percent. In the study only one sawmill had written objectives which is not a good practice. The operational planning is given 15 percent weightage and an average of 77 percent was observed.

Procurement: In this area the questionnaire focusses on the log supply agreement, supplier’s performance, and timber in stock already. Procurement is given the highest importance in the supply chain and shares 30 percentage of weightage and study shows an average of 87 percentage across sawmills but none of the sawmills had a supplier’s performance program in place which could help in reducing the cost and increase efficiency.

Production: Investment in new equipment, scope of production of demand increases, and any contingency plans in place information is collected. Production is the next important aspect in supply chain which chares 25 percent and an average of 83 percentage with 10% SD was seen across sawmills. There was a production shift from high grade lumber to low grade lumber which results in low profit margins. Much of the technology in place at these sawmills is 10 to 15 years old and were customizing more orders for their customers impacting their production negatively. Many of these sawmills did not scale back their existing production footprint to meet the declining production volumes during recession.

Transportation: In this area any plan to analyze the fuel efficiency, cost of freight, and software to maximize shipping are in place. No sawmills were using software to analyze shipping and transportation costs. “In the next 10 years, petroleum-based energy products are predicted to continue to climb in cost” (Leder, Shapiro 2008). Many are negotiating flat-rate haul costs from their transportation providers in order to control freight expenses. The sawmills scored an average of 88 percent with 14% SD.

Warehouse and Inventory management: Information of inventory like number of days lumber/logs stays in inventory, active measures to reduce inventory, and inventory classifications are collected. An average of 86 percent with 10% SD was observed in the inventory management and warehouse area, each sawmill managed their inventory in different ways. Sawmills that carried inventory were inefficient with regard to how they moved inventory throughout their operation.

Ergonomics and Innovation: Information like use of computer in capturing the information, any tool used for improvement, and any investment made on improving the supply chain are collected. This area is neglected by most of the sawmills as the results of this is not instant, no organization had a proper continuous improvement tool in place like the six sigma or lean. An average of 77% with 10% SD observed.

Environmental practices: In this area the energy utilization is measured and if any measures are taken to reduce the energy consumed were analyzed. The sawmills scored an average of 80% with 11% SD in this area and it was observed all the sawmill have minimum environmental wastage.

Summary of Analysis

The sample data collected from 15 hardwood sawmills for 7 areas related to supply chain efficiencies and their weightage w.r.t supply chain efficiency need to be relooked. Also the sample data collected during the survey showed information on improvement that did not replicate in the outcome of the report though clearly called out in their respective section:

Few examples to name are: Ergonomic and innovation section clearly states, the sawmills lacked in implementing any continuous improvement tools but yet they scored 77% and transportation scored 88% without a tool to analyze shipping and transportation costs.

Conclusion

Though study provided many pointers on improvement, somewhere they did not come out well as a statistical outcome in the report. Analysis is conducted on 15 hardwood sawmill industries which produce at least 3 million board feet of limber per year, they are a mid to large scale industries. The problem arises with the small-scale industries which fail to complete with other industries and ultimately end up in losses. May be the survey questionnaire or the math had to be revisited to figure it out. Also, the sample data collected hints towards applying parametric statistics tools, that can help draw accurate results.

References:

Beasley, J. E. (n.d.). Data envelopment analysis. Retrieved November 17, 2017, from http://people.brunel.ac.uk/~mastjjb/jeb/or/dea.html

Leder, F. and J. Shapiro. 2008. This time it’s different: An inevitable decline in world petroleum production will keep oil product prices high, causing military conflicts and shifting wealth and power from democracies to authoritarian regimes. Energy Policy 36(8):2850–2852.

“Data envelopment analysis.” Wikipedia, Wikimedia Foundation, 27 Nov. 2017, en.wikipedia.org/wiki/Data_envelopment_analysis.

Nonparametric statistics. Retrieved November 27, 2017, from https://en.wikipedia.org/wiki/Nonparametric_statistics

Parametric statistics. (2017, September 27). Retrieved November 28, 2017, from https://en.wikipedia.org/wiki/Parametric_statistics