Warehouse operator selection by combining AHP and DEA methodologies

Abstract In this paper we propose an approach for selecting the warehouse operator network by combining the analytic hierarchy process (AHP) and the data envelopment analysis (DEA). With the AHP, the alternative warehouse operators can be evaluated by using multiple, both qualitative and quantitative criteria. The outcome of the AHP analysis is a preference priority for each alternative operator describing the expected performance level.

Abstract The DEA method can then be used to solve this multi-criterion problem by developing a DEA model for measuring warehouse operators’ efficiency by using the information of the AHP analysis and combining that with data concerning the process inputs. The utilization of the proposed approach is demonstrated with an illustrative example.

Introduction The distribution centre or warehouse location problem is a strategic level network design problem (Powers, 1989). The decision is long-term and the influence on the profitability of the company will remain for years. Because outstanding service when distributing products has become one of the most important element in competition (Perkins,2004) and also because of increased computing capacity, it has been possible to introduce more qualitative aspects, e.g. customer service requirements or deliverer's own strategic views into the decision-making process in the warehouse location problem.. The validity of the input data has increased which helps to rely more on the outcome of the models.

The warehouse location problem The most traditional quantitative framework for distribution network design problem is the cost minimization approach. The focus of a more advanced warehouse location problem framework is on profit maximization. In several papers, for example Meshkat and Ballou (1996) and Canel and Khumawala (1996), customer service elements have been included in the distribution design problem, in addition to cost and profit information. Typically the service sensitive framework, includes elements like product availability, delivery time requirements and delivery frequencies.

Proposed approach Objective Warehousing activities have been outsourced. Preceded by an analysis to define the best potential location for the warehouse, to determine the feasible alternative warehouse operators and to gather extensive information on them. Objective Decide which warehouse operators of the feasible alternatives will be included in the distribution network of a company.

Preliminary analysis Stating the objectives for the warehouse network design problem Determining the best potential locations for the warehouses Defining the alternative warehouse operators on the locations Gathering and analyzing information concerning all aspects of their operations.

Defining the final evaluation problem This phase involves defining the alternative warehouse operators among which the final selection will be made. The most feasible alternative warehouse operators are defined based on the information gathered in the previous phase the warehouse operators that do not satisfy the basic requirements set on, e.g. customer service and cost level are eliminated from the final evaluation.

The AHP analysis the criteria used for analysing the alternative warehouse operators are defined, and the basic requirements concerning each criterion are defined. the criteria are structured into an AHP-hierarchy. priorities are derived for the criteria and the corresponding requirements. The focus on AHP-model is on service-related criteria.

The DEA-analysis The DEA-analysis uses the preference priorities derived in the previous phase as input. These priorities are combined with cost information in order to define the most service/cost-effective warehouse operators. The DEA-analysis is based on linear optimization and the analyses are performed separately to each warehouse.

Implementation and follow-up After implementing the decided warehouse network in practice, the AHP-models can be used for supporting periodical reviews of the actual performance of the warehouse network. The DEA-based analysis can also be used on a regular basis for analysing the actual service/cost-effectiveness of the selected warehouse operators. New alternative warehouses can also be included very easily in the analysis.

Defining the problem Production Plant Customers/ Market Areas Market Area Warehouse

AHP-based Analysis(1/4) Identify two main service aspects of warehouse performance to be included in the analysis: reliability and flexibility. Reliability : the ability to deliver products to the customers according to the target schedule, in right quantities, and without damage. Flexibility : the ability of a warehouse to arrange urgent deliveries when needed, to the flexibility of the frequency of deliveries, to the ability to conform to any special requests set by the customer, and the ability to respond to changes in warehousing capacity needs of a customer.

TO PRIORITISE THE WAREHOUSE OPERATORS BASED ON SERVICE CAPABILITIES The AHP hierarchy TO PRIORITISE THE WAREHOUSE OPERATORS BASED ON SERVICE CAPABILITIES RELIABILITY FLEXIBILITY DELIVERY TIME URGENT DELIVERIES FREQUENCY QUALITY SPECIAL REQUESTS QUANTITY CAPACITY

AHP-based Analysis(2/4) The priorities are set by comparing each set of elements with respect to each of the elements in a higher level. A verbal or a corresponding 9-point numerical scale can be used for the comparisons,

AHP-based Analysis(3/4) Use ratings instead of the actual decision alternatives at the lowest level of the hierarchy. By deriving priorities for the ratings using pairwise comparisons, the customers can define what the real utility of each performance level is for them. EX: Delivery time Using the 1-9 scale to compare the options “within 2 h” and “within 4 h”, decide which of the two options is more preferable and define how much more preferable it is.

AHP-based Analysis(4/4) Rating Delivery time: The products are usually delivered within 2, 4, 6 or 8 h of the target delivery time. Quality and quantity of deliveries: The accuracy of the deliveries with regard to the quality and quantity of products is usually over 99%, 95–99%, 90–95%, or below 90%. Urgent deliveries: Urgent deliveries can usually be arranged within 1, 2–3, 4–5 days, or over 5 days of placing the order. Frequency: The frequency of deliveries can be 3, 2 times a week, once a week, or once in 2 weeks. Special requests: The performance is at the preferred, acceptable or unacceptable level. Capacity: The ability of a warehouse to conform to capacity changes is outstanding, above average, average, below average, or unsatisfactory.

The priorities of the subcriteria

The DEA analysis Uses the preference priorities derived in the previous phase as input. These priorities are combined with cost information in order to define the most service/cost-effective warehouse operators.

The analysis of the alternative warehouse Reliability Fexibility Time(h) 0.2829 Quality(%) 0.1782 Quantity(%) 0.1123 Urgdel(day) 0.2112 Frequency 0.0761 Specreq 0.0980 Capacity 0.0412 WH1 2 90-95 95-99 1 1/W Acceptable Above average WH2 6 >99 2-3 2/W Preferred Below average WH3 4 <90 1/2W Unacceptable Average WH4 3/W WH5

The DEA analysis - input and output Alternative Directcosts Indriect costs Reliability Flexibility Time 0.2829 Quality 0.1782 Quantity 0.1123 Urgdel 0.2112 Frequency 0.0761 Specreq 0.0980 Capacity 0.0412 WH1 500 200 0.139 0.026 0.028 0.112 0.011 0.044 0.012 WH2 700 250 0.032 0.057 0.066 0.072 0.03 0.046 0.003 WH3 550 170 0.1 0.006 0.015 0.008 WH4 600 140 0.033 WH5 650 220 0.004

Efficiency of the warehouse Alternative Efficiency WH1 1.000 WH2 WH3 0.824 WH4 WH5 0.950 The warehouses one, two and four got the maximum value in the optimization. According to the DEA-philosophy the efficiency of the selected warehouses does not mean that those warehouses are absolutely efficient but those are efficient among the other warehouses.

Conclusions The proposed approach provides a systematic and flexible framework for selecting a warehouse network that maximizes the service/cost-effectiveness. The proposed approach is strongly customer-driven as the customers are provided with the possibility of presenting their preferences for the alternative warehouses in an analytic manner.