The client, a major automotive company, desired a new vehicle distribution system for its North American dealership network. The goal was to create a system that would be responsive to customer choices while reducing distribution costs. After comprehensively evaluating the supply chain, with an emphasis on customer satisfaction metrics, PMI developed and recommended a Distribution Center (DC) plan which optimally balanced customer needs and transportation costs. This plan demonstrated the possibility of reducing transportation costs by 25% while simultaneously improving customer service.
• Inefficient vehicle distribution system
• High inventory at point of sales location
• Low customer responsiveness
• High transportation costs
• Long vehicle delivery times
• Inadequate service levels
Vehicles manufactured abroad were shipped to multiple ports within the United States to satisfy North American demand. Dealerships received
inventory directly from the ports nearest to their respective metro area. Most transportation from portside distribution centers to dealerships was performed via road transportation (i.e. trucks).
The primary objective of the project was to improve customer satisfaction with a cost-effective distribution plan. Features of the former plan targeted for improvement included:
• High Transportation costs between ports and metro markets
• Long vehicle delivery times
• Waning customer satisfaction metrics relating to vehicle choice and availability
The client was considering the introduction of more distribution centers, closer to dealerships, as a potential strategy for improvement. PMI was tasked with both developing tools to generate and evaluate various distribution center placement alternatives, and proposing an improved distribution plan. Both the quantity and location of distribution centers were to be analyzed.
PMI’s first step was to thoroughly document the existing distribution network. To do this, a multi-step plan was initiated: First, process maps describing the customer and vehicle flow were created. Then, key contributors to customer service level and transportation costs were identified, using created dynamic and stochastic input variables. Such variables included dealer inventory control policies, truck load factors, customer demand and demand seasonality, as well as transportation delays. PMI consultants developed both a Mixed Integer Program (MIP) optimization and discrete event simulation model to represent the details of the distribution network. Results of the MIP, obtained with AMPL Plus, combined with ProModel what-if analysis techniques were used to determine the optimal number of DCs to include and the ideal locations to place them.
PMI’s MIP was developed to generate distribution center alternatives that minimized transportation-related costs per year. The alternatives were then evaluated using the simulation model, which explicitly considered the probability and dynamic elements in the system, and hence, estimated the overall effect of the given options more realistically. The client was updated on the distribution network options available to them, the expected benefits of each, and the new design recommended by PMI.
The solution outcomes demonstrated that a decentralized DC concept would achieve the designated performance criteria. Significant cost reduction opportunities relating to DC inventories and transportation modes were revealed. It was shown that, under certain circumstances, the recommended distribution network could yield over $20 million savings per year in transportation-related costs. In addition to cost savings, the distribution plan improves customer service levels by increasing the likelihood of first-choice vehicles being available and reducing the instances of lost customers.
A large discount retailer was preparing to incorporate a demand-driven scheduling system. A key parameter required for this system was accurate workload content by task for each individual department. This is a classic Industrial Engineering function and the retailer employed PMI to propose a methodology and to execute the study. While collecting this data, it was important to use Lean principles to identify opportunities to reduce waste and suggest process improvements.
Six stores across two states were studied. Within each store, three departments were studied. The departments studied were Lawn and Garden, Stationery, and Toys. The toy department consisted of the retail floor as well as an assembly area for bicycles. There was one common set of tasks which was applicable to all retail departments and a separate list of tasks for the assembly area.
The demand-driven scheduling system is highly desirable for the retail industry because it is crucial to provide customers with the desired service level, while avoiding overstaffing. Lean principles are currently finding their way into industries outside of manufacturing and the retail industry is no different. By identifying waste within a store, processes can be streamlined and process times can be minimized; thus improving the customer’s shopping experience and minimizing the associated costs to the retailer.
PMI utilized random sampling to measure the workload within each store. The study encompassed one business cycle across six different stores. A business cycle was defined as a seven day period, all hours of operation, as well as the opening and closing activities of associates.
Random sampling data was used to develop standard times for tasks. The number of items sold was used as the workload driver for each department. This data was used to develop demand-driven schedules. Several additional analyses were performed using this data including:
Analyzing the impact of government regulations for applying price tags to items versus shelves
Investigating the results of scheduling department managers during peak hours
Comparing task proportions between department managers and retail associates
The work content developed was compared between Old and New store structure
PMI provided the data required to support a demand-driven scheduling system based on workload and performed several detailed analyses on this data. This data can be used to ensure the appropriate service level is achieved, without overstaffing. Several process improvements were suggested based on lean principles which will enable the retailer to improve productivity of staff and to improve the customer’s shopping experience.
A major producer of baby-food products desired additional information about their existing bottling system and recommendations to improve production efficiency. To meet the client’s goals, PMI first simulated the existing design and then modeled several different scenarios to optimize system throughput.
The bottling system consisted of the following: Glass Depalletizer, Optical Scanner, Accumulation Table, Filler, Capper, Coder, Tray Packer, Case Palletizer, Labelers and a system of conveyors.
A new bottling system’s design called for the linking of the best equipment and technology that the company had available. However, this linkage did not exist or might have been inefficient and being run over capacity.
The main objective of the study was to understand the behavior of existing bottling systems and to assist in designing new and efficient ones. This was achieved by:
• Identifying bottlenecks and determining the level of resources necessary to maintain production targets.
• Providing accurate, objective, quantitative information to refine the process and increase productivity.
• Developing a control strategy for the system by understanding its logical operation.
First, a base model operating under original specifications and parameters was developed for evaluation. Then, alternative scenarios and suggested system improvements were modeled and evaluated to determine the line configuration that would optimize system throughput.
The process simulation allowed engineers to test the system and identify inefficiencies. This study led to the most effective system configuration by quantifying the effect of changes to the system.
A multi-billion dollar automotive supplier was outgrowing its 1.6 million square foot warehouse. With time running out and customer demand increasing, they called upon PMI to analyze the material flow within their facility. More than 10,000 vehicle instrument panels are assembled and shipped daily from this location.
Both in-house plastic injection-molded and purchased parts are stored in designated sections throughout the plant. These raw materials are brought to assembly lines via fork trucks or vehicle trains. WIP parts are placed in temporary holding areas and finished goods are stored or shipped out by truck to automotive assembly plants.
High vehicle congestion areas were scattered throughout the facility. Once controlled part routings were becoming difficult to manage with increased complexity caused by ILVS (In Line Vehicle Sequencing) strategies recently deployed by automotive manufacturers. With over 1200 named parts to move (including color and style complexities), plant engineers realized something had to be done to improve the situation.
The goals of the study were to improve the pre and post-assembly material flows within the facility. By establishing a data-driven baseline scenario, alternatives could be tested for increased efficiencies regarding material flow to and from assembly lines. First steps were to collect, assemble, and format material flow data. Material handling labor costs and resource utilization were analyzed, as well as verifying a path for every part within the operation. Secondary efforts included static and dynamic modeling to further test options for operational modes
A project team was established to conduct a detailed study of material flows connected to warehouse areas. A phased approach began with a review and overhaul of existing data, complete with all current part numbers. Part routings were verified on the plant floor, and mapped in a material flow computer model. Specific routes for high volume parts were modified to increase overall material handling efficiencies.
Over 100,000 square feet of floor space was isolated for a central marketplace where often-handled parts were managed through a WMS. A well-maintained database tracked part numbers and routings to assist in inventory planning and management.