White Paper. Data to Dollars: Leveraging data to generate optimal distribution center design solutions.

Transcription

1 White Paper Data to Dollars: Leveraging data to generate optimal distribution center design solutions.

2 Introduction 2 Many companies logistics and distribution operations have rapidly evolved due to factors that include mergers, acquisitions, consolidations, global expansion, new product introductions, import strategies, or new markets and channels. In most cases, these factors, whether independent or in combination, have had a detrimental impact on the efficiency and responsiveness of logistics and distribution operations. Companies impacted by these market and business dynamics continuously search for ways to re-align their operations to respond to these changing conditions. With that in mind, two questions arise. First, how does a company analyze and evaluate the effect of these factors on its logistics and distribution operations? Second, and more importantly, how can it utilize this information to improve its overall logistics and distribution operations performance? This white paper answers both questions through illustrating the value of comprehensive data analysis in designing the most efficient, responsive and cost-effective logistics and distribution solutions The results of this type of comprehensive analysis can yield significant operating and financial benefits that contribute directly to the bottom line. A case study at the end of this paper will illustrate how the concepts and approaches described were applied in a real world situation.

3 Where to Begin? Defining the Scope 3 Companies have a keen interest in improving their logistics and distribution operations, but may not know where to begin. The best place can be in the depths of your company s information systems. The bits and bytes of data tucked away in a company s information systems can be a valuable source of facts that represent and reveal your true business operations. However, raw data must be transformed into information to reveal opportunities to improver operating and financial performance. Therein lays the greatest challenge. Let s begin by talking about this data transformation process. The goal of improving distribution operations can be facilitated through a structured and comprehensive analysis of your company s logistics and distribution data. This analysis can be accomplished by using historical data and building a set of assumptions and projections that provide insight into the future operating environment. The historical data serves as the best starting point to identify both the root cause of inefficiencies as well as opportunities for developing operating improvement solutions. The more long term and strategically focused you are in this effort, the more those future projections come into play. Simply put, by mining key data and building projections that describe your customers, your products and inventory, customer order patterns, and other key operating metrics, a company can effectively and objectively discover ways to improve its productivity, profitability and bottom line. Before diving too deep into the data, the first critical step is to define the appropriate scope for the analysis. This can be a daunting task considering the amount of data and sources available for the analysis. However, keeping in mind the goal of what the analysis and results are intended to produce will assist in creating a well-defined, realistic and manageable project scope. A precise scope helps keep the project focused and helps avoid the all too familiar trap of paralysis through analysis. Virtually all distribution operations are faced with similar challenges, but with unique business, customer and operating requirements. Typically, there are a number of specific challenges related to capacity, throughput, service or customer requirements. For example, a project scope could be defined to improve storage capacity in an existing DC. In this case, key data sets required for this analysis would be order history, products, and inventory data from the source operations. Another area of interest could be to correctly understand the implications of increasing throughput requirements and evaluate the possibilities of leveraging automation to improve performance. In this case, extracting data for a sample period that covers the peaks, valleys and any other seasonality in your business is critical. One of the most accurate approaches for capturing these peaks, valleys, and in many cases, illuminating trends, is to analyze a full year s worth of data. As mentioned earlier, if the scope of the project is more strategic in nature, then future business projections and plans will need to be considered before completing the analysis and developing alternative solutions.

4 Identifying Data Sources Once the project scope has been defined, the next major challenge is to understand what data sources are available, what they mean and how they are related. Unfortunately, no one has built a magical black box that allows a company to simply do a data dump, and THEN be provided with output that defines the perfect warehouse design. If only it were that simple! An operating system will typically maintain a database of customers, products, shipments and inventory. These datasets are usually related and can be linked together by common data fields. For example, if the scope of the project is to reduce order fulfillment costs, you will be interested in extracting data that describes what your orders look like in terms of size and frequency. Two other key data sources are the item master and inventory file. The item master dataset will contain important SKU information that describes your product s physical characteristics, e.g., dimensions and weight, as well as other relevant data such as product families and classifications. The inventory file will provide information about how much or what quantity of a particular item is on-hand at any given stocking location. Getting It Right the First Time making sure that all of the critical data fields are populated, accurate, and truly reflect what the business operations actually experience. Data cleaning also entails the correlation of related files (for example, items in the order detail files with the items contained in the item master). The primary reason to invest the upfront time in preparing the data is to make sure that you prevent the possibility of drawing erroneous conclusions during the analysis phase due to a bad data set. Relying heavily on raw system data to feed your analysis increases the potential for achieving the undesirable axiom garbage in garbage out! One approach for validating the raw data is to compare the outputs of the number crunching efforts to sample data collected on site at the distribution center. Do your homework here and it will pay off in the end. Figure 1. Query Example for Data Cleaning 4 Now the fun part begins! Once the project scope has been determined and the appropriate data has been identified and extracted, it is imperative that significant time be invested in cleaning the data. More specifically, cleaning means

5 Analyzing the Data Now that the data cleaning and preparation phase has been completed, it is time to start analyzing the data. To analyze something means to examine methodically by separating into parts and studying their interrelations. Therefore, data analysis is the activity that transforms data into information and ultimately solutions. When various data elements are analyzed and connected, a compelling story unfolds. Focused data collection and analysis, combined with a structured process, will help exploit the data for maximum benefit. The results of this process will produce an objective basis for making accurate assessments, drawing intelligent conclusions, and developing and implementing corrective actions. This integrated approach will achieve the overall goal of the data analysis project improving operating efficiency and performance. One of the best ways to understand and interpret what the data is communicating about your business is to build profiles around key operating activities. In simple terms, a profile is a statistical distribution of some type of activity in the warehouse. The advantages of going through the activity profiling process include: Identifying root causes of problems Discovering potential opportunities for process improvement Ensuring that designs and plans are not built around average activity levels Stimulating creative thinking in developing solutions Before moving forward to examples in the order profiling process, it s vital to take an in-depth look at why it is important not to build design plans around average activity levels. Suppose you are looking to install a material handling system to convey and sort parcel shipments. If you look at average daily volumes over a period of time, you might determine that the system needs to be able to process an average of 8,000 cartons per day over an eight (8) hour shift. That yields an average throughput requirement of 1,000 cartons per hour. However, when you plot the daily activity level, you discover that during your peak season you process over 16,000 cartons per day. Furthermore, a more in-depth analysis reveals that toward the end of the day, you handle around 3,000 cartons per hour. If you were to define system requirements to handle the daily average, then your system would not be able to provide the throughput needed during your peaks and daily surges. An example may help to better illustrate the approach and process of activity profiling. Suppose you are a distributor of audio/video entertainment products such as CD s and DVD s. Order history data is extracted from your information systems to create a lines per order profile. The distribution of this data could be illustrated as follows: Figure 2. Importance of Collaboration to Innovation 5

6 High level analysis of this profile reveals that over 50% of your outbound parcel orders contain only a single item. Two key analytical questions to ask are: 1) why does this occur, and 2) what does this mean? The answers to these questions will provide the framework for determining how you can fix the problem or capitalize on this aspect of your business. Digging deeper into this profile yields potential root causes that could include excessive backorder shipments and indicate potential fill rate issues. Another possible root cause is that customers are not motivated or penalized to order more products on a single order. If either are the case, then further analysis and processes can be explored to remedy these problems. Conversely, if this is an acceptable fact of the business, then operational processes can be put in place to deal with the high number of single line orders such as picking and packing these items in an independent and efficient picking tour. As a result of this analysis, interpretation, and subsequent process improvement, multiple bottom line results can be achieved that include: Higher productivity Reduced labor costs Decreased congestion in pick/pack area Increased order accuracy Improved customer service Enhanced revenue accurately assessed and interpreted. Second, the information must be correctly applied to develop the right solutions. Intelligent and effective data analysis requires interpretative and application expertise. This expertise is achieved through extensive experience in distribution, deep operational understanding of data analysis, activity profiling and interpretation, and trial and error. Only then can the most appropriate and cost effective process and design solutions be generated to achieve the desired operating and financial improvement results. Leveraging your order profiles to get results is just one example of how you can turn data into dollars. A few more illustrations will help demonstrate the value of this effort by pointing to the potential opportunities and bottom line savings that can be uncovered through comprehensive data analysis and developing operational profiles: Inventory Profiles analyze on-hand inventory levels in some measure of cubic volume to reveal ways to improve space efficiency and reduce storage footprint required. Figure 3. Inventory Profiling 6 Leveraging the Data As the above example illustrates, data must be leveraged before it is useful in developing solutions. No matter what type of analysis is conducted, two key actions are required to achieve the results. First, the analysis must be

7 Leveraging the Data (continued) Figure 5. Activity Profiling 7 SKU Movement Profiles identify the placement of products within the warehouse to maximize productivity by reducing travel times and placing the most popular items in the best picking locations. Figure 4. Pareto Analysis Order Mix Distributions analyze mix of orders by handling type (full pallet, full case and broken case) to determine the most appropriate order fulfillment methodologies for each handling type that reduce labor costs and maximize customer service levels. Figure 6. Order Mix Distribution Daily Activity Profiles identify hourly peaks during the day to create an effective plan for staffing the facility. This data can also be used to evaluate opportunities to use automated material handling systems that reduce costs and increase pick/pack efficiency and throughput.

8 Evaluating Alternatives Once the analysis is complete and potential solutions have been identified, a cost/benefit analysis can be conducted to evaluate which solutions offer the greatest return on investment. Alternatives can range from process improvements to capital projects involving automation and/or technology enhancements. Those areas providing the quickest return on investment and ease of implementation can be prioritized. Moreover, the data that was used in the analysis can be leveraged to evaluate various what-if business scenarios that can further reveal operating improvement opportunities. For example, suppose you want to evaluate the impact of changes in inventory turns on storage requirements. At a current turn rate of eight (8) annual turns, you estimate that your DC can support 10% growth in volume for only one more year; after that, with continued growth, you will need to expand the facility or look for a new one. However, if you could improve your inventory turns to 10 annual turns and improve turns incrementally each subsequent year, you could stay in the existing facility for three, rather than one year. The key principles to consider when evaluating alternatives are: Comparisons must consider all cost components such as labor, space, equipment, systems, etc. Qualitative factors should be included in the comparison such as risk, flexibility, scalability, labor availability, etc. Data models should be developed to iteratively evaluate changes in parameters Sensitivities around future business and market conditions must be evaluated in light of the project goals and objectives. To illustrate this concept, consider the company that needs additional storage capacity and throughput to support its growth. The following alternatives were developed: Alternative 1 (manual) process improvements in picking methodologies and denser storage devices Advantages: minimal capital investment Disadvantages: lowest throughput, highest labor cost Alternative 2 (automated) multi-level pick to belt module with downstream shipping sortation Advantages: lowest labor cost, highest throughput Disadvantages: >3 year payback; lowest storage capacity Alternative 3 (semi-automated) SKU picking by zone with downstream sortation Advantages: <3 year payback; high storage capacity, 2nd highest throughput, moderate capital investment, flexibility Disadvantages: 2nd lowest labor cost Figure 7. Labor Analysis This company chose Alternative 3 as the best solution because it offered virtually the same headcount savings as option 2, but with increased storage capacity and flexibility to adapt to changing business requirements. 8

9 Roadmap to ROI From Strategy to Reality 9 Once alternatives have been evaluated and a final solution agreed upon, the business case for implementation must be built. The business case should include a financial qualification of the costs and benefits of implementing the solution. Qualitative factors such as risk, capacity, scalability and flexibility should also be included. The financial analysis will typically involve the calculation of the return on investment based on a series of cash flows that include capital expenditures offset by savings (such as labor) associated with implementing the solution. Figure 8. Return On Investment Implementation involves pulling together the right team and realistic schedule to execute the recommendations generated during the analysis, planning and design. Once priorities have been established and immediate opportunities identified, key tasks involved in executing the project plan include: Identifying partners Forming the team Creating the plan Building the schedule Ordering Materials Monitoring progress Testing the solution Implementing the changes A detailed analysis of your operations, properly interpreted, applied and acted upon, can yield significant cost savings. Successful execution depends upon having the right solution, resources, timely communications and thoroughly planned implementation schedule. Figure 9. Estimated Project Schedule The business case provides not only the financial justification of the project but a summary of the qualitative benefits, as well as the implications of failing to act upon the recommendations.

10 Case Study 10 One of the best ways to bring these concepts together is through an actual client case study. The case study will illustrate the approach, scope definition, process, results and benefits that companies can achieve through this methodology. Project Background ABC Company is a medical devices manufacturer producing critical care products. With average annual sales growth of 8% over the last five years, ABC Company partnered with Peach State to help them assess existing distribution operations and rationalize alternative layouts and fulfillment strategies to accomplish growth objectives while reducing operating expenses. Project Objectives Eliminate off-site storage Increase storage capacity and space utilization Maximize facility throughput Facilitate material and process flow Assess implications of absorbing additional volumes into single facility Solution Methodology ABC Company and Peach State jointly developed planning parameters and design criteria for the project. Extensive historical data analysis of orders, SKUs and inventory levels was performed to develop an objective basis for facility capacity, current and future, and order fulfillment requirement. Figure 10. Warehouse Capacity Analysis Alternative solutions were evaluated to address storage, order fulfillment and throughput needs of the future. A number of alternatives were evaluated from retrofitting the existing DC to a fully automated warehouse expansion. The final solution focused on expanding ABC Company's customer direct throughput capacity and optimized economic, space and operational considerations.

11 Case Study (continued) 11 Final Recommendations Expand current facility by 38,000 square feet to increase storage and processing capacity. Implement semi-automated zone pick and sort fulfillment system to increase productivity and throughput. Reconfigure existing storage locations into smaller slot openings to improve space utilization. Re-slot the warehouse to minimize travel time and maximize pick rates. Reduce/eliminate stocking requirements for inactive items, thereby reducing inventory carrying costs and freeing up valuable warehouse space. Solution Benefits Increases Storage Capacity By expanding the facility, consolidating two operations into one building and re-profiling storage locations, the capacity of existing building is increased by 75%. Reduces Operating Costs By increasing order fulfillment productivity by 36%, proposed material handling system is projected to reduce labor costs by $2.0 million over the next 5 years; system investment is recouped in 29 months. Recommended expansion also eliminates $150k per year in offsite storage handling expenses. Improves Customer Service Levels System will enable ABC Company to efficiently process premium service requests, extend order cut-off times and reduce order turnaround time. Adds Operational Flexibility Proposed concept allows ABC Company to effectively manage resources and daily operating plans by providing a flexible solution that can accommodate all customer and freight types, including direct, dealer and international orders.

12 Conclusion 12 In summary, data can be converted into dollars when properly analyzed and applied to identify distribution operation inefficiencies and create cost saving opportunities and solutions. While the task has its challenges, detailed data analysis far outweighs the risk of attempting to solve your problems based on experience or conjecture alone. The rewards of objectively assessing your business through this analytical process can effectively turn 'data to dollars' that generates significant return on investment and bottom line improvement. About The Author Wayne Lewis serves as Senior Director, Global Consulting & Engineering at Peach State Integrated Technologies, Inc. Wayne leads Peach State s consulting group, responsible for network optimization, distribution center design and operational excellence projects. Since joining the company, he has successfully completed numerous supply chain projects for a variety of clients. Wayne has over 13 years of hands-on, operations management experience. Prior to joining Peach State, he was a General Manager for Exel Logistics. He also previously worked with Datex-Ohmeda, where he managed the integration of an automated storage and retrieval system into existing warehouse processes and information systems. At Peach State, Wayne develops distribution network and order fulfillment solutions for clients in healthcare, retail, consumer products, parts distribution, and many other industry verticals. Key areas of focus include network modeling and design, data analysis and profiling, methods and process improvement, labor modeling, picking strategies, storage planning, conceptual design, and evaluation of alternatives. Wayne received a Bachelor of Science degree in Industrial Management from The Georgia Institute of Technology. He later received professional certificates in material handling and warehousing from The Logistics Institute. Peach State provides end-to-end supply chain consulting, engineering and systems integration solutions that increase productivity, reduce cost, mitigate risks and improve customer service for clients that manufacture and distribute their products across the globe. Peach State Integrated Technologies 3005 Business Park Drive Norcross, GA, P peachstate.com