What you will learn in this white paper

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2 Introduction You ve likely heard of big data. It s become one of those words like social media, more and more ubiquitous in our lives. Data comes from many sources, and big data is the collection of all that data and the subsequent application of it to action and decision making. It s led to many amazing applications, like racing teams that can predict who will win a race based on weather conditions and tire pressure like the McLaren racing team did in the 2008 Monaco Grand Prix, or retailers like Macy s adjusting pricing in real time on 73 million items based on demand and inventory levels. So how does big data have a role in healthcare logistics? Given the myriad of data sources in a logistics operation, from truck positions on inbound freight trips, to pick rates and distance traveled in a warehouse from RF device monitoring all this data is available and potentially powerfully useful. The challenge is to decide what s useful and what is merely just data, and then develop the systems and processes to monitor and act on that information. What you will learn in this white paper Your Customer: How your logistics operation can be applied to an improved customer experience, increase service levels, and lower cost Your Freight: The many data aspects of freight operational data that can be utilized to drive change, including improved accounting accruals, temperature tracking, security, mode shifts, accessorial fees, inbound management, and outbound consolidations Your Storage: We ll review data around space, inventory, and automation, as well as consider field inventory and the improvements possible with that hidden pools of assets Your Systems: How to use your systems to your advantage 2

3 Customer First In the process of deciding what s useful in your operational data, let s begin at the end, the customer. In healthcare operations, that customer may be a distributor, healthcare provider, retail establishment, patient or business. For the purpose of this review that isn t relevant. What does matter are key areas such as cost, service levels, and customer experience. We will begin with key questions, and then dive into the data sources that can generate that information, and review some specific examples and results. Dashboards that track cost vs. customers will allow you to focus on customers that may be costing you more than others, and allowing you to fully understand margin and/or increase price for the value-added service that you re providing. Data Check Ask yourself: Are there particular customers that cost your logistics operation more than others? Are there periods of time when the profitability of certain customers drop, due to some factor? What are your customers using as key metrics, such as % on-time and % shipped complete, to measure your performance? Are there customers that are experiencing below average levels, and what is driving that? Is there something to learn from customers reaching above average levels that can be applied? Are there data segments that will drive positive change, in improved cost, more accurate accounting, less product loss or higher customer satisfaction, from this data? One example is from a non-healthcare provider that priced product shipped to non-us customers at a discount, to compete with local products and demand. They supplied the product from their main U.S. warehouse, and didn t apply any particular shipping upcharge, allowing the international distributors to select the freight forwarder and ship collect. But many of these customers required special documentation, orders staged for weeks while customs documents were cleared, and missed appointments by unreliable customer-selected forwarders. By combining warehouse costs, inventory lag and total revenue as a three part data set, they realized these customers were operating at a loss many times, without it being noticed in margin reporting. With the addition of a shipping surcharge, they were able to stay competitive on product price, and drive customers to either streamline shipping operations or pay the premium that the level of service they were getting deserved. Service levels This analysis of service levels will lead to a factor analysis, with the multivariable inputs of inventory levels, order processing time, time of day, time of year, weather, and much more impacting the analysis. Once a customer is identified and the contributing factors determined, then either mitigation approaches or alert levels can be implemented. For example, one healthcare shipper identified a key service level was received on time, as a large portion of their orders were used in surgeries after overnight shipping. The distribution center measured shipped same day as ordered as the KPI, and showed great performance. However, customers were still receiving shipments later than desired, sometimes after the surgery was performed or delaying them. In the big data analysis, it was 3

4 determined that weather and customer location played a major factor in small parcel carriers ability to deliver within service commitments. Once this was identified, a protocol was set up that drove different shipping methods during snowy months, driving earlier delivery and increasing on time percentages. Complaints went down and customer satisfaction went up. Ordering process Data generated by the ordering process depends on the ordering methods, ranging from EDI (system to system connections) to web based, to phone calls. With EDI orders, key metrics include time to confirm the order, percent matching price, and error rates. A data analysis of the EDI process showing number of orders vs. percent confirmed within 2 hours will indicate how many customers are receiving immediate feedback on their requests. High price mismatches or high error rates are indicators of problems in your customer data, either on their side or yours. Data Check Ask yourself: What does your process look like from a customer s standpoint? How easy is it to place their order with you? Once they do, how long to get confirmation? How long until it ships, and how well are they communicated with during the shipping process? Are they receiving the product in an acceptable time frame? How smooth and easy is the return process? 4

5 Freight From a view of the customer to a view of the operations, the next logical data set is freight measurements. With GPS sensors on trucks and containers, as well as, the reporting functionality of the top-tier Transportation Management Systems (TMS), best-in-class freight operations have no shortage of data available. Data Check As you look at that myriad of available freight data, key questions are: Are there data segments that will drive positive change, in improved cost, more accurate, accounting, less product loss or higher customer satisfaction from this data? Am I leveraging my freight spend as optimally as I can? Or are others using your inbound or outbound freight to their advantage, on their contracts and even potentially with a markup? Each business need will be different, but there are some common data-analysis views that top performing organizations are using. Cost Accruals How many times has your accounting team asked you for an estimate of your freight spend by the end of the month or quarter, so they can properly accrue the expense? Given that freight carriers often take weeks to generate invoices, and volumes tend to pick up at month or quarter end, that estimation can be difficult and error-prone. With advanced TMS systems, accrual reports can be generated to very closely determine estimated spend. These reports fed by data components such as loaded trips, negotiated prices and fuel estimates have proven to be more accurate than historical estimates and invaluable to accounting teams. Cold-chain temperature monitoring The two main types of cold-chain monitoring are passive data logging sensors on shipments and active data-feeding temperature sensors. On-truck temperature monitors can now tie to GPS data feeds, giving user real time monitoring of truck location and temperature. This allows for alerts, managed by either the shipper or carrier or a contracted service, to highlight a temperature excursion or problem and trigger a response. For example, a broken air-conditioning unit on a cold-chain trailer would go unnoticed with a passive data logger until the product is received and the data downloaded, but with realtime monitoring and alerts, a problem can be noticed and remedied quickly. However, real-time monitoring can be expensive, and data logging has proven adequate with temperature mapping of routes. Data loggers can then be verified upon shipment receipt, either at the warehouse, distributor or end-user, and the product received based on a positive result. Data log results can also be fed back to the shipper, creating a profile of the route during a particular season. With enough shipment data during various points of the year, with varied load configurations, data validation can be done to even eliminate the future use of loggers based on worst case experiences. 5

6 Security GPS feeds from carriers and containers can be tied into a central transportation monitoring system, giving the shipper immediate visibility into current shipment location. These are useful when shipment arrival is critical, perhaps for new product launches, short dated product or backordered items. Shippers may also set geofences, specific location boundaries in map software that trigger alerts when a load goes outside its expected path. One example comes from the pharmaceutical industry, where cargo values are high and the product often targeted. Security professionals determined that one of the easiest ways for thieves to target trucks was to follow them from the manufacturing origin point, and wait for the driver to pull over for a stop. So they implemented rules that put a 200 mile no-stop zone from the origin, reducing theft rates. Geofenced alerts enforce this rule, triggering notification to security teams who can call the driver if a stop is made within the geofence. Potential mode shifts Small parcel analytical software can generate great data sets about destination density, which when tied with analytical views of parcel mode, can generate savings. A mode shift is applicable in situations such as shipping 2 day air, because that is your organization s default ship method, when a ground shipment would get it there in the same time. Once the analysis is run, the order entry ERP needs to be set to trigger the correct shipping method upon entry based on zip code. Data generated from small parcel systems can review the mode shift opportunity losses each month, and then fine tune your ERP as necessary. Customer demand may drive different routings, such as a requirement for an early A.M. receipt at a hospital before scheduled surgeries, but they will become the exception rather than the default. State map shows service level throughout the state where a mode shift to ground would still meet service level U.S. map shows the origin and destination states with the respective service level 6

7 Accessorials Another benefit of a data-dive into small parcel shipments is rooting out accessorial charges, which may add up quickly and be detrimental to your margins over time. Accessorials can include residential delivery fees (sometimes charged incorrectly), address corrections, and dimensional charges. Address corrections is an easy data correction, as these are likely the result of a bad address in your customer master. Flagging address corrections, particularly repeat ones, and then updating the customer master to ensure the addresses are fixed can drive these away easily. Dimensional charges, the focus of changes in the parcel world and the subject of many articles and assessments, can be tracked and analyzed with your packaging provider and shipping team to drive these costs out. Inbound freight management Freight inbound to your manufacturing centers and from manufacturing sites into distribution centers is classified as inbound freight, and is a large expense in healthcare organizations. The first point to drive change and bring visibility to these costs is to require vendors to utilize your inbound freight account, or move your freight management to a freight management company/3pl. With everything being charged to a single account, you ll have the data visibility necessary to see which vendors are using higher cost modes. Analyzing inbound freight by vendor, by SKUs or by combining with your bill of materials (BOM) into freight by finished good, can get you visibility to the problem areas. Inbound freight as a portion of the finished good cost, or final product revenue, can be a good indicator and allow you to focus on those items that are affecting larger areas. The end result of inbound freight visibility is the ideal, but ensuring that each vendor is using your account numbers, and shipping the correct mode, can be the leak in that data. A vendor compliance report needs to be created, that identifies all vendors and their compliance to your routing guides. Once the routing guide is published, you or your inbound freight management vendor can run and publish compliance reports that identify any vendors not using the approved routing information. You need to tie that to information from your purchasing system, highlighting any invoices where freight was paid as pre-paid and add instead of collect. One solution in the industry is to refuse to pay any pre-paid and add freight charges from vendors, forcing them to ship collect unless you have a specific contractual arrangement. This visibility allows you reporting on inbound shipment costs, stops leakage from non-compliant vendors, and drives savings. It doesn t capture any vendor building freight into product price, but your purchasing team will have to recognize those. 7

8 One segment of healthcare logistics that has implemented inbound freight management widely is the healthcare provider institutions. Many 3 rd party services exist to manage this spend, with standard reports and programs. Most medical device vendors are familiar with the onboarding and compliance programs from these vendors. There is no reason that other healthcare logistics operations couldn t implement an inbound management program, to the same degree and with significant expected savings and increased visibility. Consolidations Consolidation opportunities, both on the inbound and outbound freight, are significant data exercises. These are periodic assessments that can be run to determine if your operation would benefit from a consolidation program. One example of consolidation working successfully for medical device manufacturers and hospitals alike is consolidation of multiple orders. By running a data analysis that showed number of orders to the same address in a one or two day period, a supplier was able to hold their orders in the system and drop them at the end of the day into a consolidated batch. That reduced the warehouse labor and lowered freight costs for the manufacturer, and the provider was able to save labor at receiving. Consolidation on inbound utilizes staging points, for example collecting all parcel and LTL freight inbound from the West Coast for a one week period into a Reno, Nevada warehouse, consolidate onto pallets, and then ship east via intermodal railroad or full trailer load. That not only saves a significant amount on freight, but reduces the labor in receiving from hundreds of shipments to one. The data analysis can be done using origin/destination and weight information, and can provide a calculation of estimated savings. Then over time, consolidation reporting can track savings vs. projection and on time delivery. If a one week period to consolidate is too long, modeling can determine how many days of consolidations to hold vs. savings through a multi-variant generator. 8

9 Outbound staging is useful when you have many small shipments going to the same area of the country, and can consolidate them into larger shipments by holding them over time. Shipments to the same destination are easy (for example, all shipments going to a specific retailer distribution center are send on one specific day of the week), requiring just scheduling and system control. It gets more complicated when shipments to multiple destinations are planned, for example a multi-stop LTL route. By staging shipments for a period of time, these multi-stop consolidations can provide great value. The same data analysis is run to predict savings, modeling customer service impact of shipment schedules vs. freight savings. The follow up reports can be run to measure actual savings vs. predicted, balance for customer transit times vs. customer satisfaction indicators, and look for continued savings. An example of efficient outbound staging is a large provider of medications to retail channels, shipping from a single distribution center. This provider implemented a consolidation review as part of their onboarding program. Anytime a new retailer is added or a new product added to the portfolio, a review is done of the consolidation opportunities with a 24 hour, 48 hour, and 72 hour time window. The savings at each point is compared with the impact to the customer and service levels, and a program configured to stage orders for that particular consolidation window. Stats Break According to a survey conducted by AHRMM and the Center for Innovation in Healthcare Logistics 42% of healthcare supply chain professionals see the lack of data standards as a challenge to achieving supply chain excellence 31% of annual operating costs are being spent to support the supply chain of the average healthcare provider 9

10 Data Check Some questions to ask yourself about warehouse & inventory data are: Are my teams as flexible as they could possibly be, and do I have the right data sources to drive the changes to take advantage of that flexibility? Is my operation itself flexible, with space, systems and scale? Can I quickly scale to changes in my business like seasonality spikes, acquisitions or new product launches as rapidly and easily as I want? Warehousing From a warehouse operational perspective, there are hundreds of potential data points that can be measured and reported. Many of the most pertinent indicators are common key performance indicators (KPI s) already in use, and we will not focus on the typical ones for this discussion. These include orders shipped complete, on time shipments, and receipt to putaway. Let s look at the data available and review how that myriad of data can be used on different indicators that likely aren t being watched today. Labor Management Labor Management Systems, or LMS, are common practice in operations heavy with manual labor, and warehousing is certainly that. LMS can provide significant analytical power to scheduling and workforce management. For example, an operation with 75 people may work three shifts, with work zones such as receiving, putaway, picking, packing and shipping, each with an inherent cycle during the day. Receiving may be busy first thing in the morning, shipping later in the day, replenishment at night. Besides scheduling the workforce for three 25 hour shifts and a supervisor frantically watching the floor, LMS s can offer analytical planning based on historical data. That data may include annual seasonality, weekly workload shifts, a balance between temporary and permanent staff, as well as individual employee productivity. Calculating the appropriate number of workers to place on each task, on a given day, during a given hour, taking advantage of individual skills and abilities, seems like a dream. But with the right data loaded into the LMS, tied to a WMS, all that forecasting is absolutely possible. Scenario planning can determine target shift schedules, and then dashboards can indicate performance and any necessary changes. With the right configuration, workers can also see their own data on a LMS, allowing for performance based reward systems. Teams can be formed for group awards, and bonus pay tied to teams that beat productivity targets. Advanced LMS systems can do slotting simulations and optimizations, by feeding in SKU velocity (or sales quantity) and pick times. Combining this level of WMS and LMS data is not a dynamic process, one best done less frequently at the current level of sophistication of the software. But in the future, it may become more frequent, as e-commerce logistics grows and the warehouse must adjust to the rapid pace of consumer demand. Re-slotting calculations are big time saves in labor operations, as travel time is often the longest component of a pick. A metric showing pick rate by SKU, overviewed 10

11 against SKU velocity, will show the high moving SKUs and longest pick rates. Anything with a high pick rate and slow pick rate should be flagged for immediate review and re-slotting. One warehouse provider was able to shave 15% off their productivity using LMS systems, and a medical device manufacturer saved 10% by re-slotting. Those are significant gains in an economy where pressure to reduce supply chain costs is high on healthcare organizations. Space Utilization Another significant waste of costs in warehouses is open bays. The re-slotting exercises discussed previously should move your inventory forward toward your pick zones, which should leave open pick bays towards the back. Often times, healthcare logistics organizations end up with empty rows, empty levels, or empty sections of a warehouse based on cyclical demand. Inventory planners should be able to assist the warehouse with forecasted inventory projections, which when combined with historical views can show any seasonality in inventory and future requirements. Overlaying historical inventory levels and future projections onto existing warehouse space will let warehouse managers decide if the empty space that they have is worth keeping, or just excess. If the warehouse is located in a 3PL s multi-client warehouse that space can often be given back to the 3PL and the monthly space charge reduced. That has the added benefit of improving your pick density, as well as reducing overall spend. An on-going indicator can then be set to measure % of open bays in the warehouse, and target thresholds set for re-review of the projections. Inventory Non-moving inventory is another data driven cost saving opportunity. By reviewing on-hand inventory vs. SKU velocity and future projections, warehouse managers can help identify inventory that is either completely non-moving or has excess. Since that material is being continually cycle-counted, and taking up warehouse space, the removal of that inventory will be an immediate savings. By working with marketing teams, obsolete SKUs can be eliminated, inventory donated or destroyed, and warehouse space gained. Automation decisions Lastly, is the level of automation in your warehouse appropriate and giving you maximum return on investment? The balance between automation and manual labor is a productivity calculation, one that is made at the conception stage of a warehouse layout, and periodically thereafter. Would a carton erector work well to replace this labor-intense process of up-front carton building? Has the marketing team added a direct-to-patient or direct-to-physician option, and now your warehouse is playing catch up to implement some automation to streamline the process? Perhaps a certain piece of automation has been installed for years, but the efficiency was never as promised or the business profile has changed that the automation is no longer as effective. Reviewing automation efficiency, operating costs, maintenance costs, and cost of capital (and already paid depreciation), vs. labor will determine if the automation is worthwhile, worth keeping in service, or ready to be sold to someone who d better use it. Set up an ongoing report that shows all these data points for each significant operation, so you ll always be on top of capital assets, automation and manual process tradeoffs. 11

12 Field Inventory An entire library could be written on the troublesome topic of managing field inventory, but in the arena of big data, certain key areas are applicable. First, you must have a field inventory management system, something that your sales team utilizes to log in inventory when it s received, cycle count, and complete billing sheets for when it s used. Once you ve got access to that data, key data points revolve around inventory and loss. Data Check Ask yourself: Is my inventory in the right place, in the best possible control, at the right level of quality oversight? How accurate is my inventory value, and are my FEFO controls working everywhere? Inventory Field inventory can rapidly get out of control, as sales representatives are often incented to ensure procedures are conducted and physicians satisfied, rather than inventory levels being low. That requires the use of inventory par levels, or continual data analysis of inventory by sales rep vs. sales to determine who has inventory that may not be at efficient levels. Then new items can be withheld or items brought back to a distribution center for re-distribution to allow for better inventory utilization. Loss With uncontrolled inventory comes the risk of higher loss, particularly in sales rep trunks or hospital inventory shelves. Regular cycle counts, random audits, and penalties for loss can help. One data mechanism that can help is tracking compliance to cycle counts over time, measuring inventory accuracy risk. Those reps with greater accuracy get lower audits, allowing them more time in the field and less administrative duty. Those with higher losses get more frequent count requests, shortening the period of time between audited counts and reducing loss exposure. One practice organizations are trying is forward-deployed inventory, creating many smaller warehouse operations rather than having inventory with field representatives. With a contracted warehouse operation, inventory is much better controlled and systems are optimal. If a small warehouse can be set up in major customer locations, perhaps through the campus network of a large 3PL, forward deployed inventory can be shipped directly to the hospitals or physicians rather than representatives carrying it in. Couriers may also be able to do the job of a rep. That allows field personal to maximize time and the organization to have a better controlled inventory. 12

13 Systems As the use of advanced software grows in the logistics field, and storage media costs drop, the availability of data will continually increase. Managing all this data, and ensuring it is accessible in a format that allows decision making, is a foundational challenge in healthcare logistics. Many 3PL s and top companies are moving to data-warehouses and data access systems, putting the data in a system outside of the WMS/TMS/LMS framework that allows for easier access and reporting. Access systems such as Cognos (IBM), Ovation, Oracle Hyperion, and SAS allow users to access historical data and create custom reports quickly and easily, often on mobile platforms. The key with these systems is to have a tool that is reliable, secure, user-friendly enough that business users can create ad-hoc reports, and configurable for push reports. Push reports are those set to run with given parameters that can send the report on a pre-defined periodic basis, say generating and broadcasting labor statistics every Monday morning before a labor planning meeting. Mobile access has become more important for dashboarding and alerts. Configuring an alert that will broadcast when a key parameter reaches a threshold allows users to not monitor the reports and trust they ll get notifications when they need them, and then react wherever they are. For example, a user might configure a report that monitors inventory below a target threshold (therefore risking backorders), and flag any inbound shipment that is projected to miss its due date for those items. Visual data representation is a key for the systems as well, as the users need to be able to generate graphs and charts that help translate the data to actionable data. 13

14 Conclusion There is no shortage of data available to today s healthcare logistics leadership. The challenge is to access the right data, from across the multiple systems that provide services within the supply chain, and translate that data into items that have real impact on customers, costs, and performance. This review has discussed many of the key measurements that are in use across healthcare organizations as well as best practices outside the industry that healthcare can learn from. With the right systems, gathering the right data and reporting it the right way to leadership, you can get actionable information in a timeframe quickly enough for you to act on it, and make a difference. About OHL OHL is one of the largest 3PLs in the world, providing integrated global supply chain management solutions including transportation, warehousing, customs brokerage, freight forwarding, and import and export consulting services. OHL operates more than 130 value-added distribution centers, offers comprehensive transportation management services, employs nearly 7,000, and has offices worldwide. OHL has specific expertise in healthcare, with many medical device, pharmaceutical, home healthcare and healthcare providers utilizing OHL s services for warehousing and transportation management. OHL is skilled at direct-to-consumer fulfillment, serves a wide range of business sectors from specialty retail to manufacturing, and specializes in the healthcare, apparel, electronics, printing, food and beverage, and consumer packaged goods industries. About the Author: Steve Downey, Vice President, Healthcare, OHL Steve leads OHL s healthcare vertical, where he brings together a team of experts in warehousing and transportation to solve healthcare customers supply chain challenges. Steve s background includes roles in logistics, supply chain, operations, sales & marketing and executive leadership for leading medical device and pharmaceutical ingredient manufacturers 14