Category Archives: Manufacturing
That tag line got your attention – did it not? Last week I talked about how companies are trying to squeeze more value out of their asset data (e.g. equipment of any kind) and the systems that house it. I also highlighted the fact that IT departments in many companies with physical asset-heavy business models have tried (and often failed) to create a consistent view of asset data in a new ERP or data warehouse application. These environments are neither equipped to deal with all life cycle aspects of asset information, nor are they fixing the root of the data problem in the sources, i.e. where the stuff is and what it look like. It is like a teenager whose parents have spent thousands of dollars on buying him the latest garments but he always wears the same three outfits because he cannot find the other ones in the pile he hoardes under her bed. And now they bought him a smart phone to fix it. So before you buy him the next black designer shirt, maybe it would be good to find out how many of the same designer shirts he already has, what state they are in and where they are.
Recently, I had the chance to work on a like problem with a large overseas oil & gas company and a North American utility. Both are by definition asset heavy, very conservative in their business practices, highly regulated, very much dependent on outside market forces such as the oil price and geographically very dispersed; and thus, by default a classic system integration spaghetti dish.
My challenge was to find out where the biggest opportunities were in terms of harnessing data for financial benefit.
The initial sense in oil & gas was that most of the financial opportunity hidden in asset data was in G&G (geophysical & geological) and the least on the retail side (lubricants and gas for sale at operated gas stations). On the utility side, the go to area for opportunity appeared to be maintenance operations. Let’s say that I was about right with these assertions but that there were a lot more skeletons in the closet with diamond rings on their fingers than I anticipated.
After talking extensively with a number of department heads in the oil company; starting with the IT folks running half of the 400 G&G applications, the ERP instances (turns out there were 5, not 1) and the data warehouses (3), I queried the people in charge of lubricant and crude plant operations, hydrocarbon trading, finance (tax, insurance, treasury) as well as supply chain, production management, land management and HSE (health, safety, environmental).
The net-net was that the production management people said that there is no issue as they already cleaned up the ERP instance around customer and asset (well) information. The supply chain folks also indicated that they have used another vendor’s MDM application to clean up their vendor data, which funnily enough was not put back into the procurement system responsible for ordering parts. The data warehouse/BI team was comfortable that they cleaned up any information for supply chain, production and finance reports before dimension and fact tables were populated for any data marts.
All of this was pretty much a series of denial sessions on your 12-step road to recovery as the IT folks had very little interaction with the business to get any sense of how relevant, correct, timely and useful these actions are for the end consumer of the information. They also had to run and adjust fixes every month or quarter as source systems changed, new legislation dictated adjustments and new executive guidelines were announced.
While every department tried to run semi-automated and monthly clean up jobs with scripts and some off-the-shelve software to fix their particular situation, the corporate (holding) company and any downstream consumers had no consistency to make sensible decisions on where and how to invest without throwing another legion of bodies (by now over 100 FTEs in total) at the same problem.
So at every stage of the data flow from sources to the ERP to the operational BI and lastly the finance BI environment, people repeated the same tasks: profile, understand, move, aggregate, enrich, format and load.
Despite the departmental clean-up efforts, areas like production operations did not know with certainty (even after their clean up) how many well heads and bores they had, where they were downhole and who changed a characteristic as mundane as the well name last and why (governance, location match).
Marketing (Trading) was surprisingly open about their issues. They could not process incoming, anchored crude shipments into inventory or assess who the counterparty they sold to was owned by and what payment terms were appropriate given the credit or concentration risk associated (reference data, hierarchy mgmt.). As a consequence, operating cash accuracy was low despite ongoing improvements in the process and thus, incurred opportunity cost.
Operational assets like rig equipment had excess insurance coverage (location, operational data linkage) and fines paid to local governments for incorrectly filing or not renewing work visas was not returned for up to two years incurring opportunity cost (employee reference data).
A big chunk of savings was locked up in unplanned NPT (non-production time) because inconsistent, incorrect well data triggered incorrect maintenance intervals. Similarly, OEM specific DCS (drill control system) component software was lacking a central reference data store, which did not trigger alerts before components failed. If you add on top a lack of linkage of data served by thousands of sensors via well logs and Pi historians and their ever changing roll-up for operations and finance, the resulting chaos is complete.
One approach we employed around NPT improvements was to take the revenue from production figure from their 10k and combine it with the industry benchmark related to number of NPT days per 100 day of production (typically about 30% across avg depth on & offshore types). Then you overlay it with a benchmark (if they don’t know) how many of these NPT days were due to bad data, not equipment failure or alike, and just fix a portion of that, you are getting big numbers.
When I sat back and looked at all the potential it came to more than $200 million in savings over 5 years and this before any sensor data from rig equipment, like the myriad of siloed applications running within a drill control system, are integrated and leveraged via a Hadoop cluster to influence operational decisions like drill string configuration or asmyth.
Next time I’ll share some insight into the results of my most recent utility engagement but I would love to hear from you what your experience is in these two or other similar industries.
Recommendations contained in this post are estimates only and are based entirely upon information provided by the prospective customer and on our observations. While we believe our recommendations and estimates to be sound, the degree of success achieved by the prospective customer is dependent upon a variety of factors, many of which are not under Informatica’s control and nothing in this post shall be relied upon as representative of the degree of success that may, in fact, be realized and no warrantee or representation of success, either express or implied, is made.
I believe that most in the software business believe that it is tough enough to calculate and hence financially justify the purchase or build of an application - especially middleware – to a business leader or even a CIO. Most of business-centric IT initiatives involve improving processes (order, billing, service) and visualization (scorecarding, trending) for end users to be more efficient in engaging accounts. Some of these have actually migrated to targeting improvements towards customers rather than their logical placeholders like accounts. Similar strides have been made in the realm of other party-type (vendor, employee) as well as product data. They also tackle analyzing larger or smaller data sets and providing a visual set of clues on how to interpret historical or predictive trends on orders, bills, usage, clicks, conversions, etc.
If you think this is a tough enough proposition in itself, imagine the challenge of quantifying the financial benefit derived from understanding where your “hardware” is physically located, how it is configured, who maintained it, when and how. Depending on the business model you may even have to figure out who built it or owns it. All of this has bottom-line effects on how, who and when expenses are paid and revenues get realized and recognized. And then there is the added complication that these dimensions of hardware are often fairly dynamic as they can also change ownership and/or physical location and hence, tax treatment, insurance risk, etc.
Such hardware could be a pump, a valve, a compressor, a substation, a cell tower, a truck or components within these assets. Over time, with new technologies and acquisitions coming about, the systems that plan for, install and maintain these assets become very departmentalized in terms of scope and specialized in terms of function. The same application that designs an asset for department A or region B, is not the same as the one accounting for its value, which is not the same as the one reading its operational status, which is not the one scheduling maintenance, which is not the same as the one billing for any repairs or replacement. The same folks who said the Data Warehouse is the “Golden Copy” now say the “new ERP system” is the new central source for everything. Practitioners know that this is either naiveté or maliciousness. And then there are manual adjustments….
Moreover, to truly take squeeze value out of these assets being installed and upgraded, the massive amounts of data they generate in a myriad of formats and intervals need to be understood, moved, formatted, fixed, interpreted at the right time and stored for future use in a cost-sensitive, easy-to-access and contextual meaningful way.
I wish I could tell you one application does it all but the unsurprising reality is that it takes a concoction of multiple. None or very few asset life cycle-supporting legacy applications will be retired as they often house data in formats commensurate with the age of the assets they were built for. It makes little financial sense to shut down these systems in a big bang approach but rather migrate region after region and process after process to the new system. After all, some of the assets have been in service for 50 or more years and the institutional knowledge tied to them is becoming nearly as old. Also, it is probably easier to engage in often required manual data fixes (hopefully only outliers) bit-by-bit, especially to accommodate imminent audits.
So what do you do in the meantime until all the relevant data is in a single system to get an enterprise-level way to fix your asset tower of Babel and leverage the data volume rather than treat it like an unwanted step child? Most companies, which operate in asset, fixed-cost heavy business models do not want to create a disruption but a steady tuning effect (squeezing the data orange), something rather unsexy in this internet day and age. This is especially true in “older” industries where data is still considered a necessary evil, not an opportunity ready to exploit. Fact is though; that in order to improve the bottom line, we better get going, even if it is with baby steps.
If you are aware of business models and their difficulties to leverage data, write to me. If you even know about an annoying, peculiar or esoteric data “domain”, which does not lend itself to be easily leveraged, share your thoughts. Next time, I will share some examples on how certain industries try to work in this environment, what they envision and how they go about getting there.
The Physician Payments Sunshine Act shines a spotlight on the disorganized state of physician information, which is scattered across systems, often incomplete, inaccurate and inconsistent in most pharmaceutical and medical device manufacturing companies.
According to the recent Wall Street Journal article Doctors Face New Scrutiny over Gifts, “Drug companies collectively pay hundreds of millions of dollars in fees and gifts to doctors every year. In 2012, Pfizer Inc., the biggest drug maker by sales, paid $173.2 million to U.S. health-care professionals.”
The Risks of Creating Reports with Inaccurate Physician Information
There are serious risks of filing inaccurate reports. Just imagine dealing with:
- An angry call from a physician who received a $25 meal, which was inaccurately reported as $250 or who reportedly, received a gift that actually went to someone with a similar name.
- Hefty fines and increased scrutiny from the Centers for Medicare and Medicaid Services (CMS). Fines range from $1,000 to $10,000 for each transaction with a maximum penalty of maximum $1.15 million.
- Negative media attention. Reports will be available for anyone to access on a publicly accessible website.
How prepared are manufacturers to track and report physician payment information?
One of the major obstacles is getting a complete picture of the total payments made to one physician. Manufacturers need to know if Dr. Sriram Mennon and Dr. Sri Menon are one and the same.
On top of that, they need to understand the complicated connections between Dr. Sriram Menon, sales representatives’ expense report spreadsheets (T&E), marketing and R&D expenses, event data, and accounts payable data.
3 Steps to Ensure Physician Information is Accurate
In recent years, some pharmaceutical manufacturers and medical device manufacturers were required to respond to “Sunshine Act” type laws in states like California and Massachusetts. To simplify, automate and ensure physician payment reports are filed correctly and on time, they use an Aggregate Spend Repository or Physician Spend Management solution.
They also use these solutions to proactively track and review physician payments on a regular basis to ensure mandated thresholds are met before reports are due. Aggregate Spend Repository and Physician Spend Management solutions rely on a foundation of data integration, data quality, and master data management (MDM) software to better manage physician information.
For those manufacturers who want to avoid the risk of losing valuable physician relationships, paying hefty fines, and receiving scrutiny from CMS and negative media attention, here are three steps to ensure accurate physician information:
- Bring all your scattered physician information, including identifiers, addresses and specialties into a central place to fix incorrect, missing or inconsistent information and uniquely identify each physician.
- Identify connections between physicians and the hospitals and clinics where they work to help aggregate accurate payment information for each physician.
- Standardize transaction information so it’s easy to identify the purpose of payments and related products and link transaction information to physician information.
Physicians Will Review Reports for Accuracy in January 2014
In January 2014, after physicians review the federally mandated financial disclosures, they may question the accuracy of reported payments. Within two months manufacturers will need to fix any discrepancies and file their Sunshine Act reports, which will become part of a permanent archive. Time is precious for those companies who haven’t built an Aggregate Spend Repository or Physician Spend Management solution to drive their Sunshine Act compliance reports.
If you work for one of the pharmaceutical or medical device manufacturing companies already using an Aggregate Spend Repository or Physician Spend Management solution, please share your tips and tricks with others who are behind.
Tick tock, tick tock….
Do you know how good your multichannel data is? This blog covers four business objectives when accelerating multi channel commerce and which quality of product data is needed to deliver to that and a summary of questions to ask when establishing your strategy. These questions help ecommerce managers, category managers and marketers at retailers, distributors and brand manufacturers ask the right questions on product and customer data when establishing a multi channel strategy.
The Multichannel Challenge: Availability of Relevant Information
At every customer touch point, the ready availability of product information has a profound effect on buying decisions. If your customers can’t find what they’re shopping for, don’t understand how well your product meets their needs, or aren’t confident in their choice, they won’t complete their purchase.
When customers are researching or actively online shopping for products, research says 40 is the magic number:
40 % of buyers intend to return their purchase at the time they order it.
40 % order multiple versions of a product.
40 % of all fashion product returns are the result of poor product information (Consumer electronics are 15,3%; Sources: Trusted Shops, 2012, Internet World Business 7.1.2013)
All the high-quality product data in the world is useless if an organization cannot leverage that data for quicker time to market, improved e-commerce performance, and greater customer satisfaction.
Four Business Objectives When Accelerating Multi Channel Commerce
This white paper comes with four common use cases that illustrate typical business objectives within a multichannel commerce strategy. When looking into your product information, here is a list of questions you might consider.
1. Increasing conversions and lowering return rates by ensuring that customers can access product information in an easy-to-consume form.
- Where is the flawed content coming from?
- What tools and incentives can we provide for suppliers to maintain the high quality content?
- Which data quality processes should be automated first?
- Do we need a bespoke data model to fit your requirements?
- Can we effectively use industry standards for communicating with suppliers (such as GS1 or eClass)?
2. Lowering manual processing costs by merging the best product content from multiple suppliers.
- How many product catalogs do we have and what are the processes that slow us down?
- Who is responsible for the quality of the product information?
- How can we define and enforce the objective and measurable policies?
- Which supplier has best descriptions / certain translation, high-quality images / video / etc.?
- How do we collaborate with our large and small suppliers to achieve best data quality?
3. Growing margins through “long tail” merchandising of a broader assortment of products.
- Can we automate product classification?
- Which taxonomy will work best for us?
- Do all stakeholders have visibility of data quality metrics and trends?
- How can we leverage information across all channels and customer touch points, not only ecommerce?
4. Increasing customer satisfaction through more consistent information and corporate identity across sales channels.
- How should we connect customer and product information to provide personalized marketing?
- How can we leverage supplier and location data for regional marketing?
- How do we enable crowd sourcing of comments, reviews and user images?
- What information do internal and external users need to access in real time?
Find more information with the complete white paper on multichannel commerce and data quality.
Last week, I posted this blog: Logitech MDM Case Study: Seven Lessons for Mastering Product and Customer Data (Part I of II) which shares highlights from recent webinar. Logitech’s Severin Stoll, Senior Business Engagement Manager of Global IT Solutions spoke with David Decloux, MDM technical lead in EMEA about Logitech’s Global MDM implementation, in which they are mastering product, customer and consumer data.
In this blog, I’ll share some of the highlights of the Q&A I led and results from two polls. (more…)
Logitech’s Severin Stoll, Senior Business Engagement Manager of Global IT Solutions participated in a webinar with David Decloux, MDM technical lead in EMEA about Logitech’s Global MDM Implementation.
Severin shared the business challenges that led the global electronics company to pursue an MDM program, their business requirements, and the seven lessons for mastering product and customer data. Their next step is mastering consumer data.
In the next two blogs I’ll share some of the highlights from the discussion, the Q&A session and the poll results from this popular webinar which attracted more than 800 registrants. (more…)
Last week I posted this blog, OppenheimerFunds Dreamforce Story: Lay a Foundation of Trusted and Complete Customer Information for Salesforce.
Another superb Dreamforce story was delivered by Interstate Batteries’ director of Market IQ, Mike Darr.
Did you know Interstate Batteries is the number one replacement brand battery in North America? Did you know batteries were perishable? These were some facts that I learned during Mike’s presentation at Dreamforce 2012. (more…)