Supply Chain Matters recently had the opportunity to speak with contract manufacturing services (CMS) firm Jabil’s, specifically Vice President of Supply Chain Solutions and Global Logistics, Fred Hartung. If readers had any perceptions that certain CMS firms were laggards in advanced technology adoption, our interview led to quite the contrary perception.
Jabil has been featured in supply chain industry headlines these past two weeks. At the recent Gartner SCM Executive Conference, Jabil’s intelligent supply chain capabilities in real-time visualization and advanced analytics resulted in receiving an award as a “Supply Chaininnovator.” Hewlett Packard unveiled what it termed as the first production-ready commercial 3D printing system and Jabil participated in the press conference. At last week’s SAP Sapphire customer conference, SAP and UPS announced a partnership for services related to an on-demand 3D printing network which involves this CMS as well.
Hartung oversees multiple roles including responsibility for advanced supply chain technology, digital supply chain, advanced planning and trade compliance. He additionally heads a team overseeing Jabil’s supply chain global network.
Our discussion touched on a number of business and technology areas.
Regarding the current CMS industry landscape, Hartung described changing global transportation costs, foreign exchange rate volatility and changes in the “value density” of products as all dynamic industry forces.
More manufacturing focused OEM’s now see themselves as incorporating more and more software and technology as major parts of product design and functionality features and that impact spills over to contract manufacturers. OEM customers were further described as increasingly practicing near-shoring manufacturing sourcing practices aligned to major geographic product demand regions with Mexico and Vietnam really taking off along with resurgence towards manufacturing in Malaysia. Hartung indicated Jabil’s belief that 3-D printing will make a big difference in localized manufacturing tied to customer fulfillment. OEM’s are still experimenting with incorporating 3D printing concepts into product strategy and Jabil is assisting by maintaining various labs across Silicon Valley.
We discussed what is often described as the number one multi-industry supply chain decision-support challenge, that being gaining and enabling end-to-end planning and customer fulfillment visibility. Hartung described this challenge in the context of “actionable visibility”, a focus on the most pertinent information supporting business processes along with “in-control” digitized streaming information flow that is anchored in analytics-driven decision-making capabilities. Another Jabil consideration in its use of advanced analytics is directed at managing and mitigating supply chain risk. Nine separate categories of risk are continually tracked ranging from low to higher supply chain disruption and risk factors.
In the area of addressing Internet of Things, machine learning and cognitive computing opportunities, Hartung acknowledged that information security has got to be an area to be taken very seriously, and prominent in the early design process. Jabil views IoT as an enabler of new business models for customers and for Jabil, and here again, leveraging analytics, either prescriptive or predictive, is the important area of concentration. Responding to the question of whether customers ready for these types of initiatives, Hartung indicated that while Jabil is way ahead on the learning curve, customers indicate that they want to hear more.
Besides incorporating advanced supply chain technology and multi-tenancy practices across Jabil’s own extended supply chain, the CMS is increasingly being called upon to assist OEM customers themselves in deployment of such technologies across their extended supply chains as-well. This has been a new area of technology services for some CMS providers.
As a key supply chain partner in many more multi-industry settings, a contract manufacturer must be knowledgeable of the business process and enabling technology competences that make a difference in meeting both customer and internal business and supply chain outcomes. This is an industry that moves in lock-step with its customers, and is constantly challenged with narrow margins to work with.
As a recognized supply chain industry analyst, this author has had the opportunity to view a number of Jabil industry presentations over past years as well as to speak with the firm’s executives. This CMS has consistently demonstrated a willingness to leverage and collaborate with customers on advanced technology use cases across its supply chain management processes. After my recent interview, I am further impressed with the firm’s understanding and practice of leveraging areas where technology enablement can indeed be a facilitator of a more adaptive and resilient supply chain.
© Copyright 2016. The Ferrari Consulting and Research Group LLC and Supply Chain Matters® blog. All rights reserved.
In our most recent Supply Chain Matters commentary related to Apple that noted the huge thud that rang across Silicon Valley, we explored potential areas of product management related areas that are now under enormous pressure to energize additional product volumes and consequent product and services revenues. This week features an announcement of a new partnership directed at enhancing the business applications support applicability to Apple’s mobile devices.
Apple and SAP jointly announced a partnership directed at revolutionizing the mobile work experience for enterprise customers of all sizes, combining powerful native apps for iPhone and iPad with the capabilities of the SAP HANA platform. According to this announcement, this joint effort will further deliver a new iOS software development kit (SDK) and training academy so that developers, partners and customers can easily build native iOS apps tailored to their business needs, including forms of analytics.
This partnership for leveraging more business applications on Apple’s iOS come after prior joint-development announcements with both IBM and Cisco. In a related interview conducted with The Wall Street Journal, Apple CEO Tim Cook re-iterated that leveraging enterprise business applications on Apple mobile devices is a key growth opportunity. However, he declined to share an update on any revenue numbers to-date related to prior joint-efforts this area. Cook further indicated that he view the SAP partnership as a “starting gun” for the development of workplace applications similar to the opening of the Apple App Store in 2008.
That obviously established some ongoing high expectations for all parties in this area.
The timing of this announcement is noteworthy, since he comes two weeks before SAP’s annual Sapphire customer conference, where no doubt, some keynote stage time will be dedicated to this new partnership. Then again, we can all speculate as to why the announcement was moved this week, rather than during Sapphire. Perhaps Apple needed to have some positive news streaming this week.
I suppose we can all look forward to more SAP supply chain, PLM and procurement applications and supporting analytics capabilities running on Apple mobile devices. The open question remains timeframe.
April brings about the kickoff of spring industry and technology focused conferences and briefing sessions for industry analysts including this independent supply chain industry analyst. Over the coming weeks I will be attending and sharing impressions a number of venues and events.
This week, I was invited to attend the Open Text 2016 Industry Analyst Event held in a Boston. Some of our readers that may not be directly familiar with Open Text which categories itself as an Enterprise Information Management (EIM) technology support provider. However, they may be supporting their supply chain messaging and transactional needs from this vendor’s technology network.
Readers who have been following our Supply Chain Matters commentaries focused on end-to-end supply chain network technology platforms may recall GXS. In a 2012 commentary, this Editor declared GXS as a hidden gem in B2B information services. This company’s heritage stemmed from the late sixties with its initial founding as General Electric Information Services (GEIS) providing computer time-sharing to general users, migrating to support value-added network (VAN) services such as EDI for both GE and external clients. By 1998, GEIS’s global electronic trading community exceeded 100,000 trading partners, and in 2002, the renamed GXS was spun out as an independent technology services provider purchased by venture capital firms Francisco Partners and Norwest Venture Partners.
In 2005, GXS was provided the opportunity to acquire the former IBM EDI and Business Exchange Services network. In 2010, GXS also acquired a company called Inovis, which we later highlighted for its innovative B2B collaborative process support potential.
By 2011, this B2B services provider had garnered over 40,000 network clients including 75 percent of Fortune 500 customers. At the time, GXS direct materials and associated services networks were reportedly processing over 12 billion transactions representing a highly significant dollar volume of electronic commerce. In 2013, its network was renamed the GXS Trading Grid. Yet in all this time, GXS struggled to deliver robust profitability growth.
In November of 2013, GXS was acquired by Open Text for an estimated $1.2 billion, roughly 2.4 times GXS Fiscal 2012 revenues. The stated goal of the acquisition was to combine OpenText’s Information Exchange capabilities with GXS’s portfolio of B2B managed and integration services.
Since that time, we have monitored ongoing progress from a B2B supply chain network lens.
In June of 2014, this author scribed his impressions from the Open Text Industry Analyst briefing event. In summary, I had walked away with many open questions regarding the broad scope the strategy, and specifically, more concentrated strategy and emphasis on further leveraging B2B supply chain and specific manufacturing and retail industry and emerging online commerce support.
I purposely stayed away from the 2015 briefing event, but elected to attend this year’s event to ascertain progress in B2B supply chain network focused areas.
Because of current time constraints, I will refrain from a detailed commentary as to specifics. I can however share that the strategy is finally showing promise, one that brings together the tenets of EIM in the dimensions of supply chain messaging, managed services, business process management and deeper network-focused analytics.
In conjunction with this week’s Analyst event, Open Text formally announced OpenText Release 16, what the vendor describes as the most comprehensive, integrated digital information platform. OpenText Release 16 consists of two separate offerings, Open Text Suite 16 and Open Text Cloud 16, combined within a single platform that manages and analyzes the entire flow of information. In addition, OpenText Release 16 can be deployed on-premises, in the cloud or in hybrid cloud environments.
We learned that with Cloud 16, the GXS Trading Grid is renamed the Release 16 Business Network, and moves beyond information exchange will include support in process areas related to:
- Procure-to-pay information and transactional management
- Logistics track and trace
- Trading partner digitization and analytics
- Electronic invoicing and Ecommerce needs
- Supply chain analytics
Of further interest is planned introduction of what is termed as Supply Chain Activity Index, an analytical based aggregate view of the B2B network, and forms of Business Process Management (BPM) support for processes that span the supply and value chain network. These two areas should really peak interest, depending on eventual design and functionality.
As for now, this analyst is modifying his prior impressions. Open Text may indeed be on the road towards addressing the various complex and fast-changing requirements for supporting today’s globally extended B2B business process networks. It is far more than messaging and EDI support.
Open Text may well have capabilities of interest from the perspective of a B2B network as a Digital Platform that exchanges various forms of mission critical transactional or regulatory information. While this development remains somewhat a continued work-in-progress, Open Text Suite 16 provides some promising opportunities for certain industry sectors, especially business networks supporting regulated business process requirements or those struggling with expanding needs to support unique content for various online customer fulfillment channels.
This analyst will provide added details at a later date along with continued assessment commentaries related to Open Text Suite 16 and its B2B supply chain business network development and product release efforts. In the meantime, if readers have specific questions, send us an email or call.
© 2016 The Ferrari Consulting and Research Group and the Supply Chain Matters® blog. All rights reserved.
This Supply Chain Matters posting is a continuation of our market education series related to approaches towards enterprise optimization and analytics applied to critical decision-making needs.
The Wall Street Journal recently published a report: Uber Should Share More Data with Cities, (Paid subscription required) which for this author, provides a further example for moving beyond traditional functionally based intelligence and decision-making into the broader, more prescriptive context of business or enterprise-wide decision making needs.
The essence of this report reflects on a recent quantitative survey commissioned by a Washington DC non-profit that recommended that ride-hailing services such as Lyft and Uber share more data about pick-ups and drop-offs with city public transit agencies who could use that data to better serve overall public transit needs. The study itself, involving over 4000 consumers in seven U.S. cities, revealed that people often utilize ride-hailing services on weekends to attend special events or to insure reliable transportation while relying on public transportation for daily commute needs. A little over 20 percent of respondents indicated that they used ride-hailing services for commuting to work.
The “Walled Garden” of Non-Data Sharing
The authors of this study commented that a “walled garden” of non-data-sharing will hamper both constituencies if they expect to take part in a wider transportation mobility ecosystem. In essence, while insuring that personal identity data is protected, information or knowledge reflecting the demand patterns of the broader ecosystem or enterprise is far more meaningful and important toward meeting changing consumer and customer support and fulfillment needs.
We note the above because it provides yet another analogy to the obstacles and needs that constantly occur across the end-to-end supply chain, and indeed the enterprise. It reflects the challenges of the analytics maturity curve where notions that individual-knowledge-is-power clash with requirements for organizational wide insights and needs for various forms of prescriptive analytics based decision-making.
In the ride-hailing survey noted by the WSJ, the survey data uncovered the opportunity for communities to support what was termed as “supersharers”, people less likely to own a car in a city, and more inclined to utilize a combination of ride-hailing services such as car and bicycle sharing for a certain amount of trips at certain times. In essence, the opportunity is to better identify and serve quickly changing transportation needs within cities because of the existence of newer services introduced.
The Power of Prescriptive in Ride-Sharing
But what if the ride-sharing context was anchored in survey data of usage patterns of ride sharing services to identify future service growth needs accomplished by time-series extrapolation or regression?
In other words, more prescriptive connotations, for instance:
- Modeling the entire network of ride-sharing services including public, private or super sharing services.
- Utilizing data from predictive tools to feed the network described above
- Modeling and identification of the various constraints associated with the problem at-hand; e.g. the number of total vehicles, drivers or no drivers required, passengers per vehicle or known capacity constraints related to demand for transportation services.
- Identify the best mix of people transportation services that optimizes the objectives at-hand- in this case, customer satisfaction needs of ride-sharing passengers, optimizing budget or operational constraints for public or governmental transit agencies, maximizing constrained government budgets or profit opportunities for private ride-sharing firms.
Moving along the analytics maturity curve often involves a number of maturity phases. That includes demonstrating the business value and power of sharing data across business functions in certain context. It further involves incorporating more predictive analytics to convert data into insights as to what can or should be expected, given the current pattern of events.
However, at the same time, there is sometimes a reality, namely that enterprise sharing of data does not lend itself to a cookie-cutter approach.
Ditching the Cookie-Cutter Approach for Better Supply Chain Insights
Too often, teams approach broader data and information sharing from the perspective of force-fitting existing business systems. As an example, there are applications supporting supply chain related process support needs, financial management and budgeting as well as overall customer support needs. The obstacles for both integrating all of such business process data into more prescriptive based insights often presents itself in Sales and Operations Planning (S&OP) processes which by their existence, represent cross-business representation and advocacy. The context of data knowledge is the dominant business application supporting the organization or function, such as the ride-hailing example. The need for deeper, more prescriptive insights is satisfied when similar and added data is shared with common context, for example, profiles of overall customer demand contrasted to fulfillment channels, or categories of various customers in-context to fulfillment needs and cost-to-serve needs.
How We Can Break Down the Barriers to Analytics Maturity
Moving along the analytics maturity curve has the same People-Process-Technology implications as other major enterprise-wide change management initiatives.
Improve Analytical Skills
Teams and individuals need to improve their analytical skills because most industry environments are moving quickly towards quicker, more analytics driven decision-making needs. They need the ability to formulate more cross-functional approaches to enterprise wide analytics and decision making that is not hampered by existing business applications that are singularly functionally anchored. Business leaders need to advocate and actively support not only the cross functional sharing of data, but more prototyping approaches to needs in supporting more prescriptive decision-making vs. reactive decision-making.
Include All Business Silos
Processes need to move their context and information sharing beyond a single functional umbrella such as supply chain management and instead include context for product management or design, individual customer service process and channel fulfillment expectations, as well as financial and business outcome goals and metrics.
Technology needs to be able to span beyond business application system umbrellas that existed years ago into mechanisms for streaming important data, performance needs and insights derived across key business processes. The context is what analyst firm Gartner defined as bridging the gap of moving from “Systems of differentiation” to “Systems of innovation.” Analyst firm IDC defines these newer capabilities under the banner of “Systems of Intelligence.”
Consider the Cloud
An implied reality is that implementing augmented technology that supports innovation needs cannot be a major disruption to existing business operations, hence the new considerations for evaluating cloud-based innovative technology approaches.
Expand the Horizons of IT Support
A final note relates to IT support teams whom line-of-business, enterprise and functional business teams turn to for counsel and support for information technology. Clinging to the assumption that systems of innovation or deeper intelligence can be supported purely by pre-packaged software developed by business process design and data management principles that are now quickly becoming outdated is a disservice. Similarly, clinging to the belief that the innovative system does not currently appear on the radar screen of the dominant backbone system provider is a similar disservice towards meeting a business need for time-sensitive process and decision-making innovation.
Now is the time to move beyond the comfort zones of knowledge of existing application and systems and into the lens of enterprise-wide data sharing supplemented by various forms of analytics powered decision-making. More of that will be shared in our next posting in this market education series.
Readers can view our past postings in this series by clicking below:
© 2016 The Ferrari Consulting and Research Group and the Supply Chain Matters® blog. All rights reserved.
Disclosure: River Logic Software is one of other sponsors of the Supply Chain Matters blog.
More than ever, supply chain functional or line of business teams have been frustrated by their increasing needs for broader and more-timely business intelligence (BI). The reasons are many and in increasing cases, very valid. But more than ever, teams should now be turning their attention towards leveraging processes and technology anchored in prescriptive analytics.
The architectural approach of traditional BI approaches stem from tapping centralized data warehouses, where all forms of data and information from various business systems are stored. Such data, information and reports are often historical in nature and require additional work in either Excel spreadsheets or “rules based tools” to convert data into needs for more predictive information. BI extraction
The notions of product demand forecasting based on historic sales of the product, or a particular customer’s demand and revenue history were often a function of such needs for more intelligence. The need in traditional BI was to allow users the ability to contrast plans with actual results or to prepare sales and operations planning (S&OP) teams with needed information to make important decisions related to addressing product demand or supply gaps. The ability to leverage hidden intelligence was often constrained because of the resource limitations of IT, the complexity of the centralized information warehouse, or the turnaround time for information requests . When business teams finally get the insights they seek, the gap between data and optimal decisions has been lost or too difficult to overcome.
Two other important trends have since occurred. First and foremost, the clock speed of market changes and/or business events has dramatically increased requiring that supply chain and line-of-business teams anticipate such changes and be prepared with various scenarios for more informed response to such changes. Overall complexity of supply chain decisions has further increased. There can be occurrences of increased risk, quickly evolving industry and market opportunities, or needs for more impactful supply chain efficiency and cost reduction.
The second is today’s continual advances in information technology surrounding in-memory computing, streaming Big-Data analysis, more user friendly data visualization tools and cloud-based platforms. More sophisticated mathematical optimization techniques, which were previously only available through custom coded software or by the hiring of experienced data scientists, is now becoming available in packaged software.
In our previous Supply Chain Matters posting: The Journey Towards Integrated Business Planning, we expanded on our prediction that S&OP processes will morph into broader forms of integrated business planning that are more anchored in analytics support capabilities. Decisions are no longer anchored on what occurred in the past, but rather, what is expected to occur based on various forms of both internal and externally available information. Decisions are not one-dimensional but instead predicated on the most up-to-date information placed in proper context as to impacts on business metrics or required business outcomes.
Supply Chain Matters sponsor River Logic Software succinctly describes these decisions as:
Descriptive: Which products and customers are most / least profitable in this plan?
Diagnostic: Where are our marginal opportunities to improve profitability?
Predictive: What happens if the price of a key input component rises dramatically because of unplanned market dynamics?
Prescriptive: What should we do if our new product innovation doesn’t drive the forecasted demand?
As was observed in our prior commentary, many of today’s legacy enterprise systems are anchored in heuristics and data models focused solely on product demand, supply, capacity and inventory data from a historic perspective as contrasted to a what to anticipate perspective. S&OP participants desiring broader business intelligence and insights are more than ever expressing a need to make forms of descriptive, diagnostic, predictive or prescriptive decisions. The former two types of decisions often originate in tactical S&OP phases while the latter two often originate in executive level S&OP decision-making. The key is to bring together all existing forms of internal enterprise data, be it supply chain, procurement, product management, financial or customer focused. It is further predicated on functional and business teams to have more user-friendly tools and techniques to assemble and context these levels of analytics that can support such decision-making.
Thus, supporting needs for more timely, contextual and integrative forms of decision-making should be predicated on a framework of decision support analytics that context available enterprise-level data that spans not just supply chain, but other business and functional information sources.
It is no longer a limited context warehouse approach, but rather an enterprise capability to transform information into analytics powered decision-support.
Disclosure: River Logic is one of other sponsors of the Supply Chain Matters blog.