In small and midsize businesses, supply chains can sometimes be more daunting than their much-larger rivals’. The challenges may be the same; but when every customer counts, there is little room for delayed product shipments, lost material orders, and low-quality supplies. Access to high-quality resources at the right time, right place, and right quantity matters to smaller firms. But more important, they must take every opportunity to drive customer satisfaction and loyalty, generate a stable cash flow, and reduce overhead costs.
To create supply chains with “big business” advantages such as cost-efficiency, growth-enablement, and strategic outcomes, small and midsize companies require a forward-thinking mindset and the right tools. The eBook “Ecosystem in Action: SME Customer Success Stories,” recently released by SAP, offers various examples of supply chains that are running like a big business with the agility and nimbleness that only small and midsize firms can deliver. Here are three of those success stories.
With a workforce of 350 employees, CPIC Abahsain Fiberglass, a joint venture uniting Bahrain-based Abahsain Fiberglass and China-based Chongqing Polycomp International Corporation (CPIC), is fulfilling the high demand for corrosion-resistant fiberglass and glass fiber across the Persian Gulf region. The company has created a business model dependent on high-quality products, proximity to its customers, reliability, and timeliness. However, maintaining this well-earned reputation required a new perspective after Abahsain opened a new state-of-the-art manufacturing facility shortly after its partnership with CPIC was formed.
To operate more efficiently as it significantly increased production output and connectivity with CPIC, Abahsain implemented a foundational enterprise resource planning (ERP) solution system. In short order, CPIC Abahsain Fiberglass enhanced its ability to change and expedite product customizations – leading to 50% greater efficiency, 10% lower operational costs, and higher customer satisfaction.
Royal Can Industries: Bringing clarity to product costs and driving 5% cost savings
For years, Royal Can Industries has demonstrated mastery in the art of maintaining the lush flavor and nutrients of fruits and vegetables as they are packed in a tin can and shipped to the rest of the world. As a leading packaging can manufacturer in Thailand, the company is the first company of its kind to penetrate the Japanese market – proving its position as the industry’s primary innovator.
When demand and revenue surged, Royal Can Industries’ siloed platform made it difficult to support informed decision making. After implementing an ERP application, all levels of the firm gained an uninterrupted flow of business-critical information and detailed visibility into costs by product and process. With greater transparency and a clear vision, Royal Can Industries decreased its costs by five percent while consistently innovating attractive and convenient packaging designs for consumers.
3F Industries: Responding to dynamic market conditions
3F Industries is playing a vital role in millions of households, bakeries, and confectionery manufacturers – providing vegetable fat products and edible oils that make food healthier, tastier, and longer lasting. To stay competitive in a highly dynamic market that varies from one location to another, 3F must customize its offerings quickly with a high level of flexibility and accuracy.
An ERP software system running on an in-memory computing platform has brought tremendous advantages to 3F’s production operations. The company can now respond quickly to changing market conditions by tailoring daily price changes and schemes to align with different products and locations. Plus, digitalization of quality management activities is ensuring consistent control over product quality across the entire value chain – from procurement to sales – to safeguard the hard-earned trust of its consumers and customers.
Eating is the great equalizer: Everyone needs to eat to survive. And as medical science advances, we’re learning more about how what we eat can directly impact our health. Scientific innovations are helping us learn new ways we can improve our health while also greatly impacting the food and grocery industry.
It seems that new innovations in health and nutrition are happening every day. From new studies revealing the brain-gut connection to biotech that improves nutrition, the world of food is changing. Here’s a look at a few of the changes that are coming.
Wearable fitness tech
A wide range of wearable devices is available to help boost your health, along with plenty of apps to help you track that information. We’re all familiar with wearable devices that record our activity, but new innovations include necklaces that estimate how many calories we consume by listening to the sound of swallowing. While this technology is still being developed, it could help users get a better overall picture of their lifestyle and habits.
DNA-based nutritional profiling
Though the jury still seems to be out on this particular area of nutritional science, there are strong arguments on both sides of this developing trend. Several companies offer to analyze customers’ DNA to determine their best nutritional choices, although some scientists feel that science hasn’t progressed enough to provide significant information on customized nutrition based on DNA. Either way, it will be interesting to see where this technology will take us.
As scientists learn more about micronutrients, phenolic compounds, and nutraceuticals, the supplement market continues to grow. As more nutrients show exceptional health benefits, the process of standardization and regulation in the approval pipeline is also improving through Big Data, analytics, and cooperation and information-sharing among researchers across the globe. By improving the communications and computing power between these groups, we can collectively increase our understanding of treating medical conditions through better nutrition.
Nanotechnology increases nutrients
As specific compounds are linked with a wide range of benefits, the science of getting those compounds into a bioavailable format is becoming more important. One approach to this is nanotechnology, which helps address malnutrition and improves nutrition for those who have specific dietary needs due to illness or other medical conditions. In recent years, scientists from around the world have been sharing data and collaborating on research to quickly bring this new technology into the forefront of nutritional science.
Food tech is just one small part of the discussion we’re having in SAP’s Future of Food series and related presentations. Please feel free to join the conversation today to help your business stay on top of these disruptive changes.
The Digitalist Magazine is your online destination for everything you need to know to lead your enterprise’s digital transformation.
Read the Digitalist Magazine and get the latest insights about the digital economy that you can capitalize on today.
About Clemens Suter-Crazzolara
Clemens Suter-Crazzolara is the VP Chief Product Expert at SAP. He is responsible for in- and outbound product management; his focus is on software market research, defining business cases, partner management, customer engagement, go-to-market, sales support and product definition.
Only a few months ago, blockchain was considered a bit geeky and known mainly in crypto-hacking and techno-banking circles. Critics said it was a technology in search of use cases. While it’s hardly widely adopted, blockchain certainly found its feet in 2016, and we’re now seeing banks experimenting with a variety of proof-of-concept use cases around international payments, e-identity, and smart contracts. But now, blockchain has a big new target in its sights: digitizing the financial supply chain. And it has the potential to save you millions.
Before I explain how, let’s do an abridged history lesson just for context. Letters of credit, the bedrock of supplier trade finance, have been in existence for more than 3,000 years and date back to ancient Egypt. The whole point of a letter of credit was – and still is – to provide certainty of payment to the seller. But times have changed significantly since goods went off to market on the back of a camel.
As I type this, there are more than $7 trillion of payables and receivables on companies’ ledgers worldwide. The globalization of buyers and sellers, combined with promotional trading peaks such as Black Friday (which generated $5.27 billion in just one day in the U.S. in 2016), are making it increasingly costly to process letters of credit. It’s time the financial supply chain went digital. And blockchain makes that possible.
Processing letters of credit today is largely manual and expensive. And as fraud becomes more sophisticated and prevalent, it’s also becoming riskier. (The audacious €1 billion letter of credit scam back in 2008 would no doubt be far more sophisticated by today’s standards.) As the financial supply chain moves from purchase order, to material ordered, to shipping, to invoice dispatch, to invoice approval, multiple parties are involved – including a host of banks.
Blockchain provides visibility across the entire financial supply chain from the first supplier through to the end customer. This not only reduces risk, it also amplifies fiscal liquidity across the chain for all participants, including small companies, banks, and non-banks – so they can all participate safely in financing the chain with certainty.
I mentioned earlier that blockchain could save you millions. That’s because all participants in a blockchain network have a complete copy of the shared ledger where all transactions are recorded. This includes details of where the money should be sent and provides banks with an opportunity to increase the straight-through processing rate, saving both time and money. One UK clearing bank I spoke with recently believes the cost of redressing instructions for the two to three percent that require exception handling equates to the same cost of processing the remaining 97-98%. In my view, it’s an area that’s screaming out to be digitized.
It’s time to give this expensive, risky, and time-consuming process a makeover and add greater security and transparency to the financial supply chain. Blockchain has the potential to improve the way the entire financial supply chain is funded and traded globally, bringing a new wave of economic benefits.
We humans make sense of the world by looking for patterns, filtering them through what we think we already know, and making decisions accordingly. When we talk about handing decisions off to artificial intelligence (AI), we expect it to do the same, only better.
Machine learning does, in fact, have the potential to be a tremendous force for good. Humans are hindered by both their unconscious assumptions and their simple inability to process huge amounts of information. AI, on the other hand, can be taught to filter irrelevancies out of the decision-making process, pluck the most suitable candidates from a haystack of résumés, and guide us based on what it calculates is objectively best rather than simply what we’ve done in the past.
In other words, AI has the potential to help us avoid bias in hiring, operations, customer service, and the broader business and social communities—and doing so makes good business sense. For one thing, even the most unintentional discrimination can cost a company significantly, in both money and brand equity. The mere fact of having to defend against an accusation of bias can linger long after the issue itself is settled.
Beyond managing risk related to legal and regulatory issues, though, there’s a broader argument for tackling bias: in a relentlessly competitive and global economy, no organization can afford to shut itself off from broader input, more varied experiences, a wider range of talent, and larger potential markets.
That said, the algorithms that drive AI don’t reveal pure, objective truth just because they’re mathematical. Humans must tell AI what they consider suitable, teach it which information is relevant, and indicate that the outcomes they consider best—ethically, legally, and, of course, financially—are those that are free from bias, conscious or otherwise. That’s the only way AI can help us create systems that are fair, more productive, and ultimately better for both business and the broader society.
Bias: Bad for Business
When people talk about AI and machine learning, they usually mean algorithms that learn over time as they process large data sets. Organizations that have gathered vast amounts of data can use these algorithms to apply sophisticated mathematical modeling techniques to see if the results can predict future outcomes, such as fluctuations in the price of materials or traffic flows around a port facility. Computers are ideally suited to processing these massive data volumes to reveal patterns and interactions that might help organizations get ahead of their competitors. As we gather more types and sources of data with which to train increasingly complex algorithms, interest in AI will become even more intense.
Using AI for automated decision making is becoming more common, at least for simple tasks, such as recommending additional products at the point of sale based on a customer’s current and past purchases. The hope is that AI will be able to take on the process of making increasingly sophisticated decisions, such as suggesting entirely new markets where a company could be profitable, or finding the most qualified candidates for jobs by helping HR look beyond the expected demographics.
As AI takes on these increasingly complex decisions, it can help reduce bias, conscious or otherwise. By exposing a bias, algorithms allow us to lessen the impact of that bias on our decisions and actions. They enable us to make decisions that reflect objective data instead of untested assumptions; they reveal imbalances; and they alert people to their cognitive blind spots so they can make more accurate, unbiased decisions.
Imagine, for example, a major company that realizes that its past hiring practices were biased against women and that would benefit from having more women in its management pipeline. AI can help the company analyze its past job postings for gender-biased language, which might have discouraged some applicants. Future postings could be more gender neutral, increasing the number of female applicants who get past the initial screenings.
AI can also support people in making less-biased decisions. For example, a company is considering two candidates for an influential management position: one man and one woman. The final hiring decision lies with a hiring manager who, when they learn that the female candidate has a small child at home, assumes that she would prefer a part-time schedule.
That assumption may be well intentioned, but it runs counter to the outcome the company is looking for. An AI could apply corrective pressure by reminding the hiring manager that all qualifications being equal, the female candidate is an objectively good choice who meets the company’s criteria. The hope is that the hiring manager will realize their unfounded assumption and remove it from their decision-making process.
At the same time, by tracking the pattern of hiring decisions this manager makes, the AI could alert them—and other people in HR—that the company still has some remaining hidden biases against female candidates to address.
Look for Where Bias Already Exists
In other words, if we want AI to counter the effects of a biased world, we have to begin by acknowledging that the world is biased. And that starts in a surprisingly low-tech spot: identifying any biases baked into your own organization’s current processes. From there, you can determine how to address those biases and improve outcomes.
There are many scenarios where humans can collaborate with AI to prevent or even reverse bias, says Jason Baldridge, a former associate professor of computational linguistics at the University of Texas at Austin and now co-founder of People Pattern, a startup for predictive demographics using social media analytics. In the highly regulated financial services industry, for example, Baldridge says banks are required to ensure that their algorithmic choices are not based on input variables that correlate with protected demographic variables (like race and gender). The banks also have to prove to regulators that their mathematical models don’t focus on patterns that disfavor specific demographic groups, he says. What’s more, they have to allow outside data scientists to assess their models for code or data that might have a discriminatory effect. As a result, banks are more evenhanded in their lending.
Code Is Only Human
The reason for these checks and balances is clear: the algorithms that drive AI are built by humans, and humans choose the data with which to shape and train the resulting models. Because humans are prone to bias, we have to be careful that we are neither simply confirming existing biases nor introducing new ones when we develop AI models and feed them data.
“From the perspective of a business leader who wants to do the right thing, it’s a design question,” says Cathy O’Neil, whose best-selling book Weapons of Math Destruction was long-listed for the 2016 National Book Award. “You wouldn’t let your company design a car and send it out in the world without knowing whether it’s safe. You have to design it with safety standards in mind,” she says. “By the same token, algorithms have to be designed with fairness and legality in mind, with standards that are understandable to everyone, from the business leader to the people being scored.” (To learn more from O’Neil about transparency in algorithms, read Thinkers in this issue.)
Don’t Do What You’ve Always Done
To eliminate bias, you must first make sure that the data you’re using to train the algorithm is itself free of bias, or, rather, that the algorithm can recognize bias in that data and bring the bias to a human’s attention.
SAP has been working on an initiative that tackles this issue directly by spotting and categorizing gendered terminology in old job postings. Nothing as overt as No women need apply, which everyone knows is discriminatory, but phrases like outspoken and aggressively pursuing opportunities, which are proven to attract male job applicants and repel female applicants, and words like caring and flexible, which do the opposite.
Once humans categorize this language and feed it into an algorithm, the AI can learn to flag words that imply bias and suggest gender-neutral alternatives. Unfortunately, this de-biasing process currently requires too much human intervention to scale easily, but as the amount of available de-biased data grows, this will become far less of a limitation in developing AI for HR.
Similarly, companies should look for specificity in how their algorithms search for new talent. According to O’Neil, there’s no one-size-fits-all definition of the best engineer; there’s only the best engineer for a particular role or project at a particular time. That’s the needle in the haystack that AI is well suited to find.
Look Beyond the Obvious
AI could be invaluable in radically reducing deliberate and unconscious discrimination in the workplace. However, the more data your company analyzes, the more likely it is that you will deal with stereotypes, O’Neil says. If you’re looking for math professors, for example, and you load your hiring algorithm with all the data you can find about math professors, your algorithm may give a lower score to a black female candidate living in Harlem simply because there are fewer black female mathematicians in your data set. But if that candidate has a PhD in math from Cornell, and if you’ve trained your AI to prioritize that criterion, the algorithm will bump her up the list of candidates rather than summarily ruling out a potentially high-value hire on the spurious basis of race and gender.
To further improve the odds that AI will be useful, companies have to go beyond spotting relationships between data and the outcomes they care about. It doesn’t take sophisticated predictive modeling to determine, for example, that women are disproportionately likely to jump off the corporate ladder at the halfway point because they’re struggling with work/life balance.
Many companies find it all too easy to conclude that women simply aren’t qualified for middle management. However, a company committed to smart talent management will instead ask what it is about these positions that makes them incompatible with women’s lives. It will then explore what it can change so that it doesn’t lose talent and institutional knowledge that will cost the company far more to replace than to retain.
That company may even apply a second layer of machine learning that looks at its own suggestions and makes further recommendations: “It looks like you’re trying to do X, so consider doing Y,” where X might be promoting more women, making the workforce more ethnically diverse, or improving retention statistics, and Y is redefining job responsibilities with greater flexibility, hosting recruiting events in communities of color, or redesigning benefits packages based on what similar companies offer.
Context Matters—and Context Changes
Even though AI learns—and maybe because it learns—it can never be considered “set it and forget it” technology. To remain both accurate and relevant, it has to be continually trained to account for changes in the market, your company’s needs, and the data itself.
Sources for language analysis, for example, tend to be biased toward standard American English, so if you’re building models to analyze social media posts or conversational language input, Baldridge says, you have to make a deliberate effort to include and correct for slang and nonstandard dialects. Standard English applies the word sick to someone having health problems, but it’s also a popular slang term for something good or impressive, which could lead to an awkward experience if someone confuses the two meanings, to say the least. Correcting for that, or adding more rules to the algorithm, such as “The word sick appears in proximity to positive emoji,” takes human oversight.
Moving Forward with AI
Today, AI excels at making biased data obvious, but that isn’t the same as eliminating it. It’s up to human beings to pay attention to the existence of bias and enlist AI to help avoid it. That goes beyond simply implementing AI to insisting that it meet benchmarks for positive impact. The business benefits of taking this step are—or soon will be—obvious.
In IDC FutureScapes’ webcast “Worldwide Big Data, Business Analytics, and Cognitive Software 2017 Predictions,” research director David Schubmehl predicted that by 2020 perceived bias and lack of evidentiary transparency in cognitive/AI solutions will create an activist backlash movement, with up to 10% of users backing away from the technology. However, Schubmehl also speculated that consumer and enterprise users of machine learning will be far more likely to trust AI’s recommendations and decisions if they understand how those recommendations and decisions are made. That means knowing what goes into the algorithms, how they arrive at their conclusions, and whether they deliver desired outcomes that are also legally and ethically fair.
Clearly, organizations that can address this concern explicitly will have a competitive advantage, but simply stating their commitment to using AI for good may not be enough. They also may wish to support academic efforts to research AI and bias, such as the annual Fairness, Accountability, and Transparency in Machine Learning (FATML) workshop, which was held for the third time in November 2016.
O’Neil, who blogs about data science and founded the Lede Program for Data Journalism, an intensive certification program at Columbia University, is going one step further. She is attempting to create an entirely new industry dedicated to auditing and monitoring algorithms to ensure that they not only reveal bias but actively eliminate it. She proposes the formation of groups of data scientists that evaluate supply chains for signs of forced labor, connect children at risk of abuse with resources to support their families, or alert people through a smartphone app when their credit scores are used to evaluate eligibility for something other than a loan.
As we begin to entrust AI with more complex and consequential decisions, organizations may also want to be proactive about ensuring that their algorithms do good—so that their companies can use AI to do well. D!
Imagine the following situation: you are analyzing and gathering insights about product sales performance and wonder why a certain area in your country is doing better than others. You deep dive, slice, dice, and use different perspectives to analyze, but can’t find the answer to why sales are better for that region.
You conclude you need data that is not available in your corporate systems. Some geographical data that is available through Hadoop might answer your question. How can you get this information and quickly analyze it all?
Bring analytics to data
If we don’t want to go the traditional route of specifying, remodeling the data warehouse, and uploading and testing data, we’d need a whole new way of modern data warehousing. What we ultimately need is a kind of semantics that allows us to remodel our data warehouse in real time and on the fly – semantics that allows decision makers to leave the data where it is stored without populating it into the data warehouse. What we really need is a way to bring our analytics to data, instead of the other way around.
So our analytics wish list would be:
Access to the data source on the fly
Ability to remodel the data warehouse on the fly
No replication of data; the data stays where it is
Not losing time with data-load jobs
Analytical processing done in the moment with pushback to an in-memory computing platform
Drastic reduction of data objects to be stored and maintained
Elimination of aggregates
Traditional data warehousing is probably the biggest hurdle when it comes to agile business analytics. Though modern analytical tools perfectly add data sources on the fly and blend different data sources, these components are still analytical tools. When additional data must be available for multiple users or is huge in scale and complexity, analytical tools lack the computing power and scalability needed. It simply doesn’t make sense to blend them individually when multiple users require the same complex, additional data.
A data warehouse, in this case, is the answer. However, there is still one hurdle to overcome: A traditional data warehouse requires a substantial effort to adjust to new data needs. So we add to our wish list:
Adjust and adapt the modeling
Develop load and transformation script
Setup scheduling and linage
Test and maintain
In 2016, the future of data warehousing began. In-memory technology with smart, native, and real-time access moved information from analytics to the data warehouse, as well as the data warehouse to core in-memory systems. Combined with pushback technology, where analytical calculations are pushed back onto an in-memory computing platform, analytics is brought back to data. End-to-end in-memory processing has become the reality, enabling true agility. And end-to-end processing is ready for the Internet of Things at the petabyte scale.
Are we happy with this? Sure, we are! Does it come as a surprise? Of course, not! Digital transformation just enabled it!
Native, real-time access for analytics
What do next-generation data warehouses bring to analytics? Well, they allow for native access from top-end analytics components through the data warehouse and all the way to the core in-memory platform with our operational data. Even more, this native access is real-time. Every analytics-driven interaction from an end-user generates calculations. With the described architecture, these calculations are massively pushed back to the core platform where our data resides.
The same integrated architecture is also a game changer when it comes to agility and data optimization. When new, complex data is required, it can be added without data replication. Since there is no data replication, the data warehouse modeling can be done on the fly, leveraging the semantics. We no longer have to model, create, and populate new tables and aggregates when additional data is required in the data warehouse, because there are no new tables needed! We only create additional semantics, and this can be done on the fly.