Amick Brown has been certified by the US Small Business Administration (SBA) as an 8(a) Certified Small Business.  Entrance into the SBA’s 8(a) business development program was granted after Amick Brown successfully completed a rigorous application process to ensure that we met the SBA 8(a) program standards.

The 8(a) certification with its associated business development opportunities has a nine-year contract period and will allow Amick Brown to pursue sole source federal and state government contracts as well as set-aside contracts allocated to 8(a) certified companies.  We look forward to new opportunities to serve our customers in the government sector.

Anitha Brown, Managing Partner and Co-Founder commented saying “This is an exciting opportunity for Amick Brown.  Our company has had steady growth since its establishment in 2010.  Achieving 8(a) certification at this point in our history offers the potential for significant growth over the next few years.”

Amick Brown also offers the following technology services under GSA Schedule 70: SIN 132 51 – Information Technology Professional Services (NAICS 541511, 541512, 541513, 541519)

Amick Brown, LLC is an Information Technology consulting company providing IT consulting and staffing services with a focus on Cloud & Network Technologies, SAP and Business Intelligence.  We are also an SBA Woman-Owned Small Business (WOSB) and are certified by the Women Business National Council (WBENC) as a Woman-Owned Business Enterprise (WBE). We are an SAP Services Silver Partner and are IS0 9001-2015 certified.

The company was formed in 2010 by a team of experienced IT professionals. We are headquartered in San Ramon, CA and have an additional office in Sacramento, CA. Our experienced IT professionals support customers nationwide in the commercial and public sectors. Our team has mature processes, established infrastructure and the ability to scale which enables us to quickly provide resources matching our customer’s requirements.

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Our decision to pursue ISO 9001 accreditation supports our commitment to providing high-quality services to our clients and to our goals of continual improvement.  We have developed our quality management system in order to improve overall performance, to maintain a high-level of quality with regard to our services and to focus on customer satisfaction.

To become ISO 9001 compliant, Amick Brown developed key process plans and documentation, a quality manual, measurement, communication and purchasing plans, as well as processes to track non-conformances and corrective actions.  We would like to acknowledge The Core Solution.com – ISO Experts for Small Businesses (www.thecoresolution.com) for helping us to understand the standard, providing training to our team and guidance as we built processes to ensure our compliance.

Amick Brown underwent two comprehensive audits by Perry Johnson Registrars, Inc. (www.pjr.com) in order to achieve the certification.  We are proud to say that there were no non-conformances noted.

We look forward to providing high quality services to our clients and to continual improvement.

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For the second year in a row SAP has received the number one ranking in the Wisdom of Crowds 2016 Advanced and Predictive Analytics Market Study by Dresner Advisory Services. The Dresner study reached over 3000 organizations and vendors’ customer communities and 20+ industry verticals with an organization size ranging from 100 to 10,000+.

Study findings include:

• Organizations view advanced/predictive analytics as building on existing business intelligence efforts.
• Over 90% agree about the importance and value of advanced and predictive analytics.
• Statisticians/data scientists, business intelligence experts, and business analysts are the greatest adopters of advanced and predictive analytics.
• Regression models, clustering, textbook statistical functions, and geospatial analysis are the most important analytic user features/functions.
• Usability features addressing sophisticated advanced/predictive analytic users are almost uniformly important today and over time, led by easy iteration, advanced analytic support, and model iteration.
• In-memory analytics and in-database analytics are the most important scalability requirements to respondents, followed distantly by Hadoop and MPP architecture.

I find it interesting that the Dresner study finds “Hybrid roles are also evident” and confirms SAP’s customer organization usage of predictive analytics. The research study looked at core advanced and predictive features, data preparation, usability, scalability, and integration as key criteria to rank the vendors.  Though usability criteria looked at many things, I would like to highlight one key one—“Support for easy iteration”—that ranked as most important.

In the scalability criteria, “In-memory analytics” ranked as most important one followed by “In-database analytics” and “In-Hadoop analytics (on file system).”

Read the Complete Dresner Report

You can find lots more in the 92-page Dresner Wisdom of the Crowds report. I invite you to take a look.

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In our previous articles, we discussed how disruptions to a supply chain can originate from a multitude of sources. According to some current trends, it is apparent that there is continued rise in measured losses from disruptions such as natural events and business volatility. Traditionally, supply chains are designed for lean operational efficiency wherever possible, yet such efficiency requires the minimization of excess capacity, inventory and redundancy – the very things that are needed to create resiliency against disruptive risks. Risk assessment tools and methodologies help decision-makers to identify the most cost effective controls that can strike the right balance between cost and risk reduction to protect against disruption. Typically, the most cost effective controls are those that can minimize the common effects arising from multiple disruptive threats. In order to understand the kind of controls that could be effective, one must recognize the risk outcomes from common supply chain vulnerabilities, which is the focus of this article.

What is Risk?

Before continuing, it would be worthwhile to revisit some of the terminology that we have been using in previous discussion, in order to understand how risk is derived. Fundamentally, risk is the chance (or the probability) of a loss or unwanted negative consequence. For decision purposes, it is often calculated numerically as a function of probability and impact (sometimes called single loss expectancy), and quantitatively expressed as an “expected” loss in monetary value or some other units. A common flaw with using risk values is that they mask the effects of impact versus probability. For example, an expected loss of $100 does not reflect whether high impact is overwhelming low probability, or high probability is overwhelming low impact. Thus, it is not clear whether this value is the expected loss due to an event that occurs 10% of the time and causes $1000 in damages when it occurs, or due to an event that occurs 20% of the time and causes $500 in damages when it occurs. For this very reason, risk values must be used in conjunction with probability and damage values, along with many other metrics, in order for the decision maker to compare the one risk against another. Risk values are not precise and are usually not to be used as standardized values for business management. Nevertheless, risk values can be used to provide decision makers with a means to distinguish risks and control options on a relative basis. Figure 1 illustrates the fundamental parameters that are used to construct risk values, and how they relate to each other.

Figure 1 – Fundamental Components of Risk

Hazards, conditions and triggers are situations that increase or cause the likelihood of an adverse event (sometimes referred to as a peril). In our last article, we examined numerous sources of hazards that can threaten a supply chain. Vulnerabilities are factors that can make a system, in our case a supply chain, susceptible to hazards.  They are usually weaknesses that can be compromised by a hazardous condition, resulting in a threat. The likelihood, or probability, of a threat circumstance occurring must be considered, for reasons discussed above. If it occurs, failures can take place, whose effects are quantified as impacts. When impacts are weighed against the likelihood of the threat, the result is a risk that poses an expected loss. Controls are countermeasures that a firm can use to offset expected losses.

With respect to a supply chain, there are many ways to classify risk. Academics have made many attempts to try to classify risks according to some kind of ontology or framework (Harland, Brenchley and Walker 2003) (Gupta, Kumar Sahu and Khandelwal 2014) (Tummala and Schoenherr 2011) (Peck 2005) (Monroe, Teets and Martin 2012) (Chopra and Sodhi 2004). Some of the more common supply chain risk classifications include:

Recurring risks – These risks arise within the operational environment due to the inability to match supply and demand on a routine basis. The ensuing effects are lower service levels and fill rates.

Disruptive risk – These risks result from loss of supply or supplier capacity, typically driven by some disruptive event.

Exogenous risk – These risks arise within the operational environment and are process driven (e.g. poor quality control, design flaws, etc.), usually within the direct influence of the firm. They typically require the use of preventive mechanisms for control.

Endogenous risk – These risks originate externally, either from the supply side or demand side, which may not necessarily be under a firm’s direct influence. They typically involve the use of responsive mechanisms for control.

While many classification attempts have been noble in nature, in the end it is difficult to classify risks according to a single scheme, for a variety of reasons. First, the lines of demarcation between risk categories can be blurred and there could be overlap between them. For example, from the above categories, one can easily argue about the differences between exogenous and recurring risks. Second, every firm is different, and thus one framework may not fit all. Finally, risk methodology approaches may differ somewhat across various industries, as evidenced by different industry best practices and standards for risk analysis.

Supply chains can exhibit many kinds of vulnerabilities, but quite often these can be viewed as either structural or procedural in nature. Structural vulnerabilities stem from deficiencies in how the supply chain is organized, provisioned and engineered. Single points of failure can arise when there is insufficient diversity across suppliers, product sources or the geographical locations of sources. Inadequate provisioning can create shortages in inventory or capacity to meet customer demands. Procedural vulnerabilities stem from deficiencies in business or operational processes. Gaps and oversights in planning, production or transport processes could adversely affect a firm’s ability to respond to customer needs. Insufficient supply chain visibility could render a firm blind to oversights in supplier vetting and management practices, quality assurance and control, or demand planning.

Such kinds of vulnerabilities, combined with an aforementioned hazardous condition, results in the supply chain failing in some fashion. Table 1 illustrates some of the more common modes of supply chain failure.

Table 1 – Common Supply Chain Failure Modes

Ultimately, such supply chain failures result in increased costs, loss of revenue, loss of assets, or combination thereof. Common risks are typically assessed as increases in ordering costs, product costs, or safety stock costs. Product stock out losses can be assessed as backorder costs or loss of sales and business revenue. Different kinds of firms will be prone to different types of risks. For example, a manufacturing firm with long supply chains will be more susceptible to ordering variability (or bullwhip) types of effects versus a shorter retail supply chain which would be more sensitive to fill rate and service level variability. Understanding and characterizing these risks is necessary in order to develop strategies to control or manage them. Quantifying risks provides the decision maker with a gauge to assess risk before and after a control is applied, thereby assessing the prospective benefit of a potential control. Using quantified risk values, in combination with other parameters, enables a decision maker to prioritize potential control strategies according to their cost-effectiveness.

Conclusions

Risk is the chance or the probability of a loss or unwanted negative consequence. Inherent supply chain weaknesses such as sole sourcing, process gaps or lack of geographical sourcing diversity can render a supply chain more vulnerable to some hazardous, unforeseen condition or trigger event, such as a strike or major storm, resulting in undesirable increases in costs, asset loss or revenue loss. Such risks can be quantified to some extent, quite often in monetary units, and can be used to facilitate cost-benefit analysis of potential control strategies. In our next article, we will take a look some of the most favored strategies to control supply chain risk.

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Bibliography

Chopra, S., and M. Sodhi. “Managing Risk to Avoid Supply-Chain Breakdown.” MIT Sloan Management Review, 2004: 53-61.

Gupta, G., V. Kumar Sahu, and A. K. Khandelwal. “Risks in Supply Chain Management and its Mitigation.” IOSR Journal of Engineering, 2014: 42-50.

Harland, C., R. Brenchley, and H. Walker. “Risk in Supply Networks.” Journal of Purchasing & Supply Management, 2003: 51-62.

Monroe, R. W., J. M. Teets, and P. R. Martin. “A Taxonomy for Categorizing Supply Chain Events: Strategies for Addressing Supply Chain Disruptions.” SEDSI 2012 Annual Meeting Conference Proceedings. Southeast Decision Sciences Institute, 2012.

Peck, H. “Drivers of Supply Chain Vulnerability.” International Journal of Physical Distribution & Logistics Management, 2005: 210-232.

Tummala, R., and T. Schoenherr. “Assessing and Managing Risks Using the Supply Chain Risk Management Process (SCRMP).” Supply Chain Management: An International Journal, 2011: 474-483.

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In our last article, we looked at the magnitude of the supply chain risk problem and how it is a major concern for most companies – large or small. Studies have shown that most companies experience one or few supply chain disruptions annually, each resulting in some significant loss. Many of these disruptions involve key suppliers or those below Tier 1. Never the less, many firms still lack commitment to controlling supply chain risk for the reasons of the costs and complexity involved. Consequently, many firms will tend to favor short term ROI solutions versus longer-term solutions that involve investing capital to improve both their supply chain infrastructure and operational resilience. Larger firms will manage risk more strategically using a combination of executive governance and/or data driven approaches. While operational data is increasingly becoming more available, much work is still needed in leveraging such data for strategic risk management. The nature of risk in the supply chain lies with a firm’s exposure to potential disturbances to the supply chain operation. Many of these disturbances can be manifested in various ways, usually in the form of single, multiple or recurring events, conditions or phenomena. In this article, we will examine what kinds of hazards, events or triggers can possibly compromise supply chain weaknesses and can ultimately threaten supply chain operations.

The Changing Nature of Threats

When one thinks about threats to a supply chain, natural disasters usually first come to mind. Figure 1 shows the trend in major U.S. disaster declarations as reported from the Federal Emergency Management Administration (FEMA, 2011). While it is clear that there has been a rising trend in declarations, the reasons may vary from the increase in severe weather events due to climate change, to political influences. Figure 2 shows a trend in worldwide natural catastrophes (Munich RE, 2014).

Figure 1 – Trend in U.S. Disaster Declarations

Figure 2 – Trend in Worldwide Natural Catastrophic Losses

As evident in the Figure, there’s an ever growing trend in measured losses. While natural catastrophes have been occurring since the beginning of time, their effects over the years have been more far reaching due to population growth and insurability trends. These trends, combined with human created disruptions, together have created an environment of increased volatility for supply chains, as depicted in Figure 3 (Martin & Howleg, 2011).

Figure 3 – Trend in Supply Chain Volatility

This Figure shows the annual volatility in a composite set of key business parameters such as exchange rates, interest rates, shipping costs and raw material prices. They are combined into a single volatility index using the coefficient of variation (CoV) of the business indices representing these parameters to produce a normalized volatility metric. While in the far past there has been a timely return to supply chain stability following adverse events, the recent increase in volatility bandwidth questions whether this trend would likely continue. The high collective swings (versus individual swings) in key business parameters, which may be correlated with each other, suggests that an alternative approach to designing supply chains and managing supply chain risk might be preferred.

Volatility can arise from many possible undesirable hazards, conditions or trigger events. The likelihood of such events compromising a supply chain’s vulnerability is regarded as a threat. Table 1 lists categories of possible threat sources and examples within each category. The list was compiled from several studies and is not meant to be all-inclusive (Tummala & Schoenherr, 2011) (World Economic Forum, 2012) (Accenture and World Economic Forum, 2013) (Chopra & Sodhi, 2004).

Table 1 – Supply Chain Threat Sources

The Changing Course in Risk Management

Many supply chains are designed under the assumption of operating in stable environment (Martin C. H., 2011). While approaches such as Just-in-Time (JIT) and product-focused production are designed to minimize variation, maximize efficiency and ultimately reduce costs, they require a more rigid command-control management strategy which may not necessarily respond well in a volatile environment. In addition, the effects of volatility can be further amplified in a rigid supply chain that lacks resiliency. Building supply chain resiliency may counter the notion of an efficient operation, since it requires the addition and re-allocation of capacity, inventory and other resources that could serve as shock absorbers to withstand disruption. Since these controls will entail added costs, the use of a risk analysis methodology would be an effective tool in helping firms identify, evaluate and prioritize the most cost-effective risk-control options. It was clearly evident in Table 1 that there can be numerous sources of threats to a supply chain. However, since many threats can have similar outcomes on a supply chain operation, control options can be devised using an “all hazards” philosophy, which entails implementing controls to minimize the common effects of multiple threats or threat categories.

Conclusions

Supply chain disruptions can arise from many sources, both natural and man-made. Current trends indicate a continued rise in measured losses from natural events and increased business volatility in response to man-made events. Traditional supply chain structures designed for operational efficiency may not necessarily be able to withstand disruptions arising from numerous threat sources. Creating a more resilient supply chain may require the use of risk assessment tools and methods to help decision-makers identify the most cost effective controls that could minimize the common effects arising from multiple threats. In the next article, we will examine some common supply chain vulnerabilities and their ensuing risks.

Bibliography

Accenture and World Economic Forum. (2013). Building Resilience in Supply Chains. Accenture.

Chopra, S., & Sodhi, M. S. (2004). Managing Risk To Avoid Supply-Chain Breakdown. MIT Sloan Management Review, 46(1), 53-61.

FEMA. (2011). Democratic Blog News. Retrieved from http://www.demblognews.com/2011/09/water-development-board-report-says.html

Martin, C. H. (2011). Supply Chain 2.0: Managing Supply Chains in the Era of Turbulence. International Journal of Physical Distribution & Logistics Management, 41(1), 63-82.

Martin, C., & Howleg, M. (2011). Supply Chain 2.0: Managing Supply Chains in the Era of Turbulence. International Journal of Physical Distribution & Logistics Management, 41(1), 63-82.

Munich RE. (2014, January). Topics Geo: After the Floods. Munchen: Munich RE.

Tummala, R., & Schoenherr, T. (2011). Assessing and Managing Rrisks Using the Supply Chain Risk Management Process (SCRMP). Supply Chain Management: An International Journal, 16(6), 474–483.

World Economic Forum. (2012). New Models for Addressing Supply Chain and Transport Risk. World Economic Forum.

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Sr Director SAP Big Data Product Marketing, Technology & Innovation Platform

Big Data offers analysts and data scientists the opportunity to build more sophisticated and more accurate predictive models than before, but without the right data environment, it’s not easy. It requires an in-memory architecture that supports thousands of columns and billions of rows and a predictive analytics tool that can harness that architecture, such as SAP BusinessObjects Predictive Analytics.

Twentieth-century technology is insufficient. Blame it on the disk. Back in the 1980s, database engineers saw a world where memory was extremely expensive. Just one terabyte of RAM cost over $100 million US dollars. Today, we can get it for less than $5,000 US dollars. So, vendors built database architectures centered on the disk.

In a Big Data world, the disk is simply too slow. Consider this: reading 1 petabyte of data off a disk sequentially – i.e. no seeking, just end-to-end straight off the disk – takes 58 days using the fastest hard disk available today (according to the Tom’s Hardware website). SSD definitely speeds things up: two days with the fastest SSD RAID. It’ll cost millions to buy, though.

In many ways, Big Data is a real-time data access problem. That’s precisely why innovators are developing new ways to store and process data, all in an effort to get around the hard disk bottleneck. All of the approaches, in essence, minimize the bottleneck in order to improve response time.

Distributed Computing

Distributed computing spreads a lot of data across many disks that can all be read simultaneously. Hadoop builds on the concept of distributed computing, but opens up the platform to handle any data set with any arbitrarily designed algorithm. To overcome the disk, the Hadoop community built Apache Spark, which provides a distributed data processing architecture, like Hadoop HDFS, that operates in-memory across commodity hardware.

Columnar Databases

Like distributed databases and Hadoop, columnar databases optimize data storage architecture in order to reduce the amount of data read off any one disk. It does this by grouping related attributes, or columns, together. The assumption is that most analytical queries only use a subset of columns, so you should only access data related to those specific columns. They also highly compress the data, further reducing the number of bits read off disk.

In-Memory Databases

In-memory databases take it to a whole new level by removing the disk from the equation altogether. It leverages the power of today’s processors to read and analyze data at a raw speed that’s 1,000 to 10,000 times faster than reading data off the disk. In some cases, customers have experienced performance gains of 100,000 times faster. How?

–   Compress the data with in-memory columnar data stores

–   Move the data accessed most often into L1 caches on the chip

That’s why we are so bullish about in-memory and the SAP HANA platform for Big Data. That’s not to say disk solutions don’t have a role to play. But…at the core you want an in-memory system that can run algorithms where your data is. No moving the data to the algorithms, that doesn’t work in a Big Data world. Instead, move the core algorithms into the data system.

SAP BusinessObjects Predictive Analytics

SAP BusinessObjects Predictive Analytics is the right tool for business analysts and data scientists to build predictive models from Big Data. First and foremost, it can analyze data inside SAP HANA and Apache Spark. There’s no need to transfer data out of these environments for processing. Rather, the SAP BusinessObjects Predictive Analytics processing engine can run inside these tools –  dramatically improving performance.

SAP BusinessObjects Predictive Analytics is also able to analyze exceptionally wide datasets. In fact, you can have up 15,000 columns in a dataset, while other tools support only a few hundred to 1,000 columns at most. This ensures that your predictive models provide the greatest level of accuracy possible.

Big Data is radically altering our world. It’s a game changer. For those who grab hold of it, you have an opportunity to propel your business forward – and the surest way forward is with SAP BusinessObjects Predictive Analytics running on SAP HANA or Apache Spark. It is the best combination for building predictive models on Big Data, whether you’re a business analyst or data scientist.

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