Case Study Description of Sampling and Statistical Inference
An introduction to sampling and statistical inference that covers the main concepts (confidence intervals, tests of statistical significance, choice of sample size) that are needed in making inferences about a population mean or percent. Includes discussion of problems of sampling in the real world where response bias and nonrepresentativeness violate the principles on which statistical inference is based.
Swot Analysis of "Sampling and Statistical Inference" written by Arthur Schleifer Jr. includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Sampling Inference facing as an external strategic factors. Some of the topics covered in Sampling and Statistical Inference case study are - Strategic Management Strategies, Demographics, Forecasting, Market research and Strategy & Execution.
Some of the macro environment factors that can be used to understand the Sampling and Statistical Inference casestudy better are - – talent flight as more people leaving formal jobs, challanges to central banks by blockchain based private currencies, geopolitical disruptions, supply chains are disrupted by pandemic , increasing inequality as vast percentage of new income is going to the top 1%, increasing energy prices, cloud computing is disrupting traditional business models,
technology disruption, competitive advantages are harder to sustain because of technology dispersion, etc
Introduction to SWOT Analysis of Sampling and Statistical Inference
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Sampling and Statistical Inference case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Sampling Inference, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Sampling Inference operates in.
According to Harvard Business Review, 75% of the managers use SWOT analysis for various purposes such as – evaluating current scenario, strategic planning, new venture feasibility, personal growth goals, new market entry, Go To market strategies, portfolio management and strategic trade-off assessment, organizational restructuring, etc.
SWOT Objectives / Importance of SWOT Analysis and SWOT Matrix
SWOT analysis of Sampling and Statistical Inference can be done for the following purposes –
1. Strategic planning using facts provided in Sampling and Statistical Inference case study
2. Improving business portfolio management of Sampling Inference
3. Assessing feasibility of the new initiative in Strategy & Execution field.
4. Making a Strategy & Execution topic specific business decision
5. Set goals for the organization
6. Organizational restructuring of Sampling Inference
Strengths Sampling and Statistical Inference | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Sampling Inference in Sampling and Statistical Inference Harvard Business Review case study are -
Effective Research and Development (R&D)
– Sampling Inference has innovation driven culture where significant part of the revenues are spent on the research and development activities. This has resulted in, as mentioned in case study Sampling and Statistical Inference - staying ahead in the industry in terms of – new product launches, superior customer experience, highly competitive pricing strategies, and great returns to the shareholders.
Cross disciplinary teams
– Horizontal connected teams at the Sampling Inference are driving operational speed, building greater agility, and keeping the organization nimble to compete with new competitors. It helps are organization to ideate new ideas, and execute them swiftly in the marketplace.
Training and development
– Sampling Inference has one of the best training and development program in the industry. The effectiveness of the training programs can be measured in Sampling and Statistical Inference Harvard Business Review case study by analyzing – employees retention, in-house promotion, loyalty, new venture initiation, lack of conflict, and high level of both employees and customer engagement.
Ability to lead change in Strategy & Execution field
– Sampling Inference is one of the leading players in its industry. Over the years it has not only transformed the business landscape in its segment but also across the whole industry. The ability to lead change has enabled Sampling Inference in – penetrating new markets, reaching out to new customers, and providing different value propositions to different customers in the international markets.
Analytics focus
– Sampling Inference is putting a lot of focus on utilizing the power of analytics in business decision making. This has put it among the leading players in the industry. The technology infrastructure suggested by Arthur Schleifer Jr. can also help it to harness the power of analytics for – marketing optimization, demand forecasting, customer relationship management, inventory management, information sharing across the value chain etc.
Low bargaining power of suppliers
– Suppliers of Sampling Inference in the sector have low bargaining power. Sampling and Statistical Inference has further diversified its suppliers portfolio by building a robust supply chain across various countries. This helps Sampling Inference to manage not only supply disruptions but also source products at highly competitive prices.
Diverse revenue streams
– Sampling Inference is present in almost all the verticals within the industry. This has provided firm in Sampling and Statistical Inference case study a diverse revenue stream that has helped it to survive disruptions such as global pandemic in Covid-19, financial disruption of 2008, and supply chain disruption of 2021.
Sustainable margins compare to other players in Strategy & Execution industry
– Sampling and Statistical Inference firm has clearly differentiated products in the market place. This has enabled Sampling Inference to fetch slight price premium compare to the competitors in the Strategy & Execution industry. The sustainable margins have also helped Sampling Inference to invest into research and development (R&D) and innovation.
Highly skilled collaborators
– Sampling Inference has highly efficient outsourcing and offshoring strategy. It has resulted in greater operational flexibility and bringing down the costs in highly price sensitive segment. Secondly the value chain collaborators of the firm in Sampling and Statistical Inference HBR case study have helped the firm to develop new products and bring them quickly to the marketplace.
Ability to recruit top talent
– Sampling Inference is one of the leading recruiters in the industry. Managers in the Sampling and Statistical Inference are in a position to attract the best talent available. The firm has a robust talent identification program that helps in identifying the brightest.
Strong track record of project management
– Sampling Inference is known for sticking to its project targets. This enables the firm to manage – time, project costs, and have sustainable margins on the projects.
Learning organization
- Sampling Inference is a learning organization. It has inculcated three key characters of learning organization in its processes and operations – exploration, creativity, and expansiveness. The work place at Sampling Inference is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders in Sampling and Statistical Inference Harvard Business Review case study emphasize – knowledge, initiative, and innovation.
Weaknesses Sampling and Statistical Inference | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Sampling and Statistical Inference are -
Capital Spending Reduction
– Even during the low interest decade, Sampling Inference has not been able to do capital spending to the tune of the competition. This has resulted into fewer innovations and company facing stiff competition from both existing competitors and new entrants who are disrupting the industry using digital technology.
Interest costs
– Compare to the competition, Sampling Inference has borrowed money from the capital market at higher rates. It needs to restructure the interest payment and costs so that it can compete better and improve profitability.
Aligning sales with marketing
– It come across in the case study Sampling and Statistical Inference that the firm needs to have more collaboration between its sales team and marketing team. Sales professionals in the industry have deep experience in developing customer relationships. Marketing department in the case Sampling and Statistical Inference can leverage the sales team experience to cultivate customer relationships as Sampling Inference is planning to shift buying processes online.
Slow to strategic competitive environment developments
– As Sampling and Statistical Inference HBR case study mentions - Sampling Inference takes time to assess the upcoming competitions. This has led to missing out on atleast 2-3 big opportunities in the industry in last five years.
High dependence on star products
– The top 2 products and services of the firm as mentioned in the Sampling and Statistical Inference HBR case study still accounts for major business revenue. This dependence on star products in has resulted into insufficient focus on developing new products, even though Sampling Inference has relatively successful track record of launching new products.
High operating costs
– Compare to the competitors, firm in the HBR case study Sampling and Statistical Inference has high operating costs in the. This can be harder to sustain given the new emerging competition from nimble players who are using technology to attract Sampling Inference 's lucrative customers.
No frontier risks strategy
– After analyzing the HBR case study Sampling and Statistical Inference, it seems that company is thinking about the frontier risks that can impact Strategy & Execution strategy. But it has very little resources allocation to manage the risks emerging from events such as natural disasters, climate change, melting of permafrost, tacking the rise of artificial intelligence, opportunities and threats emerging from commercialization of space etc.
High dependence on existing supply chain
– The disruption in the global supply chains because of the Covid-19 pandemic and blockage of the Suez Canal illustrated the fragile nature of Sampling Inference supply chain. Even after few cautionary changes mentioned in the HBR case study - Sampling and Statistical Inference, it is still heavily dependent upon the existing supply chain. The existing supply chain though brings in cost efficiencies but it has left Sampling Inference vulnerable to further global disruptions in South East Asia.
Slow decision making process
– As mentioned earlier in the report, Sampling Inference has a very deliberative decision making approach. This approach has resulted in prudent decisions, but it has also resulted in missing opportunities in the industry over the last five years. Sampling Inference even though has strong showing on digital transformation primary two stages, it has struggled to capitalize the power of digital transformation in marketing efforts and new venture efforts.
Compensation and incentives
– The revenue per employee as mentioned in the HBR case study Sampling and Statistical Inference, is just above the industry average. Sampling Inference needs to redesign the compensation structure and incentives to increase the revenue per employees. Some of the steps that it can take are – hiring more specialists on project basis, etc.
Increasing silos among functional specialists
– The organizational structure of Sampling Inference is dominated by functional specialists. It is not different from other players in the Strategy & Execution segment. Sampling Inference needs to de-silo the office environment to harness the true potential of its workforce. Secondly the de-silo will also help Sampling Inference to focus more on services rather than just following the product oriented approach.
Opportunities Sampling and Statistical Inference | External Strategic Factors
What are Opportunities in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The opportunities highlighted in the Harvard Business Review case study Sampling and Statistical Inference are -
Identify volunteer opportunities
– Covid-19 has impacted working population in two ways – it has led to people soul searching about their professional choices, resulting in mass resignation. Secondly it has encouraged people to do things that they are passionate about. This has opened opportunities for businesses to build volunteer oriented socially driven projects. Sampling Inference can explore opportunities that can attract volunteers and are consistent with its mission and vision.
Lowering marketing communication costs
– 5G expansion will open new opportunities for Sampling Inference in the field of marketing communication. It will bring down the cost of doing business, provide technology platform to build new products in the Strategy & Execution segment, and it will provide faster access to the consumers.
Remote work and new talent hiring opportunities
– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Sampling Inference to expand its talent hiring zone. According to McKinsey Global Institute, 20% of the high end workforce in fields such as finance, information technology, can continously work from remote local post Covid-19. This presents a really great opportunity for Sampling Inference to hire the very best people irrespective of their geographical location.
Building a culture of innovation
– managers at Sampling Inference can make experimentation a productive activity and build a culture of innovation using approaches such as – mining transaction data, A/B testing of websites and selling platforms, engaging potential customers over various needs, and building on small ideas in the Strategy & Execution segment.
Creating value in data economy
– The success of analytics program of Sampling Inference has opened avenues for new revenue streams for the organization in the industry. This can help Sampling Inference to build a more holistic ecosystem as suggested in the Sampling and Statistical Inference case study. Sampling Inference can build new products and services such as - data insight services, data privacy related products, data based consulting services, etc.
Changes in consumer behavior post Covid-19
– Consumer behavior has changed in the Strategy & Execution industry because of Covid-19 restrictions. Some of this behavior will stay once things get back to normal. Sampling Inference can take advantage of these changes in consumer behavior to build a far more efficient business model. For example consumer regular ordering of products can reduce both last mile delivery costs and market penetration costs. Sampling Inference can further use this consumer data to build better customer loyalty, provide better products and service collection, and improve the value proposition in inflationary times.
Harnessing reconfiguration of the global supply chains
– As the trade war between US and China heats up in the coming years, Sampling Inference can build a diversified supply chain model across various countries in - South East Asia, India, and other parts of the world. This reconfiguration of global supply chain can help, as suggested in case study, Sampling and Statistical Inference, to buy more products closer to the markets, and it can leverage its size and influence to get better deal from the local markets.
Low interest rates
– Even though inflation is raising its head in most developed economies, Sampling Inference can still utilize the low interest rates to borrow money for capital investment. Secondly it can also use the increase of government spending in infrastructure projects to get new business.
Learning at scale
– Online learning technologies has now opened space for Sampling Inference to conduct training and development for its employees across the world. This will result in not only reducing the cost of training but also help employees in different part of the world to integrate with the headquarter work culture, ethos, and standards.
Using analytics as competitive advantage
– Sampling Inference has spent a significant amount of money and effort to integrate analytics and machine learning into its operations in the sector. This continuous investment in analytics has enabled, as illustrated in the Harvard case study Sampling and Statistical Inference - to build a competitive advantage using analytics. The analytics driven competitive advantage can help Sampling Inference to build faster Go To Market strategies, better consumer insights, developing relevant product features, and building a highly efficient supply chain.
Redefining models of collaboration and team work
– As explained in the weaknesses section, Sampling Inference is facing challenges because of the dominance of functional experts in the organization. Sampling and Statistical Inference case study suggests that firm can utilize new technology to build more coordinated teams and streamline operations and communications using tools such as CAD, Zoom, etc.
Buying journey improvements
– Sampling Inference can improve the customer journey of consumers in the industry by using analytics and artificial intelligence. Sampling and Statistical Inference suggest that firm can provide automated chats to help consumers solve their own problems, provide online suggestions to get maximum out of the products and services, and help consumers to build a community where they can interact with each other to develop new features and uses.
Use of Bitcoin and other crypto currencies for transactions
– The popularity of Bitcoin and other crypto currencies as asset class and medium of transaction has opened new opportunities for Sampling Inference in the consumer business. Now Sampling Inference can target international markets with far fewer capital restrictions requirements than the existing system.
Threats Sampling and Statistical Inference External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats mentioned in the HBR case study Sampling and Statistical Inference are -
Technology disruption because of hacks, piracy etc
– The colonial pipeline illustrated, how vulnerable modern organization are to international hackers, miscreants, and disruptors. The cyber security interruption, data leaks, etc can seriously jeopardize the future growth of the organization.
Capital market disruption
– During the Covid-19, Dow Jones has touched record high. The valuations of a number of companies are way beyond their existing business model potential. This can lead to capital market correction which can put a number of suppliers, collaborators, value chain partners in great financial difficulty. It will directly impact the business of Sampling Inference.
Environmental challenges
– Sampling Inference needs to have a robust strategy against the disruptions arising from climate change and energy requirements. EU has identified it as key priority area and spending 30% of its 880 billion Euros European post Covid-19 recovery funds on green technology. Sampling Inference can take advantage of this fund but it will also bring new competitors in the Strategy & Execution industry.
Backlash against dominant players
– US Congress and other legislative arms of the government are getting tough on big business especially technology companies. The digital arm of Sampling Inference business can come under increasing regulations regarding data privacy, data security, etc.
Easy access to finance
– Easy access to finance in Strategy & Execution field will also reduce the barriers to entry in the industry, thus putting downward pressure on the prices because of increasing competition. Sampling Inference can utilize it by borrowing at lower rates and invest it into research and development, capital expenditure to fortify its core competitive advantage.
Increasing wage structure of Sampling Inference
– Post Covid-19 there is a sharp increase in the wages especially in the jobs that require interaction with people. The increasing wages can put downward pressure on the margins of Sampling Inference.
Regulatory challenges
– Sampling Inference needs to prepare for regulatory challenges as consumer protection groups and other pressure groups are vigorously advocating for more regulations on big business - to reduce inequality, to create a level playing field, to product data privacy and consumer privacy, to reduce the influence of big money on democratic institutions, etc. This can lead to significant changes in the Strategy & Execution industry regulations.
High level of anxiety and lack of motivation
– the Great Resignation in United States is the sign of broader dissatisfaction among the workforce in United States. Sampling Inference needs to understand the core reasons impacting the Strategy & Execution industry. This will help it in building a better workplace.
New competition
– After the dotcom bust of 2001, financial crisis of 2008-09, the business formation in US economy had declined. But in 2020 alone, there are more than 1.5 million new business applications in United States. This can lead to greater competition for Sampling Inference in the Strategy & Execution sector and impact the bottomline of the organization.
Instability in the European markets
– European Union markets are facing three big challenges post Covid – expanded balance sheets, Brexit related business disruption, and aggressive Russia looking to distract the existing security mechanism. Sampling Inference will face different problems in different parts of Europe. For example it will face inflationary pressures in UK, France, and Germany, balance sheet expansion and demand challenges in Southern European countries, and geopolitical instability in the Eastern Europe.
Trade war between China and United States
– The trade war between two of the biggest economies can hugely impact the opportunities for Sampling Inference in the Strategy & Execution industry. The Strategy & Execution industry is already at various protected from local competition in China, with the rise of trade war the protection levels may go up. This presents a clear threat of current business model in Chinese market.
Consumer confidence and its impact on Sampling Inference demand
– There is a high probability of declining consumer confidence, given – high inflammation rate, rise of gig economy, lower job stability, increasing cost of living, higher interest rates, and aging demography. All the factors contribute to people saving higher rate of their income, resulting in lower consumer demand in the industry and other sectors.
Stagnating economy with rate increase
– Sampling Inference can face lack of demand in the market place because of Fed actions to reduce inflation. This can lead to sluggish growth in the economy, lower demands, lower investments, higher borrowing costs, and consolidation in the field.
Weighted SWOT Analysis of Sampling and Statistical Inference Template, Example
Not all factors mentioned under the Strengths, Weakness, Opportunities, and Threats quadrants in the SWOT Analysis are equal. Managers in the HBR case study Sampling and Statistical Inference needs to zero down on the relative importance of each factor mentioned in the Strengths, Weakness, Opportunities, and Threats quadrants.
We can provide the relative importance to each factor by assigning relative weights. Weighted SWOT analysis process is a three stage process –
First stage for doing weighted SWOT analysis of the case study Sampling and Statistical Inference is to rank the strengths and weaknesses of the organization. This will help you to assess the most important strengths and weaknesses of the firm and which one of the strengths and weaknesses mentioned in the initial lists are marginal and can be left out.
Second stage for conducting weighted SWOT analysis of the Harvard case study Sampling and Statistical Inference is to give probabilities to the external strategic factors thus better understanding the opportunities and threats arising out of macro environment changes and developments.
Third stage of constructing weighted SWOT analysis of Sampling and Statistical Inference is to provide strategic recommendations includes – joining likelihood of external strategic factors such as opportunities and threats to the internal strategic factors – strengths and weaknesses. You should start with external factors as they will provide the direction of the overall industry. Secondly by joining probabilities with internal strategic factors can help the company not only strategic fit but also the most probably strategic trade-off that Sampling Inference needs to make to build a sustainable competitive advantage.
