Statistical Quality Control for Process Improvement SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
Technology & Operations
Strategy / MBA Resources
Case Study SWOT Analysis Solution
Case Study Description of Statistical Quality Control for Process Improvement
Describes systematic methods for process debugging and improvement, based on statistical quality control. Examples are from manufacturing settings, but techniques are also useful for services and sales, and to quantity improvement as well as quality improvement.
Swot Analysis of "Statistical Quality Control for Process Improvement" written by Roger E. Bohn includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Improvement Statistical facing as an external strategic factors. Some of the topics covered in Statistical Quality Control for Process Improvement case study are - Strategic Management Strategies, Business processes, Developing employees, Manufacturing, Product development and Technology & Operations.
Some of the macro environment factors that can be used to understand the Statistical Quality Control for Process Improvement casestudy better are - – increasing energy prices, competitive advantages are harder to sustain because of technology dispersion, technology disruption, there is increasing trade war between United States & China, increasing commodity prices, banking and financial system is disrupted by Bitcoin and other crypto currencies, there is backlash against globalization,
increasing government debt because of Covid-19 spendings, talent flight as more people leaving formal jobs, etc
Introduction to SWOT Analysis of Statistical Quality Control for Process Improvement
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Statistical Quality Control for Process Improvement case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Improvement Statistical, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Improvement Statistical 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 Statistical Quality Control for Process Improvement can be done for the following purposes –
1. Strategic planning using facts provided in Statistical Quality Control for Process Improvement case study
2. Improving business portfolio management of Improvement Statistical
3. Assessing feasibility of the new initiative in Technology & Operations field.
4. Making a Technology & Operations topic specific business decision
5. Set goals for the organization
6. Organizational restructuring of Improvement Statistical
Strengths Statistical Quality Control for Process Improvement | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Improvement Statistical in Statistical Quality Control for Process Improvement Harvard Business Review case study are -
Low bargaining power of suppliers
– Suppliers of Improvement Statistical in the sector have low bargaining power. Statistical Quality Control for Process Improvement has further diversified its suppliers portfolio by building a robust supply chain across various countries. This helps Improvement Statistical to manage not only supply disruptions but also source products at highly competitive prices.
Sustainable margins compare to other players in Technology & Operations industry
– Statistical Quality Control for Process Improvement firm has clearly differentiated products in the market place. This has enabled Improvement Statistical to fetch slight price premium compare to the competitors in the Technology & Operations industry. The sustainable margins have also helped Improvement Statistical to invest into research and development (R&D) and innovation.
Learning organization
- Improvement Statistical 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 Improvement Statistical is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders in Statistical Quality Control for Process Improvement Harvard Business Review case study emphasize – knowledge, initiative, and innovation.
Successful track record of launching new products
– Improvement Statistical has launched numerous new products in last few years, keeping in mind evolving customer preferences and competitive pressures. Improvement Statistical has effective processes in place that helps in exploring new product needs, doing quick pilot testing, and then launching the products quickly using its extensive distribution network.
High switching costs
– The high switching costs that Improvement Statistical has built up over years in its products and services combo offer has resulted in high retention of customers, lower marketing costs, and greater ability of the firm to focus on its customers.
Cross disciplinary teams
– Horizontal connected teams at the Improvement Statistical 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.
Digital Transformation in Technology & Operations segment
- digital transformation varies from industry to industry. For Improvement Statistical digital transformation journey comprises differing goals based on market maturity, customer technology acceptance, and organizational culture. Improvement Statistical has successfully integrated the four key components of digital transformation – digital integration in processes, digital integration in marketing and customer relationship management, digital integration into the value chain, and using technology to explore new products and market opportunities.
Diverse revenue streams
– Improvement Statistical is present in almost all the verticals within the industry. This has provided firm in Statistical Quality Control for Process Improvement 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.
Operational resilience
– The operational resilience strategy in the Statistical Quality Control for Process Improvement Harvard Business Review case study comprises – understanding the underlying the factors in the industry, building diversified operations across different geographies so that disruption in one part of the world doesn’t impact the overall performance of the firm, and integrating the various business operations and processes through its digital transformation drive.
Innovation driven organization
– Improvement Statistical is one of the most innovative firm in sector. Manager in Statistical Quality Control for Process Improvement Harvard Business Review case study can use Clayton Christensen Disruptive Innovation strategies to further increase the scale of innovtions in the organization.
Effective Research and Development (R&D)
– Improvement Statistical 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 Statistical Quality Control for Process Improvement - staying ahead in the industry in terms of – new product launches, superior customer experience, highly competitive pricing strategies, and great returns to the shareholders.
Analytics focus
– Improvement Statistical 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 Roger E. Bohn 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.
Weaknesses Statistical Quality Control for Process Improvement | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Statistical Quality Control for Process Improvement are -
Slow decision making process
– As mentioned earlier in the report, Improvement Statistical 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. Improvement Statistical 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.
High cash cycle compare to competitors
Improvement Statistical has a high cash cycle compare to other players in the industry. It needs to shorten the cash cycle by 12% to be more competitive in the marketplace, reduce inventory costs, and be more profitable.
Capital Spending Reduction
– Even during the low interest decade, Improvement Statistical 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.
Lack of clear differentiation of Improvement Statistical products
– To increase the profitability and margins on the products, Improvement Statistical needs to provide more differentiated products than what it is currently offering in the marketplace.
Interest costs
– Compare to the competition, Improvement Statistical 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.
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 Improvement Statistical supply chain. Even after few cautionary changes mentioned in the HBR case study - Statistical Quality Control for Process Improvement, it is still heavily dependent upon the existing supply chain. The existing supply chain though brings in cost efficiencies but it has left Improvement Statistical vulnerable to further global disruptions in South East Asia.
Ability to respond to the competition
– As the decision making is very deliberative, highlighted in the case study Statistical Quality Control for Process Improvement, in the dynamic environment Improvement Statistical has struggled to respond to the nimble upstart competition. Improvement Statistical has reasonably good record with similar level competitors but it has struggled with new entrants taking away niches of its business.
Products dominated business model
– Even though Improvement Statistical has some of the most successful products in the industry, this business model has made each new product launch extremely critical for continuous financial growth of the organization. firm in the HBR case study - Statistical Quality Control for Process Improvement should strive to include more intangible value offerings along with its core products and services.
Employees’ incomplete understanding of strategy
– From the instances in the HBR case study Statistical Quality Control for Process Improvement, it seems that the employees of Improvement Statistical don’t have comprehensive understanding of the firm’s strategy. This is reflected in number of promotional campaigns over the last few years that had mixed messaging and competing priorities. Some of the strategic activities and services promoted in the promotional campaigns were not consistent with the organization’s strategy.
Skills based hiring
– The stress on hiring functional specialists at Improvement Statistical has created an environment where the organization is dominated by functional specialists rather than management generalist. This has resulted into product oriented approach rather than marketing oriented approach or consumers oriented approach.
High bargaining power of channel partners
– Because of the regulatory requirements, Roger E. Bohn suggests that, Improvement Statistical is facing high bargaining power of the channel partners. So far it has not able to streamline the operations to reduce the bargaining power of the value chain partners in the industry.
Opportunities Statistical Quality Control for Process Improvement | 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 Statistical Quality Control for Process Improvement are -
Low interest rates
– Even though inflation is raising its head in most developed economies, Improvement Statistical 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 Improvement Statistical 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.
Building a culture of innovation
– managers at Improvement Statistical 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 Technology & Operations segment.
Developing new processes and practices
– Improvement Statistical can develop new processes and procedures in Technology & Operations industry using technology such as automation using artificial intelligence, real time transportation and products tracking, 3D modeling for concept development and new products pilot testing etc.
Leveraging digital technologies
– Improvement Statistical can leverage digital technologies such as artificial intelligence and machine learning to automate the production process, customer analytics to get better insights into consumer behavior, realtime digital dashboards to get better sales tracking, logistics and transportation, product tracking, etc.
Remote work and new talent hiring opportunities
– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Improvement Statistical 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 Improvement Statistical to hire the very best people irrespective of their geographical location.
Loyalty marketing
– Improvement Statistical has focused on building a highly responsive customer relationship management platform. This platform is built on in-house data and driven by analytics and artificial intelligence. The customer analytics can help the organization to fine tune its loyalty marketing efforts, increase the wallet share of the organization, reduce wastage on mainstream advertising spending, build better pricing strategies using personalization, etc.
Harnessing reconfiguration of the global supply chains
– As the trade war between US and China heats up in the coming years, Improvement Statistical 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, Statistical Quality Control for Process Improvement, to buy more products closer to the markets, and it can leverage its size and influence to get better deal from the local markets.
Finding new ways to collaborate
– Covid-19 has not only transformed business models of companies in Technology & Operations industry, but it has also influenced the consumer preferences. Improvement Statistical can tie-up with other value chain partners to explore new opportunities regarding meeting customer demands and building a rewarding and engaging relationship.
Lowering marketing communication costs
– 5G expansion will open new opportunities for Improvement Statistical in the field of marketing communication. It will bring down the cost of doing business, provide technology platform to build new products in the Technology & Operations segment, and it will provide faster access to the consumers.
Reforming the budgeting process
- By establishing new metrics that will be used to evaluate both existing and potential projects Improvement Statistical can not only reduce the costs of the project but also help it in integrating the projects with other processes within the organization.
Changes in consumer behavior post Covid-19
– Consumer behavior has changed in the Technology & Operations industry because of Covid-19 restrictions. Some of this behavior will stay once things get back to normal. Improvement Statistical 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. Improvement Statistical 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.
Increase in government spending
– As the United States and other governments are increasing social spending and infrastructure spending to build economies post Covid-19, Improvement Statistical can use these opportunities to build new business models that can help the communities that Improvement Statistical operates in. Secondly it can use opportunities from government spending in Technology & Operations sector.
Threats Statistical Quality Control for Process Improvement External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats mentioned in the HBR case study Statistical Quality Control for Process Improvement are -
Aging population
– As the populations of most advanced economies are aging, it will lead to high social security costs, higher savings among population, and lower demand for goods and services in the economy. The household savings in US, France, UK, Germany, and Japan are growing faster than predicted because of uncertainty caused by pandemic.
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. Improvement Statistical 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.
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.
Environmental challenges
– Improvement Statistical 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. Improvement Statistical can take advantage of this fund but it will also bring new competitors in the Technology & Operations industry.
Easy access to finance
– Easy access to finance in Technology & Operations field will also reduce the barriers to entry in the industry, thus putting downward pressure on the prices because of increasing competition. Improvement Statistical can utilize it by borrowing at lower rates and invest it into research and development, capital expenditure to fortify its core competitive advantage.
High dependence on third party suppliers
– Improvement Statistical high dependence on third party suppliers can disrupt its processes and delivery mechanism. For example -the current troubles of car makers because of chip shortage is because the chip companies started producing chips for electronic companies rather than car manufacturers.
Barriers of entry lowering
– As technology is more democratized, the barriers to entry in the industry are lowering. It can presents Improvement Statistical with greater competitive threats in the near to medium future. Secondly it will also put downward pressure on pricing throughout the sector.
Shortening product life cycle
– it is one of the major threat that Improvement Statistical is facing in Technology & Operations sector. It can lead to higher research and development costs, higher marketing expenses, lower customer loyalty, etc.
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 Improvement Statistical.
Stagnating economy with rate increase
– Improvement Statistical 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.
Regulatory challenges
– Improvement Statistical 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 Technology & Operations industry regulations.
Increasing wage structure of Improvement Statistical
– 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 Improvement Statistical.
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 Improvement Statistical in the Technology & Operations sector and impact the bottomline of the organization.
Weighted SWOT Analysis of Statistical Quality Control for Process Improvement 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 Statistical Quality Control for Process Improvement 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 Statistical Quality Control for Process Improvement 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 Statistical Quality Control for Process Improvement 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 Statistical Quality Control for Process Improvement 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 Improvement Statistical needs to make to build a sustainable competitive advantage.