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Kirill Kulikov
Kirill Kulikov

Production And Operations Analysis

Safe food production is considered as a concept of central importance because it plays an essential public health function. Unsafe food continues to be a public health problem worldwide because food borne illnesses have high prevalence locally and internationally. The objective of this study is to establish a relationship between food hygiene management and safe food production in quick service restaurants in Port Harcourt. Food hygiene management was dimensioned with personnel training, workplace design and use of personal protective equipment; while safe food production stood as standalone variable. This gave rise to three hypotheses. The cross-sectional survey of the quasi-experimental design was used to collect the data. 175 respondents returned their questionnaire copies. The data was described using frequencies and percentages while Pearson correlation statistic was used to test the hypothesis using SPSS version 21. The results showed that the dimensions of food hygiene management had significant and strong direct correlation with safe food production. Thus, it was concluded that food hygiene management is positively and significantly correlated with safe food production in quick service restaurants in Port Harcourt. Therefore, it is recommended among other things that managers of quick service restaurants in Port Harcourt should always organizes periodic staff training in order to keep members staff updated with current and safety trends in the industry.

Production and Operations Analysis


In the present era of Industry 4.0, organizations are transforming from traditional production systems to digital production systems. This transformation is in terms of additional deployment of technologies that lead to digitization and integration of products and services, business processes and customers, etc. A high volume of unstructured data is being created across different processes due to digitization. The digitization captures the data that includes text, images, multimedia, etc., due to multiplicity of platforms, e.g., machine-to-machine communications, sensors networks, cyber-physical systems, and Internet of Things. Managing this huge data generated from different sources has become a challenging task. Big data analytics (BDA) may be helpful in managing this unstructured data for effective decision making and sustainable operations. Many organizations are struggling to integrate BDA with their manufacturing processes for sustainable operations. The application of BDA from a sustainability perspective is not extensively researched in the current literature. Therefore, firstly this study explores the contribution of BDA in sustainable manufacturing operations. It further identifies strategic factors for the successful application of BDA in manufacturing for sustainable operations. For a detailed analysis of strategic factors in manufacturing, a hybrid approach comprising the analytic hierarchy process, fuzzy TOPSIS and DEMATEL is used. Results revealed that development of contract agreement among all stakeholders, engagement of top management, capability to handle big data, availability of quality and reliable data, developing team of knowledgeable, and capable decision-makers have emerged as major strategic factors for the application of BDA in the manufacturing sector for sustainable operations. Major contribution of this study is in analyzing BDA benefits for manufacturing sector, identifying major strategic factors in implementation and categorization of these factors into cause and effect group. These findings may be used by managers as guidelines for successful implementation of BDA across different functions in their respective organization to achieve sustainable operations goal. The results of this study will also motivate industry professionals to integrate BDA with their manufacturing functions for effective decision making and sustainable operations.

if(typeof ez_ad_units != 'undefined')ez_ad_units.push([[468,60],'thebusinessprofessor_com-box-4','ezslot_5',121,'0','0']);__ez_fad_position('div-gpt-ad-thebusinessprofessor_com-box-4-0');Operational analysis regards the initial analysis of what operational aspects are required to carry on the business. This is essential for determining the feasibility of a business idea.

An operational plan provides a working outline of the numerous components that make up or affect the intended business operations.Here, you examine the process by which you would exchange value with customers.

In reality the operation analysis regards the availability of resources to get started in and carry out the intended business idea. You should identify all of the actual resources (not just the cost of those resources) required to begin operations and those to carry out the business activity.

The above sections are just brief overviews of the type of operational analysis you should do in determining the feasibility of your business. You will examine the operational feasibility be comparing the anticipated availability of resources with the resource requirements that you identify in this section.

Most manufacturers have already made the most obvious changes to streamline their operations, using traditional methods to eke as much productivity out of their supply chains and plants as possible. To do even more with less in a slow-growth and uncertain environment, however, companies must look for new ways to boost the productivity and profitability of their operations.

Together, these advanced analytics approaches can deliver EBITDA (earnings before interest, taxes, depreciation, and amortization) margin improvements of as much as 4 to 10 percent. They can also boost ongoing continuous improvement efforts at a time when manufacturers have seemingly exhausted other options for increasing productivity. Moreover, they offer a lever for competitive advantage, even for companies with overcapacity, by helping them better manage their production systems and optimally reallocate resources in real time.

Oil and gas companies were early adopters of advanced analytics for predictive maintenance. One oil producer, for example, consistently faced problems with the compressors on its offshore production platforms. When one broke, the platform was forced to cease production altogether, costing the company $1 to $2 million a day. Engineers had tried for years to figure out a source of the failure with limited success. They suspected temperature or pressure of incoming fluids might be the cause, but were unable to find a correlation between either factor and the ultimate breakdown. Analyzing data from hundreds of sensors with information on 1,000 different parameters, advanced analytics revealed that high pressure and high temperature, together with several other factors, correlated with the breakdowns. The algorithm they developed could predict several weeks in advance that a compressor would go offline. While they could not prevent the failure, the company was able to decrease downtime from 14 days to just six by pre-positioning personnel and repair equipment onsite, saving millions of dollars for each occurrence.

In the same way that predictive maintenance can improve the uptime of an individual asset, YET analysis can maximize its effectiveness. Even small percentage improvements in operational efficiency can significantly enhance earnings before interest and tax (EBIT). The YET approach does that by balancing yield, throughput, and material costs to maximize the profitability of each process step.

Sometimes the changes suggested by a YET model can be simple. A steel producer, for example, had only to tweak a recipe to see a significant improvement. Other times the analysis will uncover the influence of parameters that will change over time. In those cases, the manufacturer may set up new standard operating procedures to be followed in various situations. However, the approach many manufacturers are taking is to build a performance dashboard of YET analysis in the control room fed with live data from operations, enabling production personnel to change operating conditions as indicated by the analysis.

Whereas predictive maintenance and YET analyses are designed to improve the performance and profitability of individual machines or processes, PPH maximization can optimize the interaction of those machines and processes. Encompassing every step from purchasing to production to sales, this advanced modeling technique dynamically maximizes profit generation in complex production systems and supply chains, encompassing every step from purchasing to production to sales. Unlike human planners, this advanced analytics approach typically factors in as many as 1,000 variables and 10,000 constraints to help manufacturers figure out what to buy, what to make, and how they should make it to yield the most profit in each period.

Large chemicals makers can be prime beneficiaries of PPH maximization. They must manage an enormous amount of complexity: volatile costs and prices, multiple plants, and products that can be made in various ways from diverse (and often nonlinear) combinations of materials. One global chemicals company was selling a broad range of goods to a global marketplace through a mixture of spot and long-term contracts. Decisions on production and sales were based on a complex and arcane system of transfer prices, arbitrarily set by different regions and departments. Organizational responsibilities were scattered across multiple business units and corporate functions. Management felt that suboptimal production and distribution decisions were leaving a lot of money on the table.

With the help of the model, the company identified immediate tactical changes that delivered cost savings of several million euros a year. For example, it started manufacturing an essential intermediate product on an underused line instead of buying it from a third party, and it reduced raw material costs by shifting the production of another key intermediate to equipment that gave higher yields. They identified medium-term strategic opportunities to expand capacity by increasing the throughput of some critical production assets. Moreover, they grew sales by raising the production capacity for some product categories. 041b061a72


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