Business Strategy of Indah Kiat Pulp and Paper Perawang Mill, Riau, Indonesia using PESTLE, Porter’s Five Forces, and SWOT Analysis under SOSTAC® Framework

According to the company annual report (INKP Annual Report, 2019), PT. Indah Kiat Pulp and Paper (INKP) experienced a decrease in net sales of 3.4% and net profit of 53.4% from the previous year. The decline was followed by the Covid-19 pandemic which could affect the company's sales and profits. The study was conducted using a descriptive approach in order to capture the phenomena that occur through analysis of current conditions both internal and external using PESTLE, Porter, and SWOT analysis. Afterwards, it formulates the company objective, strategy, tactics, action, and evaluation or control using the SOSTAC® framework based on the analysis result. Data are obtained from interviews, observations, documentation, and publication reports. The results of the research describe that the predominant internal strategy factors on the Strengths of INKP are the product quality and production process efficiency, and the Weaknesses are product distribution and its distribution channels. Meanwhile, the predominant external strategic factors on Opportunities are other needs in pulp derivative product, availability of raw materials and fiscal policy, and as for Threats are a decrease in fine paper demand and technology adaptation. Using the Cartesian SWOT diagram, it is found that the results of EFAS and IFAS are in the ST main strategy quadrant, which is Diversification. Based on the results of the analysis, it can be formulated that the objectives of the INKP strategy are to make diversification product such as rayon fiber with masks as the final product from pulp as the raw materials in 1 year and shifting production from 56% paper and 43% pulp to 45% paper, 34% pulp, and 20% rayon fiber in 3 years to maintain sales volume and gain increased profit. The corporate level strategy is a diversification product while the business level strategy is a differentiation product in accordance with Porter's Generic Matrix. This investment has a positive value of both CAPM and NPV while CAPM has 0.0708 value. As for the IRR, the rate of return is 39.87%. Planned tactics and actions using project management with Gantt Chart within a project period of 1 year in the main activities of building production lines and marketing. Control and evaluation are carried out through the implementation of targets or KPI in accordance with work functions. EBIT and EBITDA is used at the final level of overall production and marketing.

Anti-dripping flame retardancy and mechanical properties of polylactide/ammonium polyphosphate/rayon fiber composites

Polylactide (PLA) composites containing a flame retadant, ammonium polyphosphate (APP), and short rayon fiber were prepared by direct melting compounding in a brabender. The limiting oxygen index (LOI) of the neat PLA sample was only 20.5%, which was increased to 29% by adding 15 wt% APP and 15% rayon to the PLA matrix (sample A15R15) as an example. During the UL-94 vertical flammability test, flame dripping was further avoided by adding the rayon fiber, and a V-0 rating was achieved. The char residue determined by thermogravimetric analysis (TGA) increased with increasing APP content in the PLA composites. However, the PLA composite revealed a loss in mechanical tensile modulus and strength due to the APP addition, which was improved when rayon fiber was added to replace a portion of APP.

DISSOLVING PULP FROM NON-WOOD PLANTS BY PREHYDROLYSISPOTASSIUM HYDROXIDE PROCESS

The garment industry is the backbone of Bangladesh’s economy, which imports 30000 metric tons of rayon fiber every year. Bangladesh used to have a rayon plant, but it was shut down a long time ago. At present, the establishment of a new rayon plant in Bangladesh has become an objective. As a forest deficient country, non-wood plants need to be explored for rayon grade dissolving pulp production. Therefore, in this paper, prehydrolysis potassium hydroxide (KOH) pulping process has been investigated for dissolving pulp production from rice and wheat straws, corn stalk, dhaincha and jute stick. The lowest prehydrolysis yield was 70.1% obtained for corn stalks and the highest prehydrolysis yield was 92.1% for wheat straw. The KOH cooking of prehydrolysed rice straw, wheat straw and corn stalks with 14% alkali charge produced pulps with kappa numbers of 5.7, 4.5 and 8.8, respectively, while prehydrolysed dhaincha and jute stick needed 18% alkali charge to get a bleachable pulp. Dhaincha showed the highest pulp yield (37.3%) with the highest α-cellulose content (92.24%) and the lowest residual pentosan content (5.37%). Further purification with cold KOH extraction increased purity by 3% for rice straw pulp and by 1% for dhaincha, wheat straw, corn stalks and jute stick pulp. Thus, the dissolving pulp produced in this study by the prehydrolysis KOH process can meet the criteria for rayon grade pulp.

The Mechanical and Comfort Properties of Sustainable Blended Fabrics of Bamboo With Cotton and Regenerated Fibers

The worldwide growing need of cotton but its lower production has boosted the production of regenerated cellulosic fibers. This work compares the thermal comfort and mechanical properties of bamboo rayon fiber blends with cotton and other regenerated fibers. So, bamboo rayon fibers were blended with cotton, tencel lyocell, modal rayon, and viscose rayon. One-hundred-percent pure fabrics of bamboo rayon, cotton, tencel lyocell, modal rayon, and viscose rayon were made. Also, 50:50 blends of bamboo rayon with cotton, tencel lyocell, modal rayon, and viscose rayon were prepared. Plain-woven fabrics were made by using yarns of 20 tex. The thermal comfort and mechanical properties were analyzed. It is found that 100% tencel lyocell fabrics give higher mechanical and comfort properties. Similarly, bamboo rayon:tencel lyocell (50:50)–blended fabric gives better thermal comfort and mechanical properties than bamboo rayon:cotton–, bamboo rayon:modal rayon–, and bamboo rayon:viscose rayon–blended fabrics.

Utilizing Rayon Fiber Residues from Fiber Manufacturing Industry for Preparation of Cellulose/CMC Hydrogels

The cellulose-based hydrogel was successfully prepared from rayon fiber residue obtained from the fiber manufacturing industry. By chemical means, the hydrogel was simply prepared at an ambient temperature by mixing rayon with carboxymethyl cellulose (CMC) in NaOH/urea solution with epichlorohydrin (ECH) as a crosslinking agent. Rayon cellulose was used for stabilizing of hydrogel structure, providing a dimensional stability to the hydrogel whereas CMC acted as a porogen, widening the pore size within the hydrogel structure while swelling in water. With increasing CMC content, the percent water uptake of the hydrogel was increased but the structural stability was impaired. The prepared rayon cellulose/CMC hydrogel could take up more than 200% water within 60 minutes with an appropriate rayon cellulose-to-CMC ratio of 1:1 providing an ultimate balance between percent water uptake and the structural stability of the hydrogel. Its percent water uptake was as high as 285% to its initial dry weight.

A three-dimensional model of fine particle retention during percolation through a fiber mat

Fine particles are usually retained in fiber mats by sieving. To date, no theory has combined fiber and mat characteristics into a predictive retention model. A multilayer analytical retention model developed during this study predicts retention within a thick fiber mat by modeling retention as particles pass through a series of very thin fiber mats. A suspension of 5-75 μm toner particles was percolated through rayon fiber mats. The model’s prediction approached the experimental data only when the ratio of particle diameter to fiber diameter increased toward 2.0, the upper limit within the rayon fiber mat data set. Retention was also experimentally determined on the macroscale with simulated fiber mats, through which 4-20 mm beads were dropped. The particle diameter was at least 2.2 times the fiber diameter for all of the macroscale experimental data, explaining the much better fit of the data from those experiments to the model’s predictions.