The impact of serialisation on operational efficiency and productivity in pharmaceutical sites: A literature review

Abstract

Serialization technology was introduced to protect the pharmaceutical supply chain from falsified and substandard medicines infiltration. The implementation of serialization systems required a substantial investment by pharmaceutical manufacturers. This study investigated the impact of Serialisation on operational efficiency and productivity in the pharmaceutical industry. A literature review demonstrated limited publications on Serialisation concerning its costs and effects on packing line operational efficiency and productivity. Therefore, a literature review was carried out to assess the relationship between Serialisation, operational efficiency, and productivity. The study revealed that Serialisation had the potential to impact pack line Operational Equipment Effectiveness negatively and line availability, as well as the unit cost of packaged pharmaceuticals and that actual capital costs of Serialisation were greater than the costs originally outlined by policymakers. In addition, the study identified a trend where pharmaceutical sites move away from smaller batch production and toward larger batches to gain greater efficiencies. This is the first study of Serialisation literature from a manufacturing viewpoint.

Keywords:

• pharmaceutical cost of goods sold
• pharmaceutical COGS
• pharmaceutical OEE
• serialisation OEE

1. Introduction

Since the 1980s, the World Health Organization (WHO) has identified a growing threat to patient safety from falsified and substandard medicines. These fake medicines had started to gain a foothold in the legitimate supply chain (World Health Organisation, 2023). As a result, there were multiple incidents where unsuspecting patients were given unsafe medicines resulting in injury and death. By the late 1990s, the reported incidents of falsified medicines started to rise dramatically, and Regulatory authorities started to take action to protect patients (Buckley et al., 2013). The pharmaceutical industry also realized the danger posed by criminals operating in their Industry (International Federation of Pharmaceutical Manufacturers and Associations, 2023). The risk to patient safety, reputational damage and revenue loss focused the Pharmaceutical Industry’s attention on counterfeit medicines (OECD/EUIPO, 2020).

Implementing anti-counterfeiting regulations has required a large pharmaceutical industry investment in new equipment and resources (Chatterjee, 2014). The regulations introduced to protect pharmaceutical supply chains use serialization technology to print a unique identifier on each medicine pack. Every carton, bottle or medicine vial produced for the US and European markets must carry a serialized code unique to that pack. The serial code, expiry date and batch number are contained in a 2D matrix code mandated in regulations, with serial codes decommissioned at the point of dispensing by a pharmacist (European Medicines Verification System, 2023). Printing and checking a 2D Matrix code is simple, but flawlessly printing 200 codes per minute on a 24/7 cycle shift in a pharmaceutical manufacturing facility requires great skill and resources (O’Mahony, 2020). In addition, unit-level Serialization creates a large amount of data that must be stored, retrieved, and communicated across multiple systems. For example, a large pharmaceutical company will produce 650 GB of serialization data annually (Willis, 2017). Any mismanagement of this data can lead to production line stoppages, product recalls and a halt to the supply of essential medicines to patients (Sarkar, 2022).

This research aims to assess the impact of Serialisation on the efficiency and productivity of pharmaceutical sites from the literature viewpoint. The next objective was to quantify the impact of Serialisation on pharmaceutical facilities’ operational efficiencies using measurements such as Operational Equipment Effectiveness (OEE) and production line availability measures. Serialization inherently requires adding new process steps into existing operations which might infer a reduction in OEE (Arslan, 2019; Sarkar, 2022). Conversely, adding new equipment might increase OEE levels as when replacing existing equipment, the OEE can increase due to more modern technology and higher equipment speeds resulting in less downtime or minimizing product quality issues (Penfold, 2018). The research looked at the literature to gain insight into the experiences of manufacturers in the post-serialization era.

The last objective was to determine if serialization processes impacted site productivity. Serialization required a substantial pharmaceutical industry investment in capital expenditure (O’Mahony, 2020; Sarkar, 2022). New expertise and resources were required to manage and operate the serialization system and to store and distribute data (Cordon et al., 2016). Serialization track and trace systems can aid productivity by providing manufacturers with better data to manage supply chains, and productivity changes can be measured using changes to the cost of goods sold (COGS) and unit pricing (Arslan, 2019; Cordon et al., 2016). Serialization and the trend toward operational excellence techniques have coincided to create a greater impact on productivity (Jablonski & Brochu, 2006). There is a trend toward moving away from smaller batch sizes which may have coincided with the implementation of serialization processes, and these two changes in the production process have exacerbated each other (O’Mahony, 2020)

The purpose of the literature review was to:

1. Identify what consideration, if policymakers and industry bodies gave any, as to the impact of Serialisation on operational efficiency in the pre-serialization phase and the period after the implementation of serialization processes.

2. Identify from the literature the impact of Serialisation on operational efficiency and pharmaceutical site productivity.

Section 2 outlines the literature related to Serialisation and its origins, the requirement for Serialisation and the regulatory requirements around Serialisation. Section 3 outlines the methodology. Sections 4 and 5 present the results and discussion, while Section 6 outlines the conclusion.

2. Serialization and its origins

2.1. Counterfeit medicines

Estimations vary on the value of trade in counterfeit medicines. However, there is consensus that sub-standard and falsified medicines present an enormous risk to patient safety and the legitimate medicines supply chain (European Commission, 2008).

It can be difficult to assess the scale of the black market in counterfeit drugs. By its very nature, the trade in illicit medicines is controlled by criminals and can be dangerous to investigate. In addition, drug companies may be aware of copies of their medicines in some markets but may be slow to discuss these findings publicly (Cockburn et al., 2005).

Pharmaceutical supply chains are complex and stretch globally (Singh et al., 2016). For example, a pharmaceutical company may fill semi-finished products into primary packaging at their facilities but may have an outsourced contract packaging organization (CPO) manage the packing of drugs into labelled secondary packaging. Finished products may then go to an in-house distribution centre or may instead go to a licensed third-party logistics provider (3PL) or a licensed wholesaler/distributor. The complexity of the pharmaceutical supply chain makes it susceptible to infiltration in terms of sub-standard raw materials and fake finished products, according to a WHO report on counterfeit medicines (Pisani, 2017). Adopting serialization systems is just one of the tools used to combat illegal medicine supply (Hall, 2012).

2.2. Examples of falsified medicines in the supply chain

Heparin is a blood-thinning drug used to treat dialysis patients and seriously ill post-operative patients to prevent blood clotting. In 2008 fake versions of Heparin started to appear in the US market. The active pharmaceutical ingredient in Heparin was switched for a cheaper chemical compound with anti-coagulant properties (Hubbard, 2009). Infections caused by injections of fake Heparin caused the death of 81 patients in the US (Harris & Bogdanich, 2008). There were also reports of infections from the counterfeit Heparin in the EU, though these did not result in any deaths (European Medicines Agency, 2018)

In 2012 reports emerged of counterfeit Avastin circulating in the US market. Avastin (Bevacizumab) is a drug developed by Roche and Genentech as an oncology medicine for treating tumours and had sales of $6bn USD in 2012 at a product cost of about $2,500 per dose. When US regulators tested the fake Avastin, they found no active pharmaceutical ingredients (Mackey et al., 2015). There are multiple examples of counterfeit medicines and adverse side effects (WHO, 2003, 2013; IRACM and; IRACM & Przyswa, 2013).

In 2007, customs officials seized over 4 million articles of counterfeit medicines (European Commission, 2008). By 2011, this number had increased over 5-fold to 27.4 m medicine articles with a retail value of €27.6 m (European Commission, 2012). At the time of policy formation regarding falsified medicines and Serialisation, the threat of unlicensed drugs grew alarmingly. However, by 2019 the figures had dropped, with 166,000 articles seized with a retail value of just over €4 m (European Commission, 2019). In addition, EUROPOL’s MISMED program, a crackdown by law enforcement, customs and health regulatory authorities from 16 European countries, netted a haul of more than 13 million doses above €165 million (Europol, 2020).

The European Union allows the free movement of goods and intra-national trade within the community. Medicines are often legally relabelled for sale in different member-state markets. Parallel market relabelling operations may also take medicines outside the EU for remarketing in another EU Member State (Bird & Chaudhry, 2009). While this free movement of goods is accepted, the activity is seen as susceptible to criminal infiltration or abuse (Vander Beken & Balcaen, 2006). The European falsified medicines directive demands specific measures, including Serialisation, to control parallel trade. In the US, parallel trade is also a susceptible entry point for illegal medicines into the supply chain (Liang, 2006).

The European Union Intellectual Property Organisation (EUIPO) has worked with the Organization for Economic Cooperation and Development (OECD) to offer a deep analysis of the impact of counterfeit medicines in the European Union. In a 2020 report, the EUIPO and OECD used a figure of €4.4bn for the global trade in counterfeit medicines. The report outlines that 38% of seized counterfeit medicines infringe US patent and trademark rights. European trademark and patent holders are the next most affected group (EUIPO. and OECD, 2020).

Another 2019 EUIPO report estimates the indirect impact of counterfeit medicines on the European pharmaceutical Industry. The report calculates that unlicensed medicines cost 37,700 jobs in the EU. In addition, another 53,000 jobs are lost in supporting activities. The statement sizes the cost of counterfeit medicines at €10.2bn per annum when lost revenue is considered (EUIPO. and OECD, 2019).

The Pharmaceutical Security Institute (PSI) gathers incident data privately from pharmaceutical companies where their products have been counterfeited but may not wish to highlight these incidents publicly. The PSI reported 5,987 pharmaceutical crime incidents in 2021, increasing by thirty-eight per cent (+38%) from the previous year (Pharmaceutical Security Insitute, 2023). The increase in new incidents can be attributed to easing pandemic restrictions and criminal organizations taking advantage of new opportunities.

3. Methodology for literature review

A literature review supported the main aim of the research to determine the impact of Serialisation on operational efficiency and productivity on pharmaceutical sites. The literature review sought to identify relevant journal articles, industry reports and other sources that could inform the key objectives of the research. While many literature reviews excuse grey literature, this paper did not. Grey literature is defined as: “that which is produced on all levels of government, academics, business and industry in print and electronic formats, but which is not controlled by commercial publishers” (Bellefontaine & Lee, 2014; Mahood et al., 2014; Pappas & Williams, 2011). There were several reasons for the inclusion of grey literature. One reason is that grey literature can broaden the scope to more relevant studies, providing a complete view of available evidence (Mahood et al., 2014). Also, there can be a delay between research and publication, and thus access to innovative and up-to-date information and reports is more challenging (Pappas & Williams, 2011)

The literature review was conducted in two phases (Table 1) . The first phase of the literature review examined how policymakers and industry stakeholders considered the impact of serialization processes on operational efficiency and productivity. The themes of policymakers and industry bodies considering efficiency and productivity during the formulation of serialization regulations and its impact in the post-serialization period were analyzed. Also, it examined how operational efficiency is measured in the pharmaceutical industry. A search was conducted to find articles on how the pharmaceutical industry adopted serialization technology and if serialization systems had hindered or helped efficiency. This section also sought contributions about line availability.

Table 1. Literature review strategy

Literature Review Strategy
Phase	Topic	Sources
Phase I	Measurement of operational efficiency in the pharmaceutical industry and the impact of Serialisation	EC Reports, FDA Reports, Industry journal articles and industry magazines, journal papers (peer-reviewed)
Phase II	Measurement of productivity in the pharmaceutical industry and the impact of Serialisation on productivity in pharmaceutical packaging companies	EC Reports, FDA Reports, Industry journal articles and industry magazines, peer-reviewed journal papers

The final section of the literature review focused on productivity. The review examined how productivity is measured in the pharmaceutical industry. Contributions to the cost of goods sold and unit cost were examined. The literature review sought to examine articles on the relationship between serialization and unit cost.

Search tools used in the literature review included Sage Journals, EBSCO, Google Scholar, ResearchGate, PubMed, EOLAS, Emerald Insight and J-Stor. The literature review used a combination of Boolean Search functions, including the UK spelling of “serialization” and the US spelling of “serialization”. In addition, variations of “operational effectiveness”, “OEE”, “Operational Excellence”, “OPEX”, and “impact” were used in the Boolean searches.

The authors carried out a thematic analysis aligned with the research objectives under four main themes: the costs of Serialisation, serialization effects on manufacturing efficiency, serialization effects on planned downtime productivity and equipment efficiency and overall pharmaceutical industry productivity.

A flow chart to visualise the literature review is demonstrated in Figure 1.

Figure 1. The literature review process flow (Source: Authors own).

4. Results

The results of the literature were analysed in themes according to Figure 2.

Figure 2. Thematic analysis flow chart based on literature review and research questions (Source: Authors own).

4.1. The literature on serialisation in pharma

Much of the literature related to the topic of Serialisation in pharmaceuticals discusses the regulatory requirements for Serialisation (Nalam, 2023), the need for Serialization in counteracting drug counterfeiting (Lahjouji et al., 2023) and the track and traceability aspects of Serialisation (Trajanovska et al., 2023). However, apart from some reputable industry and consultants’ reports (Bellm, 2015; Gyurjyan et al., 2017; Mankins, 2017), few studies address the costs of Serialisation, the impact of Serialisation on manufacturing in terms of productivity, line efficiency and continuous flow. The studies that do address manufacturing serialization are concerned with the storage of the data involved for the serialization information (Nalam, 2023; Nguyen et al., 2023; Shanley, 2016) or the technology applied (Abdallah & Nizamuddin, 2023; Rajora, 2023; Trajanovska et al., 2023).

4.2. The expected costs of serialisation

The primary literature related to the expected costs of Serialisation within the European pharma industry was the European Federation of Pharmaceutical Industries and Associations (EFPIA) report in response to European Commission on the Falsified Medicine Directive. This report estimated the expected costs of implementing Serialisation in the pharmaceutical industry of €125 m annually for Serialisation (EFPIA, 2012). The EFPIA report also cites a cost of 1.6 cents per pack of medicine and an annual cost to a large manufacturer of €8 m annually. In its submission, the EFPIA stated that an average manufacturer would have €7bn in sales and produce 500 m packs of medicine per year (EFPIA, 2012).

The European Commission has published the correspondence from industry stakeholders, including submissions from Pfizer and Amgen which refer to the cost of Serialisation; however, none of the 100 submissions mentions an impact on the operational efficiency of manufacturing sites and, therefore, a potential impact on the availability of medicine (European Commission Enterprise + Industry, 2008).

In the final European Commission’s impact report on the falsified medicines directive, there is no reference to any possible impact on operational efficiencies. However, the report does estimate that once-off costs for serialization technology would come to €150,000 per pack line. This estimated that across the 12,000 non-prescription medicines pack lines, this would mean an industry investment of €1.8bn for line upgrades. In addition, another €4bn investment was required to provide the necessary IT systems to manage the flow of serialized data. The final report estimated that printing and packing serialized codes would cost 2 cents per pack in the first five years. With 14.85bn packs of prescription medicines traded annually in the EU, 2 cents per pack equates to an industry cost of €297 m per annum to print and check serialization codes on European pack lines (Commission of the European Communities, 2008a).

In the US, the FDA did not directly carry out a similar impact report; however, several indirect reports did assess the potential impacts of serialization processes on the industry. The Pew Healthcare Foundation published comprehensive research that estimated the costs of Serialisation (Charitable Trusts & Allen Hamilton, 2013). Based on estimates from both pharma companies and vendors, the Pew Healthcare report set the average cost to serialize a pack line at $1.4 m. This cost includes not just the cost of equipment and software but also the cost to implement the project and enterprise costs. This was a multiple of the European Commission’s estimates and highlights additional labour costs of $291,000 per annum per pack line (Foundation & Allen Hamilton, 2014). There is no reference to an impact on operational efficiency in the report. The report stated that no public analysis was available on the costs associated with implementing Serialisation at the time of publication.

The US Center for Disease Control (CDC) and Deloitte consultants did publish an impact assessment report on 2D data matrix code printing on the vaccine supply chain (Deloitte, 2012). The report considered the impact of Serialisation on manufacturers, distributors and healthcare providers (Robinson et al., 2013). The report cited the complexity of printing 2D matrix codes compared to traditional linear barcodes as regulations stipulated that manufacturers achieve a minimum ISO grade C for printed labels. In addition, each label must be checked to ensure its readability. Barcode scanners are too slow to read all the labels on a high-speed pack line, and therefore industrial grade cameras are used, adding more complexity and cost. Along with the complexity of the 2D codes, the FDA stipulated that manufacturers would still be expected to print linear barcodes on the packaging, thus increasing the risk of printing errors (Center for Drug Evaluation and Research, 2018). Again, the CDC impact report did not reference Serialisation’s potential impact on operational efficiency.

4.3. Serialization effects on manufacturing efficiency

In manufacturing environments, operational efficiency is often measured using the OEE method (Overall Equipment Effectiveness). The OEE concept was first introduced by Seiichi Nakajima in the seminal work Total Productive Maintenance (TPM), published in 1988 (Nakajima, 1988). Nakajima identified six factors that had the most impact on OEE. These are the big losses, Equipment failure/breakdown losses, Setup/adjustment time, Idling and minor stop losses, Reduced line speed, and Reduced yield until machines stabilize and quality.

The OEE calculation provides a common standard to determine production efficiency in different manufacturing sites and industrial sectors (de Ron & Rooda, 2006). OEE is made up of three elements (i) Performance, (ii) availability (iii) quality. Performance is a measurement of line speed. For example, a packaging machine rated to produce 200 packs per minute but that only produces 100 packs is operating at 50% performance. Availability is a measure of time. The percentage of stoppage time during which a pack line should be available for packing processes. Quality measures the percentage of good quality products produced from the total. The OEE is calculated as a composite of all three measurements.

OEE % = % Performance X % Availability X % Quality

Serialization can affect the OEE calculation’s three measures (Arslan, 2019; Cordon et al., 2016). The regulations promote using ISO standards that measure the quality of the 2D data matrix codes on the medicine pack (ISO, 2006). The requirement to print complex 2D matrix codes, apply tamper evidence seals and check the readability of print may slow the pack line speed performance. In addition, line availability may be affected by the time operators set up serialization data and clear down unused serialized codes and stoppages caused by poor-quality print (Fortier & Joevan, 2017; Ramesh, 2015).

While there was an absence of comments on pack line efficiency in the impact reports by the CDC and the European Commission, there was some industry realization of the potential of a negative impact on O.E.E. Rotunno et al. (2014a) commented that due to the changes in the process operations due to serialization activity, there could be an impact to the overall equipment effectiveness (OEE) of the pharma production lines. Waiting for continuous data exchanges and data valid signals may result in an overall reduction in line speeds and a reduction in performance efficiency” (Rotunno et al., 2014b). A report from Pharma Logistics IQ (2017) also cited efficiency-related costs, which were also unquantified.

In a report, by Rodgers (2015) in Healthcare Packaging estimated the negative OEE impact at 8% to 10% post-implementation. They estimated that OEE would recover to a point 4% lower than pre-serialization (Rodgers, 2014). This range of OEE loss was validated in an article in Pharmaceutical Commerce magazine, where a loss of between 5% and 10% was estimated for the period after ramp-up and stabilization, but losses of up to 30% were observed during the ramp-up period after implementation (Ozkaya et al., 2017). The article also pointed out that operators need training and experience to maximize efficiency post-serialization. The International Society of Pharmaceutical Engineers (ISPE) indicated 10% to 25% losses for up to 2 months post-serialization implementation. Penfold (2018) states that lines may recover 1% to 5% lower than the original OEE position after about six months (Penfold, 2018). However, these calculations are estimated based on a combination of the Healthcare Packaging study (Rodgers, 2014), personal experience and input from industry colleagues. Due to the emerging nature of the technology, there was not a large amount of supporting literature for the OEE impact claims in the ISPE study.

One of the advantages of the 2D Matrix Codes (DMC) used for Serialisation is that they are flexible from an operational perspective, as the DMC is readable from any orientation. In addition, the codes have built-in error correction that allows a printed code with up to 30% degradation in print quality to be effectively still read. Therefore, from an OEE quality factor perspective, the 2D data matrix codes help maximize OEE (GS1 AISBL, 2013).

4.4. Planned downtime productivity and equipment efficiency

The Harvard Business Review defines productivity as “the number of labour hours required to accomplish a given task compared with the standard in that industry or setting.” A productivity gain is when a manufacturing site produces more with the same resources than peer companies, i.e. doing more with the same resources. On the other hand, the same publication defines efficiency as “doing the same with less. Companies most often improve labour efficiency by finding ways to reduce the number of labour hours required to produce the same level of output” (Mankins, 2017). The serialization implementation process is not a single event, as software and hardware must evolve to meet regulatory and market requirements. As regulations evolve, so must the software and hardware on packaging lines (Fortier & Joevan, 2017; Ozkaya et al., 2017).

Each time a serialization system is updated to meet these regulatory requirements, the packaging line must be stopped. These stoppages affect the productivity of the manufacturing site. Updates to serialization equipment are classified as planned maintenance and do not affect the availability measures in OEE. Even though these stoppages are planned, the effect on productivity should be measured. Reductions in productivity will be reflected in the cost of goods (COGS) from the site and in the price patients pay for healthcare (Gyurjyan et al., 2017; McKinsey, 2022).

As serialization processes evolve with new regulatory demands, the time required to update and maintain systems is accounted for (Penfold, 2018). If pack lines become unavailable due to updates in pack line software and systems, this may not be captured in an OEE measurement. Planned maintenance may be used in pharmaceutical sites to mask some of the productivity impacts caused by the requirement to update serialization equipment (O’Mahony, 2020).

4.5. The OPEX wave in pharma

OEE, OEEM and E measurements are part of the operational excellence framework (OPEX) (Borges, 2023). The concepts associated with operational excellence grew from the methodologies adopted by Toyota and other Japanese manufacturers (Hayes & Wheelwright, 1984). The pharmaceutical Industry was a late starter in operational excellence (Chatterjee, 2014). This was evident in the high levels of raw materials and finished inventory carried by the pharmaceutical sector compared to other industries (Spector, 2018), (Starke & Kumor, 2013).

The branded pharmaceutical Industry enjoyed a high-margin environment until the introduction of the Hatch Watchman Act in 1984. This legislation paved the way for generic drug manufacturers to compete with branded drug companies once a medicine no longer had patent protection (Mossinghoff, 1999). The squeeze on margins by generic manufacturers gave pharmaceutical companies a “burning platform” to initiate improvements (Schonberger, 2007). However, by the start of the 21^st century, drug companies started to feel the pressure imposed on the industry by generic medicines. Pharmaceutical companies found that their margins quickly eroded once drugs came off patent. As a result, manufacturers needed to adopt lean manufacturing techniques to compete in markets not protected by patents (Bellm, 2015).

The imposition of manufacturing licenses by regulators was often cited as a reason for pharmaceutical manufacturers not trying to improve their processes. Processes were seen as frozen and not open to improvement (Friedli et al., 2013). In the mid-2000s, leading pharma companies started to adopt operational excellence programs. Examples include Genentech (Griffith et al., 2010), Abbott Pharmaceuticals (Starke & Kumor, 2013) and Pfizer (Werani et al., 2010). Following decades of focusing on quality control and stabilization programs for the control of manufacturing processes, pharma companies have moved to a new phase of trying to improve their organizations and processes systematically.

The pharmaceutical Industry’s adoption of operational excellence techniques since the 2000s led to substantial improvement in OEE and other key performances. A St. Gallen benchmarking report outlines the improvements in OEE by the pharmaceutical company participants between 2006 and 2012 (Bellm, 2015). The report cites a 53% gain in OEE performance. In 2007 the average OEE in a best in class of food processing operation was 24% ahead of the average OEE in a best-in-class pharmaceutical company. In a 2015 analysis of global “best in class” pharmaceutical sites, Ireland had 5% of the total (Bellm, 2015).

Part of the reason that pharmaceutical companies struggle with OEE compared to other industry sectors is due to batch changeover times. Regulations oblige companies to fully clear down packing lines between batches (European Commission, 2017). Information regarding the batch number, expiry date and Serialization information must also be set up on the pack lines before manufacture, and each step of the process must be checked and double-checked against standard operating procedures (SOPs). Best-in-class pharmaceutical companies achieved a four-fold reduction in changeover times compared to the poorest-performing sites (Manufacturing, 2007). However, just-in-time manufacturing and build-to-order batches mean an increase in the frequency of changeovers. More batch changeovers negatively impact line availability and OEE (Casali, 2019). The serialization setup process can contribute directly to these changeover times.

Some of the negative impacts on OEE, identified in the literature and previously discussed, were exacerbated by this drive toward lean manufacturing and operational excellence. For example, any delay in setting up serialization information for a batch during batch changeover impacts availability (Borges, 2023). In addition, the more batches that go through a pack line, the greater the risk of the serialization label print and check systems causing errors and affecting product quality.

Negative impacts on OEE must be balanced against some positive effects of serialization implementation. The age of the pack line may influence this balance. Pack line equipment generally has a lifecycle of 20–25 years (Commission of the European Communities, 2008b). With the onset of Serialisation, some manufacturers may have replaced older pack lines with newer equipment (Fortier & Joevan, 2017; GS1 Ireland, 2023). This capex investment in new equipment may also have brought better line speeds and faster changeover times. Adding better cameras and printers during serialization installation may improve line performance without replacing older pack lines. One vendor reported that a manufacturer saved $100,000 annually by replacing manual inspectors with an automated vision system during a serialization implementation (Pirrera & Jordan, 2014). Another vendor reports that a client started to monitor OEE post-serialization seriously, and by working closely with operators, the business was able to eliminate waste and increase OEE by 20% (Butschli, 2017).

The pharmaceutical pack line is the epitome of the late-stage customization demanded by lean manufacturing, as a medicine does not become a medicine until it is correctly labelled and serialized for a specific market. Therefore, any negative or positive impact on OEE or OEEM will affect a manufacturing site’s operational efficiency and productivity.

4.6. Pharmaceutical industry productivity

The St. Gallen studies outline the improvements in equipment efficiency in the pharmaceutical industry over 10 years (Bellm, 2015). However, during this period, no real improvement in pharmaceutical industry productivity has occurred. A key indicator of a manufacturing company’s progression in lean manufacturing is its inventory turns. Spector reported that compared to other manufacturing industries, the pharmaceutical sector made little impact on inventory levels from 2000 to 2009 (Spector, 2018). Analysis of public company data from 2007 to date indicates that inventory turn has essentially flatlined (Discover, 2020). McKinsey reports that the cost to produce medicines has not changed across the industry, the generic medicines sector being the only exception (Gyurjyan et al., 2017).

Basu et al. also identified this stagnation in the cost of goods as a percentage of total sales across pharmaceutical productivity from 2006 to 2008 (Basu et al., 2008). Vernon et al. (2007) identified that the cost of goods in medicines manufacture relates directly to the cost of healthcare. Any reduction in the cost of goods is taken as an additional margin by manufacturers, while any increase in the cost of goods is passed on through higher prices. If serialization processes did affect productivity, this might be reflected in the cost of goods and healthcare for patients. Serialization processes do not just impact the packaging areas in pharmaceutical companies. A study by GS1 Ireland and industry consultants Enterprise System Partners found that serialization project teams included representatives from departments such as packaging, automation, engineering, IT, quality, operations, manufacturing, artwork, and sales (GS1 Ireland, 2023). The Harvard Business Review (HBR) defines this cross-functional activity as “organizational drag”. The HBR reports that companies can lose up to 20% of their productive capacity through structures and processes that consume personnel’s time (Mankins, 2017). However, there is some light at the end of the tunnel regarding the serialisation costs. With the advent of blockchain, technology serialisation can be improved using blockchain technology, availability of cheaper QR-codes and the introduction of 2-stage product identification, which provides labels on the outside and inside the package to prevent the possibility of re-use of labels (Abdallah & Nizamuddin, 2023; Chiacchio et al., 2019).

5. Conclusion

Sufficient gaps in the literature were identified to warrant the design of a research method to investigate the impacts of Serialisation on operational efficiency and productivity in the pharma manufacturing context. The literature provided some base data on the considerations of policymakers and industry representatives as to the expected capital and operational cost of Serialisation. However, there was little follow-up in the literature regarding the accuracy of the original expectations outlined in policy maker’s impact assessment reports. No literature discussed a detailed impact of Serialisation on operational efficiency or productivity. The literature indicated that efficiency might increase or decrease because of Serialisation, but no clear outcomes were identified. Very little data was available in the literature that discussed Serialisation in the manufacturing context. The literature did not indicate how serialization processes might have affected pharmaceutical productivity or the cost of goods sold. A summary of the thematic areas of the literature regarding the challenges to manufacturers is presented in Table 2.

Table 2. Challenges from serialisation and the literature sources

Challenges from Serialisation	Literature Sources
Cost to implement	Jablonski and Brochu (2006) EFPIA (2012), Basu et al. (2008), Vernon et al. (2007)
Effect on line flow/rework	Abdallah and Nizamuddin (2023) Rajora (2023), Trajanovska et al. (2023).
Label quality issues	Deloitte (2012) Robinson et al. (2013)
Regulatory considerations (line changes/validation)	Jablonski and Brochu (2006), O’Mahony (2020)
Use of blockchain technology to reduce costs of Serialisation	Abdallah and Nizamuddin (2023) Chiacchio et al. (2019)
New Packaging line requirements	Jablonski and Brochu (2006), O’Mahony (2020)

6. Discussion & conclusion

This research aimed to determine the impact of Serialisation on operational efficiency and productivity on pharmaceutical sites. The research had two objectives, firstly, to test the assumptions made by industry bodies and policymakers before the implementation of Serialisation. Secondly, to assess the impact of Serialisation on operational efficiency and the impact of Serialisation on-site productivity.

As discussed in section 4.1, the literature is sparse about the productivity and efficiency impacts of Serialisation on Pharma and is not specific in terms of the costs of Serialisation. This study found that despite all types of studies related to Serialization aspects, for example, the reasons for Serializations, the benefits of serializations, the regulatory aspects of Serialisation and the data responsibilities that come with Serialisation that there is a dearth of studies related to the costs to manufacturers and other productivity effects on the supply chain.

The study has theoretical and managerial implications for the pharma industry. This is one of the first literature review studies on Serialisation effects on manufacturers in the pharma industry and can inform the industry of the downstream effects and issues around Serialisation. The reviewed research and literature indicated that Serialisation could potentially harm operational efficiency in pharmaceutical sites. There was some postulation in the literature before implementing the Drug Supply Chain Security Act (DSCSA) and the European Falsified Medicines Directive (FMD) that Serialisation could improve operational efficiency. There was an argument that new equipment and interconnected systems could improve operational effectiveness. O’Mahony (2020) found in a study of Irish Pharma manufacturers that costs had increased since serialization introduction and that Serialisation had an impact on productivity.

The research also looked back on the assessments of policymakers and industry bodies before the track and trace regulations. From the literature, it was clear that little consideration was given to the potential impact of Serialisation on efficiency and productivity. Estimations by policymakers on the cost associated with Serialisation were also inadequate. This research indicated that policymakers underestimated the cost of serialization projects by a factor of four, as the 2008 European Commission Assessment report had predicted an average cost of Serialisation per pack of 5 cents (EUROPEAN COMMISSION Enterprise + industry, 2008). Further research could be conducted to narrow down the annual cost of Serialisation in pharmaceutical sites as a follow-up to this study. Investigating how blockchain technology changes sterilisation costs and methods is also an opportunity for future research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Notes on contributors

Daniel O’ Mahony

Dan O'Mahony, MSc is a part time PHD student in the College of Science & Engineering in the University of Galway under the supervision of Dr. Olivia McDermott. Dan also lectures part time on the Irish National Institute for Bioprocessing Research and Training/Atlantic Technological University Sligo's joint program in Bio-Industry 4.0.

Olivia McDermott

Dr Olivia McDermott is an Associate Professor in Operational Excellence and Regulatory Affairs in the University of Galway, Ireland. She has co-authored over 90 peer reviewed research papers and conference papers. She has eight PHD students within her Operational Excellence Research group working on Medtech Regulations, Risk Management, Lean and Quality Management.

Alan Lynch

Dr. Alan Lynch is currently the Head of Professional Education at Grant Thornton Ireland and was Head of Faculty of the Graduate Business School at Griffith College, Ireland where he continues to lecture part time.

Kathryn Cormican

Dr. Kathryn Cormican is a Professor in Systems Engineering at the University of Galway. She leads research in the areas of in the area of Technology Innovation Management and Enterprise Systems in the School of Engineering. Her research group Enterprise Research Centre comprises nine PhD students, two research associates and two postdoctoral fellows.